On Efficient Data Transfers Across Geographically Dispersed Datacenters

Mohammad Noormohammadpour

REVIEW 3 major objections 5 minor 165 references

On Efficient Data Transfers Across Geographically Dispersed Datacenters

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Centralized traffic engineering can match global optimization in admitted traffic while cutting admission control time by three orders of magnitude.

desk verdict Solid heuristic work on inter-DC admission control and multicast; the BWR worst-case routing foundation has a conceded formal gap, but the main contributions survive. read the letter →

arxiv 1908.11131 v1 pith:S5OMYVP5 submitted 2019-08-29 cs.NI

Mohammad Noormohammadpour This is my paper · ORCID

classification cs.NI

keywords inter-datacenternetworkstrafficengineeringdeadline-awareadmissioncontrolas-late-as-possibleschedulingmulticastforwardingtreesSteinertreetransfercompletiontimesload-awarerouting

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This work establishes a practical case for centralized traffic engineering on dedicated inter-datacenter networks, where a single controller sees all transfer requests, link loads, and forwarding state. Its central claim is that routing and scheduling decisions should be driven by outstanding byte counts and deadlines rather than by instantaneous link utilization. The As-Late-As-Possible (ALAP) policy schedules each admitted transfer as close to its deadline as possible, which makes admission checks small and fast; combined with load-aware path selection, DCRoute processes requests about three orders of magnitude faster than linear-programming baselines while rejecting at most a few percent more traffic. For one-to-many transfers, selecting a single load-aware Steiner tree per transfer (DCCast) is claimed to reduce bandwidth use and tail completion times by up to half compared with sending independent point-to-point copies. The broader point is that centralized inter-DC traffic management can move from expensive global optimizations to fast heuristics without sacrificing much admitted traffic or completion-time performance.

What carries the argument

The load-aware cost metric is the load-bearing object. Each edge $e$ carries a cost $L_e + V$, where $L_e$ is the total volume of scheduled but not yet sent traffic on that edge and $V$ is the size of the incoming transfer; a path or tree is chosen by minimizing the sum of edge costs (via Dijkstra for single paths and GreedyFLAC for Steiner trees). Working with it is ALAP scheduling, which fills the timeline backward from each deadline, so that every admitted transfer occupies the latest possible timeslots and leaves earlier slots free. In Best Worst-case Routing the same idea appears in a different form: edge cost is the remaining data units of flows sharing the edge, and the selected path is the one minimizing the sum of competing data units. Together these turn a global mixed-integer problem into a shortest-path or Steiner-tree computation plus a small per-request allocation.

What would settle it

Build a small network with a source, a destination, and two candidate paths, where one path shares an edge with a large flow and the other shares edges with several smaller flows whose paths are mutually edge-disjoint. Compute Eq. 3.1 weights, then simulate the two choices under a work-conserving scheduler that adversarially delays the new flow; if the lower-weight path ever yields a larger actual worst-case completion time than the higher-weight path, the reduction in Proposition 1 is not exact.

Watch

Extended reading notes

Core claim

The core discovery is that two simple accounting rules capture most of the benefit of inter-datacenter optimization. First, the cost of routing a new transfer over a path can be written as the sum of remaining data units of all ongoing transfers that share an edge with that path, and minimizing this path weight (Best Worst-case Routing) approximately minimizes the worst-case completion time of the new transfer under any scheduling policy. Second, for deadline-constrained admission, allocating each transfer as late as possible leaves the maximum possible room for future arrivals, so a feasibility check only needs to inspect residual capacity on one candidate path rather than re-solving a global schedule. On the multicast side, the same load-plus-volume edge weights define a minimum-weight Steiner tree per transfer, and one tree per receiver group is enough to save bandwidth while keeping receivers synchronized. The dissertation reports that these heuristics match global LP-based admission within a few percent rejected traffic, speed up request processing by roughly three orders of magnitude, and cut multicast bandwidth and tail completion times by up to half as receiver count grows.

Load-bearing premise

The load-bearing premise is that the worst-case completion time of a new transfer equals the sum of remaining data units of all transfers sharing an edge with its candidate path, because edge-disjoint transfers are assumed unable to help each other; the dissertation itself notes this quantity may overshoot the true worst case.

Editorial extensions

If this is right

Operators can run deadline-aware admission control centrally at millisecond-scale per request instead of solving a global LP on every arrival, with rejected traffic within about 4 percentage points of the best single-path baseline.
Replicating an object from one datacenter to many can be done over one forwarding tree per transfer, roughly halving bandwidth consumption and tail completion time as the number of destinations grows.
Two edge-disjoint parallel trees capture most of the multicast speedup; adding a third tree gives little further benefit and can hurt by consuming extra bandwidth.
Partitioning receivers by their access speed and giving each partition its own tree can speed up the fastest receivers by tens of times while barely increasing total capacity use.
Application-specified mixed objectives for multicast can be encoded as receiver rankings and partition choices, meeting completion-time targets at about half the average receiver completion time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the Eq. 3.1 worst-case bound overshoots, the routing gains attributed to worst-case minimization may actually come from load balancing; comparing BWRHF against a true minimal-completion-time scheduler on layered topologies would separate the effects.
ALAP's no-rescheduling rule is a form of lazy scheduling; a testable extension is whether a bounded look-ahead admission policy that occasionally repacks the near future improves admission rates under bursty arrivals without losing the speed advantage.
The load-plus-volume edge weight is reminiscent of shortest-remaining-processing-time queueing; it could be tested against other online convex-cost routing metrics on real inter-DC traffic traces to see whether the reported advantage holds outside synthetic workloads.
The receiver-partitioning principle suggests a design rule: intentionally group slow receivers together so a single slow tree does not throttle fast ones, and use parallel trees only for the fast group; this could extend to coded multicast where overlapping partitions share some packets.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Three things you should know before reading this. It is a PhD dissertation, not a focused paper, and it shows: several distinct contributions, each with its own evaluation. The strongest material is Chapters 4 and 5 — ALAP admission control and DCCast's load-aware multicast trees. The headline numbers there, about 1000x faster admission processing with at most 4% more rejections than LP-based baselines, and up to 50% bandwidth/tail-completion savings versus point-to-point LP, are plausible and tested against solid baselines (Amoeba, K-shortest paths, global LP). The single-path ALAP optimality proof in Chapter 4 is a clean exchange argument and it holds.

What is genuinely new: the MINSUM(load+demand) cost metric, the ALAP scheduling policy, and load-aware Steiner tree selection for inter-DC multicast. These are simple, practical ideas that prior work (TEMPUS, Amoeba, k-shortest path LPs) does not contain. DCCast also has a public GitHub implementation, which is more than most dissertation chapters offer.

The soft spot is in Chapter 3. Proposition 1 claims that minimizing Eq. 3.1 gives the minimum worst-case completion time regardless of scheduling policy. But Eq. 3.1 sums the remaining data units of every flow sharing at least one edge with the candidate path, and the author concedes in Section 3.5.1 that this 'may overshoot' because edge-disjoint flows can transmit in parallel. So BWR formally optimizes an upper bound on the worst case, not the worst case itself. The simulations may still show real gains, but the stated formal claim is not supported. This is a real gap, but it is contained: DCRoute and DCCast do not depend on Proposition 1.

Two more minor issues. Chapters 6-8 were truncated in the version I saw, so the abstract's 'up to 35x' and 'up to 40%' claims are not verifiable from this text. And beyond DCCast's code, no simulation data or full artifacts are shipped, which limits independent checking. These are structural, not scandalous.

Who is this for: researchers working on centralized WAN traffic engineering, inter-DC bulk transfers, or multicast scheduling. They will get useful heuristics, thoughtful evaluations, and an honest admission of where the theory is a proxy. A serious editor should send this to peer review. The referee should ask that the BWR claim be reframed as optimizing an upper bound, and that the later chapters' data be made available or cited properly. But the work deserves referee time.

Referee Report

3 major / 5 minor

Summary. This dissertation develops centralized traffic-engineering algorithms for inter-datacenter networks. Chapter 3 introduces Best Worst-case Routing (BWR) and two heuristics, BWRH and BWRHF, which assign paths to long flows using the remaining data units of ongoing flows rather than instantaneous link utilization, claiming large reductions in mean and tail flow completion times. Chapter 4 addresses deadline-aware admission control: it proposes As-Late-As-Possible (ALAP) scheduling on a single path, then generalizes it to DCRoute, a load-aware single-path routing and admission scheme, and to MP-DCRoute with edge-disjoint multipath. Chapter 5 treats point-to-multipoint transfers, selecting load-aware Steiner trees in DCCast and adding deadline-aware admission control in DDCCast. The headline empirical claims are that DCRoute is about three orders of magnitude faster than PMC/SPMC while rejecting at most 4% more traffic, and that DCCast reduces bandwidth and tail completion times by up to 50% relative to P2P-SRPT-LP as the number of destinations grows.

Significance. If the empirical claims hold, the work makes a useful systems contribution: it shows that centralized deadline-aware admission control and multicast tree selection can run at practical speeds over inter-datacenter networks with only small admission-rate penalties. The strengths of the manuscript include a clean exchange argument for ALAP on a single path (Theorem 1, Section 4.2.3), evaluation against standard baselines (Amoeba, K-shortest paths, LP-based P2P schemes), use of multiple real WAN topologies, and a publicly available implementation of DCCast. The main formal weakness is in the BWR chapter: the reduction from worst-case completion time to the path-weight metric in Eq. (3.1) is not a correct characterization of worst-case completion time, as the manuscript itself concedes in Section 3.5.1. The DCRoute and DCCast simulation results are not directly invalidated by this issue, but the stated theoretical guarantee for BWR needs to be substantially reworded or replaced.

major comments (3)

[Section 3.4.2 and Section 3.5.1 (Eq. 3.1, Proposition 1)] Proposition 1 is not established by the given reduction. Eq. (3.1) sums the remaining data units of every ongoing flow that shares at least one edge with the candidate path, but this quantity is an upper bound on the worst-case completion time, not the worst-case completion time itself: flows on edge-disjoint subpaths can transmit in parallel, and a flow that shares multiple edges with the path does not necessarily delay the new flow once per shared edge. The manuscript itself states in Section 3.5.1 that the computed quantity 'may overshoot, that is, the worst-case may be larger than necessary.' Consequently, minimizing Eq. (3.1) minimizes an upper bound, and the claim that BWR minimizes the worst-case completion time 'regardless of the scheduling policy' is formally unsupported. I recommend restating Proposition 1 as a claim about minimizing a load-based upper-bound metric, or supplying a correct derivation of the true worst-case completion time, and adjusting the strong conclusions in Section 3.6 accordingly.
[Section 4.3.5 (Figures 4.6 and 4.7)] The headline claim that DCRoute rejects 'at most 4% more traffic' than all compared schemes is an empirical statement based on two synthetic settings: the GScale topology and random networks with 5 to 20 nodes. The figures report point values without confidence intervals or standard deviations, even though arrivals, demands, and deadlines are stochastic. Because the admission-rate gap is the load-bearing evidence for the 'almost identical performance' claim, the paper should report multiple independent runs, confidence intervals or box plots, and state explicitly that the 'at most 4%' bound is an observed maximum over these experiments rather than a general guarantee.
[Section 5.2.5 (Figure 5.6)] The DCCast claim of 'up to 50% less' bandwidth and tail completion time relative to P2P-SRPT-LP is based on a single simulation configuration per data point, with no reported variance. Given that the comparison involves stochastic request arrivals and exponential transfer sizes, the plotted differences should be accompanied by error bars or a statistical assessment so that the reader can judge whether the 50% improvement is stable across random seeds and traffic instantiations.

minor comments (5)

[Section 3.5.1, Running Time paragraph] The text says 'The maximum running time of Algorithm 1 was 17.88 milliseconds' in the BWRHF subsection; this should reference Algorithm 2, since the paragraph is describing BWRHF.
[Figures 4.6 and 4.7] The axis labels render the arrival-rate symbol as 'Arrival Rate (6)' and '6 = 6.0' in the provided text; the LaTeX/PDF rendering of the lambda symbol should be fixed.
[Section 3.4.5, BWRH optimality gap] The reported average optimality gap of less than 0.25% is computed only for small topologies and for scenarios with fewer than 20 ongoing flows (due to the branch-and-bound implementation); this scope limitation should be stated in the main text rather than only implied by the evaluation description.
[Section 5.2.5, Evaluation setup] The DCCast simulations are described as running 'as many timeslots as needed' with the last arrival at 500; the stopping rule and the number of independent replications should be stated explicitly so that the normalized means and tails are reproducible.
[Table 2.2] Several entries contain LaTeX artifacts such as 'Set⟨⟩' and 'EEEΨ⊂ EEG'; these should be cleaned up in the final typeset version.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the Eq. 3.1 overshoot admitted in Section 3.5.1 is a formal approximation gap, not a circular reduction.

full rationale

No significant circularity. The central contributions are evaluated against external baselines (MinHop, Min-Max Utilization, K-Shortest Paths, PMC/SPMC, Amoeba, and P2P-SRPT-LP) using standard synthetic workloads, and the proposed metrics and heuristics are defined independently of the reported outcomes. DCRoute's speed and admission-rate claims in Sections 4.3.4–4.3.5 and DCCast's bandwidth and completion-time improvements in Section 5.2.5 are not fitted parameters renamed as predictions; no parameter is calibrated to force a target result. The self-citations to the author's earlier papers describe the same algorithms and are not used as load-bearing external authority or as uniqueness theorems. The one formal weakness in the derivation chain is Proposition 1 (Section 3.4.2), where Problem 1 is reduced to Problem 2 by equating worst-case completion time with the sum of remaining data units on shared edges (Eq. 3.1). The dissertation itself concedes in Section 3.5.1 that this quantity 'may overshoot, that is, the worst-case may be larger than necessary' because edge-disjoint flows can transmit in parallel. That is an acknowledged approximation gap in the reduction, and it weakens the formal optimality claim of BWR, but it is not circular: Eq. 3.1 is an asserted upper bound, not a restatement of the definition of worst-case completion time, and the empirical FCT comparisons in Figures 3.4–3.12 stand independently of the proposition's formal validity. Therefore the circularity score is 0.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The dissertation's algorithms rest on a centralized, slotted-time model of inter-datacenter WANs, on synthetic workload models, and on one load-bearing reduction (Eq. 3.1) that the author acknowledges can overshoot. No new physical entities are introduced; the load metric is a cost definition, not an entity, and the forwarding trees and partitions are algorithmic constructs built from the existing graph model.

free parameters (5)

Epsilon in BWRHF edge weight (Algorithm 2) = 0 < epsilon << 1 (unspecified)
Hand-chosen tie-break coefficient added to every edge weight so Dijkstra prefers shortest hop paths among equal-load paths; the value is not derived and results are asserted to be insensitive to it within the stated range.
K, number of parallel edge-disjoint paths in MP-DCRoute = up to 2 to 3 paths in evaluation
Section 4.4.1: K is an operator-set configuration parameter. Evaluation shows 2 paths capture most benefit and 3 paths can hurt admission, so the choice is heuristic and load-dependent.
T, batching window for the BATCHING baseline in DCCast evaluation = 10, 50, 100 timeslots
Section 5.2.5: evaluation parameter for one baseline scheme only; not part of the main DCCast algorithm.
alpha, bandwidth priority weight in the Chapter 2 objective example = 0 < alpha << 1
Section 2.3: illustrative operator-chosen coefficient in Min(sum tau + alpha times sum Omega); not used by the later heuristic algorithms.
Load-aware cost assignment (Le + VRnew) for paths and Steiner trees = sum of scheduled residual load plus new transfer volume
Hand-designed heuristic cost used in Chapters 3-5 for Dijkstra path selection and Steiner tree selection. It is validated only by the author's own simulations against utilization-based baselines, not derived from an optimality argument.

assumptions (6)

domain assumption A logically centralized Traffic Engineering Server (TES) has complete visibility of transfer demands, link state, and forwarding state, and can enforce per-timeslot rates at all senders and install forwarding rules network-wide.
Chapter 2, Section 2.1 and Figure 2.1. Every algorithm in Chapters 3-5 runs inside this architecture, modeled on Google B4, Microsoft SWAN, and Facebook Express Backbone. Deployment requires SDN control planes the dissertation does not build.
domain assumption Zero propagation and queuing latency for long flows; one data unit traverses an edge per timeslot.
Section 3.4.1. Underlies the BWR worst-case timing model and the ALAP timeslot model. The author justifies it by focusing on long flows where transmission dominates, but real WAN RTTs and TCP dynamics are omitted.
ad hoc to paper The worst-case completion time of a new flow equals the sum of remaining data units of all flows that share at least one edge with the candidate path (Eq. 3.1).
Proposition 1, Section 3.4.2. The author concedes in Section 3.5.1 that the sum 'may overshoot' because edge-disjoint flows can transmit in parallel, so the reduction is to an upper bound on the stated objective.
domain assumption Transfer arrivals are online with no knowledge of the future; evaluation uses Poisson arrivals with exponential/Pareto sizes and Facebook-style patterns.
Chapters 3-5 evaluation sections and Section 3.1.1. Central to the online framework and to the claimed gains; results are not demonstrated under other arrival or size distributions.
standard math GreedyFLAC returns near-optimal minimum-weight Steiner trees.
Chapter 5, Algorithm 4 and Section 5.2.4. The approximation quality of this cited heuristic [72,143] is not re-verified on the evaluated topologies, and the optimality gap of the DCCast tree selection is not reported.
standard math Problem 2 (minimum weight path with non-edge-decomposable weights) is NP-hard.
Section 3.4.3 refers to Appendix A for the proof. The appendix exists in the dissertation but its content was not fully visible in the reviewed portion.

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Cite this review

Pith. "Pith review of On Efficient Data Transfers Across Geographically Dispersed Datacenters." pith.science (2026). https://pith.science/paper/S5OMYVP5

@misc{pith2026190811131,
  author       = {Pith},
  title        = {Pith review of: On Efficient Data Transfers Across Geographically Dispersed Datacenters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S5OMYVP5}},
  note         = {Machine review of arXiv:1908.11131}
}

read the original abstract

As applications become more distributed to improve user experience and offer higher availability, businesses rely on geographically dispersed datacenters that host such applications more than ever. Dedicated inter-datacenter networks have been built that provide high visibility into the network status and flexible control over traffic forwarding to offer quality communication across the instances of applications hosted on many datacenters. These networks are relatively small, with tens to hundreds of nodes and are managed by the same organization that operates the datacenters which make centralized traffic engineering feasible. Using coordinated data transmission from the services and routing over the inter-datacenter network, one can optimize the network performance according to a variety of utility functions that take into account data transfer deadlines, network capacity consumption, and transfer completion times. In this dissertation, we study techniques and algorithms for fast and efficient data transfers across geographically dispersed datacenters over the inter-datacenter networks. We discuss different forms and properties of inter-datacenter transfers and present a generalized optimization framework to maximize an operator selected utility function. Next, in the several chapters that follow, we study, in detail, the problems of admission control for transfers with deadlines and inter-datacenter multicast transfers. For the admission control problem, our solutions offer significant speed up in the admission control process while offering almost identical performance in the total traffic admitted into the network. For the bulk multicasting problem, our techniques enable significant performance gain in receiver completion times with low computational complexity, which makes them highly applicable to inter-datacenter networks.

Figures

Figures reproduced from arXiv: 1908.11131 by the authors.

**Figure 1.1.** A typical datacenter cluster communicate at high speed with minimum latency. All the machines in a rack are connected to a Top of Rack (ToR) switch which provides non-blocking connectivity among them. Rack size is typically limited by maximum number of ports that ToR switches provide and the ratio of downlink to uplink bandwidth. There is usually about tens of machines per rack [17–19]. ToR switches are then connect… view at source ↗

**Figure 1.2.** Netflix cache locations as of 2016. Green dots are ISP locations and orange [PITH_FULL_IMAGE:figures/full_fig_p018_1_2.png] view at source ↗

**Figure 1.3.** Google, a major cloud services provider, with 19 functional regions and 4 [PITH_FULL_IMAGE:figures/full_fig_p019_1_3.png] view at source ↗

Figures from the paper (84 more)

**Figure 1.4.** Figure 1.4: Google’s inter-DC network also known as B4. [PITH_FULL_IMAGE:figures/full_fig_p020_1_4.png]

**Figure 1.5.** Figure 1.5: Microsoft Azure’s inter-DC network.5 networks. The resources of these networks may be used by the services that run on the datacenters that they connect. Datacenter operators own the capacity of the inter-DC network and can manage it as needed to maximize the perform…

**Figure 1.** Figure 1: shows the growth of inter-DC bandwidth across Facebook’s datacenters. As can [PITH_FULL_IMAGE:figures/full_fig_p021_1.png]

**Figure 1.6.** Figure 1.6: Traffic growth across Facebook’s Express Backbone. [PITH_FULL_IMAGE:figures/full_fig_p022_1_6.png]

**Figure 2.1.** Figure 2.1: Central traffic management architecture. [PITH_FULL_IMAGE:figures/full_fig_p028_2_1.png]

**Figure 2.2.** Figure 2.2: Steps in processing of a new inter-DC transfer. [PITH_FULL_IMAGE:figures/full_fig_p029_2_2.png]

**Figure 2.3.** Figure 2.3: Rate-allocation per link per timeslot. at a different network entity (hypervisor, border gateway, etc.). This function could also be implemented at the datacenter network edge based on end-point addresses and using real-time packet header modification predicates. In …

**Figure 2.** Figure 2: shows how rate-limiting can be applied at the servers [PITH_FULL_IMAGE:figures/full_fig_p030_2.png]

**Figure 2.4.** Figure 2.4: Several end-point rate-limiting techniques. [PITH_FULL_IMAGE:figures/full_fig_p031_2_4.png]

**Figure 2.5.** Figure 2.5: Some penalty functions X t≤tdRi X Ψ∈ψi d ωfi Ψ(t) = VRi , ∀d ∈ DRi (2.9) The optimization problem with this constraint may become infeasible. That means the current parameters make it impossible to meet the given deadline. This process is referred to as admission con…

**Figure 1.** Figure 1: Performance of various cost metrics for path selection over Cogent WAN [ [PITH_FULL_IMAGE:figures/full_fig_p041_1.png]

**Figure 3.2.** Figure 3.2: Example of routing a new flow F4 2 it will be 9 and therefore, the logically centralized network controller will select path 1 for F4. The worst-case completion times are not affected by the scheduling policy and are independent of it. Also, the fact that F2 has thre…

**Figure 3.3.** Figure 3.3: BWRH’s optimality gap for λ = 10 and µ = 50 computed for 1000 flow arrivals. 34 [PITH_FULL_IMAGE:figures/full_fig_p048_3_3.png]

**Figure 3.4.** Figure 3.4: Online routing techniques by flow scheduling policy assuming [PITH_FULL_IMAGE:figures/full_fig_p049_3_4.png]

**Figure 3.5.** Figure 3.5: Worst-case routing scenario The worst-case optimality gap is highly dependant on the remaining flow data units and can potentially be large. However, the worst-case scenario is highly unique. We will show, through experiments, that Algorithm 2 offers close to optimal…

**Figure 3.6.** Figure 3.6: Comparison of mean and tail flow completion times for the three implemen [PITH_FULL_IMAGE:figures/full_fig_p054_3_6.png]

**Figure 3.7.** Figure 3.7: Comparison of mean and tail flow completion times for the three implementa [PITH_FULL_IMAGE:figures/full_fig_p055_3_7.png]

**Figure 3.8.** Figure 3.8: Comparison of mean and tail flow completion times for the three implemen [PITH_FULL_IMAGE:figures/full_fig_p056_3_8.png]

**Figure 3.9.** Figure 3.9: Comparison of mean and tail flow completion times for the three implementa [PITH_FULL_IMAGE:figures/full_fig_p057_3_9.png]

**Figure 3.10.** Figure 3.10: Online routing techniques by flow scheduling policy assuming [PITH_FULL_IMAGE:figures/full_fig_p058_3_10.png]

**Figure 3.11.** Figure 3.11: Online routing techniques by flow scheduling policy assuming [PITH_FULL_IMAGE:figures/full_fig_p059_3_11.png]

**Figure 3.12.** Figure 3.12: Online routing techniques by flow scheduling policy assuming [PITH_FULL_IMAGE:figures/full_fig_p060_3_12.png]

**Figure 3.13.** Figure 3.13: BWRHF’s optimality gap for λ = 10 and µ = 50 computed for 1000 flow arrivals. 3.6 Conclusions In this chapter, we explored a variety of routing heuristics and showed that the current routing techniques are insufficient for reducing the completion times of inter-DC t…

**Figure 4.** Figure 4: provides an example of the ALAP allocation technique. As can be seen, [PITH_FULL_IMAGE:figures/full_fig_p065_4.png]

**Figure 4.1.** Figure 4.1: A traffic allocation used in proof of Theorem 1. [PITH_FULL_IMAGE:figures/full_fig_p066_4_1.png]

**Figure 4.2.** Figure 4.2: An example of ALAP allocation. when the first transfer is received, the timeline is empty and therefore it is allocated adjacent to its deadline. The second transfer is allocated as close as possible to its deadline. The implication of this type of scheduling is tha…

**Figure 4.3.** Figure 4.3: Comparison between Amoeba and ALAP scheduling. [PITH_FULL_IMAGE:figures/full_fig_p067_4_3.png]

**Figure 4.** Figure 4: shows the aforementioned simulation metrics for both Amoeba and ALAP. [PITH_FULL_IMAGE:figures/full_fig_p067_4.png]

**Figure 4.4.** Figure 4.4: An example of improving utilization (i.e., PullBack phase) while keeping the [PITH_FULL_IMAGE:figures/full_fig_p071_4_4.png]

**Figure 4.** Figure 4: shows an example of this process. There are three different requests all of [PITH_FULL_IMAGE:figures/full_fig_p071_4.png]

**Figure 4.5.** Figure 4.5: An example of assigning paths to transfers and their total network capacity use [PITH_FULL_IMAGE:figures/full_fig_p073_4_5.png]

**Figure 4.** Figure 4: shows the rejection rate of different techniques for different arrival rates from low [PITH_FULL_IMAGE:figures/full_fig_p076_4.png]

**Figure 4.6.** Figure 4.6: Total % of rejected traffic and relative request processing time for GScale [PITH_FULL_IMAGE:figures/full_fig_p077_4_6.png]

**Figure 4.7.** Figure 4.7: Total % of rejected traffic and relative request processing time for networks [PITH_FULL_IMAGE:figures/full_fig_p077_4_7.png]

**Figure 4.8.** Figure 4.8: An example of multipath ALAP scheduling: traffic is allocated on edge-disjoint [PITH_FULL_IMAGE:figures/full_fig_p079_4_8.png]

**Figure 4.9.** Figure 4.9: Multipath ALAP scheduling over GScale [2] topology. [PITH_FULL_IMAGE:figures/full_fig_p081_4_9.png]

**Figure 4.10.** Figure 4.10: Multipath ALAP scheduling over ANS [4] topology. [PITH_FULL_IMAGE:figures/full_fig_p081_4_10.png]

**Figure 4.11.** Figure 4.11: Multipath ALAP scheduling over Cogent [1] topology. [PITH_FULL_IMAGE:figures/full_fig_p081_4_11.png]

**Figure 5.1.** Figure 5.1: Applications that generate transfers potentially with multiple destinations. [PITH_FULL_IMAGE:figures/full_fig_p085_5_1.png]

**Figure 5.2.** Figure 5.2: Inter-DC multicasting can reduce total bandwidth consumption as well as com [PITH_FULL_IMAGE:figures/full_fig_p086_5_2.png]

**Figure 5.3.** Figure 5.3: Tree Selection (GScale Topo) # Copies 1 2 3 4 5 6 Mean TCT 0 5 10 15 20 25 DCCast RANDOM MINMAX # Copies 1 2 3 4 5 6 Tail TCT 0 2 4 6 8 10 12 14 [PITH_FULL_IMAGE:figures/full_fig_p092_5_3.png]

**Figure 5.4.** Figure 5.4: Tree Selection (Random topology, |VG|= 50) considered schemes. In this table, the first 4 approaches are P2MP schemes and last 2 are P2P schemes that operate by breaking each P2MP transfer into multiple P2P transfers. We evaluated various forwarding tree selection cr…

**Figure 5.5.** Figure 5.5: The SRPT discipline performs considerably better with respect to mean com [PITH_FULL_IMAGE:figures/full_fig_p092_5_5.png]

**Figure 5.6.** Figure 5.6: DCCast vs Point-To-Point (P2P-SRPT-LP). 50 100 150 200 250 Nodes (edges = Nodes # 6) 0.5 1 1.5 2 2.5 Accumulative BW Used Copies = 5, Avg Req Size = 2 Full Slots Lambda = 1, Req Size Dist = Exponential 50 100 150 200 250 Nodes (edges = Nodes # 6) 0.5 1 1.5 2 2.5 Mean…

**Figure 5.7.** Figure 5.7: Performance of 3-Shortest Paths (P2P) vs DCCast as network grows. [PITH_FULL_IMAGE:figures/full_fig_p094_5_7.png]

**Figure 5.8.** Figure 5.8: Performance of 3-Shortest Paths (P2P) vs DCCast as incoming network load [PITH_FULL_IMAGE:figures/full_fig_p095_5_8.png]

**Figure 5.9.** Figure 5.9: Computational overhead of DCCast as network size grows. [PITH_FULL_IMAGE:figures/full_fig_p096_5_9.png]

**Figure 5.10.** Figure 5.10: DDCCast (Deadline-Aware DCCast 5.2) architecture. [PITH_FULL_IMAGE:figures/full_fig_p098_5_10.png]

**Figure 5.11.** Figure 5.11: Capacity consumption and total admitted traffic by [PITH_FULL_IMAGE:figures/full_fig_p101_5_11.png]

**Figure 5.** Figure 5: shows the results of this experiment. We increased the number of destinations [PITH_FULL_IMAGE:figures/full_fig_p101_5.png]

**Figure 5.12.** Figure 5.12: Capacity consumption and total admitted traffic by [PITH_FULL_IMAGE:figures/full_fig_p102_5_12.png]

**Figure 6.** Figure 6: shows an example of delivering a large object [PITH_FULL_IMAGE:figures/full_fig_p105_6.png]

**Figure 6.1.** Figure 6.1: Using multiple smaller multicast trees we can improve the completion times of [PITH_FULL_IMAGE:figures/full_fig_p106_6_1.png]

**Figure 6.** Figure 6: shows our simulation results of receiver completion times for bulk multicast [PITH_FULL_IMAGE:figures/full_fig_p115_6.png]

**Figure 6.2.** Figure 6.2: Evaluation of various weights for tree selection ( [PITH_FULL_IMAGE:figures/full_fig_p116_6_2.png]

**Figure 6.** Figure 6: shows how QuickCast can speed up multiple receivers per transfer by separating [PITH_FULL_IMAGE:figures/full_fig_p118_6.png]

**Figure 6.3.** Figure 6.3: Various schemes for bulk multicast transfers. All schemes use max-min fair [PITH_FULL_IMAGE:figures/full_fig_p119_6_3.png]

**Figure 6.4.** Figure 6.4: Mean receiver completion time speedup (larger is better) of receivers compared [PITH_FULL_IMAGE:figures/full_fig_p120_6_4.png]

**Figure 6.5.** Figure 6.5: Performance of QuickCast as a function of partitioning factor [PITH_FULL_IMAGE:figures/full_fig_p121_6_5.png]

**Figure 6.6.** Figure 6.6: Average throughput of bulk multicast transfers obtained by running different [PITH_FULL_IMAGE:figures/full_fig_p122_6_6.png]

**Figure 7.1.** Figure 7.1: A relaxed topology with infinite core capacity, and uplink and downlink capac [PITH_FULL_IMAGE:figures/full_fig_p131_7_1.png]

**Figure 7.** Figure 7: where our solution selects [PITH_FULL_IMAGE:figures/full_fig_p132_7.png]

**Figure 7.2.** Figure 7.2: Various partitioning solutions for a scenario with four receivers. Numbers show [PITH_FULL_IMAGE:figures/full_fig_p133_7_2.png]

**Figure 7.3.** Figure 7.3: A worst-case scenario for the proposed partitioning scenario. Numbers within [PITH_FULL_IMAGE:figures/full_fig_p134_7_3.png]

**Figure 7.4.** Figure 7.4: Example of a partitioning hierarchy for a transfer with 10 receivers (the topology [PITH_FULL_IMAGE:figures/full_fig_p134_7_4.png]

**Figure 7.5.** Figure 7.5: Pipeline of [PITH_FULL_IMAGE:figures/full_fig_p136_7_5.png]

**Figure 7.6.** Figure 7.6: The physical topology, and the aggregate topology to compute a lower bound [PITH_FULL_IMAGE:figures/full_fig_p143_7_6.png]

**Figure 7.7.** Figure 7.7: Comparison of various techniques by number of multicast receivers. Plots are [PITH_FULL_IMAGE:figures/full_fig_p145_7_7.png]

**Figure 7.8.** Figure 7.8: Mean completion time speedup (larger is better) of receivers normalized by no [PITH_FULL_IMAGE:figures/full_fig_p146_7_8.png]

**Figure 7.9.** Figure 7.9: CDF of receiver completion times. Every transfer has 8 receivers selected [PITH_FULL_IMAGE:figures/full_fig_p147_7_9.png]

**Figure 7.10.** Figure 7.10: Gain by rank for different receivers per transfer averaged over all transfers for [PITH_FULL_IMAGE:figures/full_fig_p149_7_10.png]

**Figure 7.11.** Figure 7.11: Mininet Emulation Results 137 [PITH_FULL_IMAGE:figures/full_fig_p151_7_11.png]

**Figure 8.1.** Figure 8.1: Using parallel forwarding trees we can increase the overall network throughput [PITH_FULL_IMAGE:figures/full_fig_p154_8_1.png]

**Figure 8.** Figure 8: shows the effect of maximum number of trees per transfer (i.e., [PITH_FULL_IMAGE:figures/full_fig_p157_8.png]

**Figure 8.2.** Figure 8.2: The effect of number of parallel trees on total bandwidth consumption and [PITH_FULL_IMAGE:figures/full_fig_p158_8_2.png]

**Figure 8.3.** Figure 8.3: The effect of number of receivers on total bandwidth consumption ratio (i.e., [PITH_FULL_IMAGE:figures/full_fig_p158_8_3.png]

**Figure 8.** Figure 8: shows the effect of different transfer size distributions which include both syn [PITH_FULL_IMAGE:figures/full_fig_p158_8.png]

**Figure 8.** Figure 8: shows the effect of scheduling policies on the flow completion times gain and the [PITH_FULL_IMAGE:figures/full_fig_p159_8.png]

**Figure 8.4.** Figure 8.4: The effect of transfer size distribution on total bandwidth consumption ratio [PITH_FULL_IMAGE:figures/full_fig_p160_8_4.png]

**Figure 8.5.** Figure 8.5: The effect of topology on total bandwidth consumption ratio (i.e., [PITH_FULL_IMAGE:figures/full_fig_p160_8_5.png]

**Figure 8.6.** Figure 8.6: The effect of traffic scheduling policy on total bandwidth consumption ratio [PITH_FULL_IMAGE:figures/full_fig_p160_8_6.png]

**Figure 8.7.** Figure 8.7: The effect of transfer arrival rate (i.e., network load) on total bandwidth con [PITH_FULL_IMAGE:figures/full_fig_p161_8_7.png]

**Figure 9.1.** Figure 9.1: Example scenario used in §9.1.1 considerably improve completion times while minimally increase bandwidth consumption. We also found that selecting more than two parallel trees does not offer any benefits in most cases but increases bandwidth consumption. 9.1 Future D…

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Works this paper leans on

165 extracted references · 78 canonical work pages

[1]

http://www.topology-zoo.org/files/Cogentco.gml

The internet topology zoo (cogent). http://www.topology-zoo.org/files/Cogentco.gml. visited on July 19, 2017

2017
[2]

B4: Experience with a globally-deployed software deﬁned wan

Sushant Jain, Alok Kumar, et al. B4: Experience with a globally-deployed software deﬁned wan. SIGCOMM, 43(4):3–14, 2013

2013
[3]

http://www.topology-zoo.org/files/Agis.gml
[4]

http://www.topology-zoo.org/files/Ans.gml
[5]

http://www.topology-zoo.org/files/AttMpls.gml

The internet topology zoo (att north america). http://www.topology-zoo.org/files/AttMpls.gml. visited on July 19, 2017

2017
[6]

http://www.topology-zoo.org/files/Geant2009.gml

The Internet Topology Zoo (GEANT). http://www.topology-zoo.org/files/Geant2009.gml
[7]

W. Xia, P. Zhao, Y. Wen, and H. Xie. A survey on data center networking (dcn): Infrastructure and operations. IEEE Communications Surveys Tutorials , 19(1):640–656, Firstquarter 2017

2017
[8]

Zhang, F

J. Zhang, F. R. Yu, S. Wang, T. Huang, Z. Liu, and Y. Liu. Load balancing in data center networks: A survey. IEEE Communications Surveys Tutorials , 20(3):2324–2352, thirdquarter 2018

2018

Show all 165 references

[9]

Google Cloud: Products and services
[10]

Directory of Azure Cloud Services
[12]

Arjun Roy, Hongyi Zeng, Jasmeet Bagga, George Porter, and Alex C. Snoeren. Inside the social network’s (datacenter) network. In SIGCOMM, pages 123–137. ACM, 2015

2015
[13]

Case study of scientiﬁc data processing on a cloud using hadoop

Chen Zhang, Hans De Sterck, Ashraf Aboulnaga, Haig Djambazian, and Rob Sladek. Case study of scientiﬁc data processing on a cloud using hadoop. In International Symposium on High Performance Computing Systems and Applications , pages 400–415. Springer, 2009

2009
[14]

https://cloud.google.com/compute/

Compute engine - iaas - google cloud platform. https://cloud.google.com/compute/
[15]

https://azure.microsoft.com/

Microsoft azure: Cloud computing platform & services. https://azure.microsoft.com/
[16]

https://aws.amazon.com/

Amazon web services (aws) - cloud computing services. https://aws.amazon.com/
[17]

Jupiter rising: A decade of clos topologies and centralized control in googles datacenter network

Arjun Singh, Joon Ong, Amit Agarwal, Glen Anderson, Ashby Armistead, Roy Bannon, Seb Boving, Gaurav Desai, Bob Felderman, Paulie Germano, Anand Kanagala, Jeﬀ Provost, Jason Simmons, Eiichi Tanda, Jim Wanderer, Urs Hlzle, Stephen Stuart, and Amin Vahdat. Jupiter rising: A decad...

2015
[18]

Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A

Albert Greenberg, James R. Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A. Maltz, Parveen Patel, and Sudipta Sengupta. VL2: A Scalable and Flexible Data Center Network. Commun. ACM , 54(3):95–104, March 2011

2011
[19]

Network in Hyper-scale data centers - Facebook , 2015

Sriram Subramanian. Network in Hyper-scale data centers - Facebook , 2015

2015
[20]

Attaining the Promise and Avoiding the Pitfalls of TCP in the Datacenter

Glenn Judd. Attaining the Promise and Avoiding the Pitfalls of TCP in the Datacenter. 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) , pages 145–157, 2015

2015
[21]

Congestion Control for Large-Scale RDMA Deployments

Yibo Zhu, Haggai Eran, Daniel Firestone, Chuanxiong Guo, Marina Lipshteyn, Yehonatan Liron, Jitendra Padhye, Shachar Raindel, Mohamad Haj Yahia, and Ming Zhang. Congestion Control for Large-Scale RDMA Deployments. Proceedings of the 2015 ACM Conference on Special Interest Grou...

2015
[22]

Less is more: trading a little bandwidth for ultra-low latency in the data center

Mohammad Alizadeh, Abdul Kabbani, Tom Edsall, Balaji Prabhakar, Amin Vahdat, and Masato Yasuda. Less is more: trading a little bandwidth for ultra-low latency in the data center. Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI...

2012
[23]

A Scalable, Commodity Data Center Network Architecture

Mohammad Al-Fares, Alexander Loukissas, and Amin Vahdat. A Scalable, Commodity Data Center Network Architecture. Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication , pages 63–74, 2008

2008
[24]

HyperX: topol- ogy, routing, and packaging of eﬃcient large-scale networks

Jung Ho Ahn, Nathan Binkert, Al Davis, Moray McLaren, and Robert S Schreiber. HyperX: topol- ogy, routing, and packaging of eﬃcient large-scale networks. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis , page 41, 2009

2009
[25]

Dcell: A Scalable and Fault-tolerant Network Structure for Data Centers

Chuanxiong Guo, Haitao Wu, Kun Tan, Lei Shi, Yongguang Zhang, and Songwu Lu. Dcell: A Scalable and Fault-tolerant Network Structure for Data Centers. SIGCOMM Comput. Commun. Rev., 38(4):75–86, August 2008

2008
[26]

On the data path performance of leaf-spine datacenter fabrics

Mohammad Alizadeh and Tom Edsall. On the data path performance of leaf-spine datacenter fabrics. 2013 IEEE 21st Annual Symposium on High-Performance Interconnects , pages 71–74, 2013

2013
[27]

Xpander: Towards Optimal- Performance Datacenters

Asaf Valadarsky, Gal Shahaf, Michael Dinitz, and Michael Schapira. Xpander: Towards Optimal- Performance Datacenters. Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies, pages 205–219, 2016

2016
[28]

Introducing data center fabric, the next-generation Facebook data center network
[29]

Jellyﬁsh: Networking data centers randomly

Ankit Singla, Chi-Yao Hong, Lucian Popa, and P Brighten Godfrey. Jellyﬁsh: Networking data centers randomly. Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12) , pages 225–238, 2012

2012
[30]

A Study of Non-Blocking Switching Networks

Charles Clos. A Study of Non-Blocking Switching Networks. Bell Labs Technical Journal , 32(2):406– 424, 1953

1953
[31]

Cisco Global Cloud Index: Forecast and Methodology, 2015-2020 White Paper

2015
[32]

The Growth of eServices: Stats and Trends
[33]

How do users feel about video streaming quality on their TVs?
[34]

Cloud Technology Why Move Critical Workloads and Data Closer to Users?
[35]

Content Delivery Networks Move Closer to the Network Edge . 159
[36]

http://datacenterfrontier.com/ mapping-netflix-content-delivery-network/

Mapping netﬂix: Content delivery network spans 233 sites. http://datacenterfrontier.com/ mapping-netflix-content-delivery-network/ . visited on March 3, 2017

2017
[37]

How netﬂix works with isps around the globe to deliver a great viewing ex- perience

Ken Florance. How netﬂix works with isps around the globe to deliver a great viewing ex- perience. https://media.netflix.com/en/company-blog/how-netflix-works-with-isps-around- the-globe-to-deliver-a-great-viewing-experience , 2016

2016
[40]

Low latency geo-distributed data analytics

Qifan Pu, Ganesh Ananthanarayanan, Peter Bodik, Srikanth Kandula, Aditya Akella, Paramvir Bahl, and Ion Stoica. Low latency geo-distributed data analytics. ACM SIGCOMM Computer Communi- cation Review, 45(4):421–434, 2015

2015
[41]

http://www.level3.com/en/products/managed-dedicated-fiber/

Dark ﬁber network and pricing. http://www.level3.com/en/products/managed-dedicated-fiber/. visited on March 29, 2019

2019
[42]

https://azure.microsoft.com/en-us/ blog/how-microsoft-builds-its-fast-and-reliable-global-network/

How microsoft builds its fast and reliable global network. https://azure.microsoft.com/en-us/ blog/how-microsoft-builds-its-fast-and-reliable-global-network/ . visited on September 30, 2017

2017
[43]

https://code.facebook.com/posts/ 1782709872057497/building-express-backbone-facebook-s-new-long-haul-network/

Building express backbone: Facebooks new long-haul network. https://code.facebook.com/posts/ 1782709872057497/building-express-backbone-facebook-s-new-long-haul-network/ . visited on September 30, 2017

2017
[44]

Achieving high utilization with software- driven wan

Chi-Yao Hong, Srikanth Kandula, Ratul Mahajan, et al. Achieving high utilization with software- driven wan. In SIGCOMM, pages 15–26. ACM, 2013

2013
[45]

The prospect of inter-data-center optical networks

Xiaoxue Zhao, Vijay Vusirikala, Bikash Koley, Valey Kamalov, and Tad Hofmeister. The prospect of inter-data-center optical networks. IEEE Communications Magazine , 51(9):32–38, 2013

2013
[46]

Calendaring for wide area networks

Srikanth Kandula, Ishai Menache, Roy Schwartz, and Spandana Raj Babbula. Calendaring for wide area networks. SIGCOMM, 44(4):515–526, 2015

2015
[47]

Dynamic pricing and traﬃc engineering for timely inter-datacenter transfers

Virajith Jalaparti, Ivan Bliznets, Srikanth Kandula, Brendan Lucier, and Ishai Menache. Dynamic pricing and traﬃc engineering for timely inter-datacenter transfers. In SIGCOMM, pages 73–86. ACM, 2016

2016
[48]

The nature of data center traﬃc: measurements & analysis

Srikanth Kandula, Sudipta Sengupta, Albert Greenberg, Parveen Patel, and Ronnie Chaiken. The nature of data center traﬃc: measurements & analysis. Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference , pages 202–208, 2009

2009
[49]

Deadline-aware datacenter tcp (d2tcp)

Balajee Vamanan, Jahangir Hasan, and TN Vijaykumar. Deadline-aware datacenter tcp (d2tcp). SIGCOMM, 42(4):115–126, 2012

2012
[50]

Maltz, Jitendra Padhye, Parveen Patel, Balaji Prabhakar, Sudipta Sengupta, and Murari Sridharan

Mohammad Alizadeh, Albert Greenberg, David A. Maltz, Jitendra Padhye, Parveen Patel, Balaji Prabhakar, Sudipta Sengupta, and Murari Sridharan. Data Center TCP (DCTCP). SIGCOMM Comput. Commun. Rev. , 41(4):63–74, August 2010

2010
[51]

DeTail: reducing the ﬂow completion time tail in datacenter networks

David Zats, Tathagata Das, Prashanth Mohan, Dhruba Borthakur, and Randy Katz. DeTail: reducing the ﬂow completion time tail in datacenter networks. ACM SIGCOMM Computer Communication Review, 42(4):139–150, 2012. 160

2012
[52]

MapReduce: simpliﬁed data processing on large clusters

Jeﬀrey Dean and Sanjay Ghemawat. MapReduce: simpliﬁed data processing on large clusters. Com- munications of the ACM , 51(1):107–113, 2008

2008
[53]

Dryad: Distributed Data- parallel Programs from Sequential Building Blocks

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. Dryad: Distributed Data- parallel Programs from Sequential Building Blocks. SIGOPS Oper. Syst. Rev. , 41(3):59–72, March 2007

2007
[54]

Delay-tolerant bulk data transfers on the internet

Nikolaos Laoutaris, Georgios Smaragdakis, Rade Stanojevic, Pablo Rodriguez, and Ravi Sundaram. Delay-tolerant bulk data transfers on the internet. IEEE/ACM TON , 21(6), 2013

2013
[55]

Guaranteeing deadlines for inter-datacenter transfers

Hong Zhang, Kai Chen, Wei Bai, et al. Guaranteeing deadlines for inter-datacenter transfers. In EuroSys, page 20. ACM, 2015

2015
[56]

Jetway: Minimizing costs on inter-datacenter video traﬃc

Yuan Feng, Baochun Li, and Bo Li. Jetway: Minimizing costs on inter-datacenter video traﬃc. In ACM international conference on Multimedia , pages 259–268. ACM, 2012

2012
[57]

Optimizing bulk transfers with software-deﬁned optical wan

Xin Jin, Yiran Li, Da Wei, Siming Li, Jie Gao, Lei Xu, Guangzhi Li, Wei Xu, and Jennifer Rexford. Optimizing bulk transfers with software-deﬁned optical wan. In SIGCOMM, pages 87–100. ACM, 2016

2016
[58]

Scaling the Facebook data warehouse to 300 PB
[59]

Vivisecting youtube: An active measurement study

Vijay Kumar Adhikari, Sourabh Jain, Yingying Chen, and Zhi-Li Zhang. Vivisecting youtube: An active measurement study. In INFOCOM, pages 2521–2525. IEEE, 2012

2012
[60]

Active-Active for Multi-Regional Re- siliency

Meshenberg, Ruslan and Gopalani, Naresh and Kosewski, Luke. Active-Active for Multi-Regional Re- siliency. http://techblog.netflix.com/2013/12/active-active-for-multi-regional.html , 2013

2013
[61]

Ecoﬂow: An economical and deadline-driven inter- datacenter video ﬂow scheduling system

Yuhua Lin, Haiying Shen, and Liuhua Chen. Ecoﬂow: An economical and deadline-driven inter- datacenter video ﬂow scheduling system. In International conference on Multimedia , pages 1059–1062. ACM, 2015

2015
[62]

http://www.datastax.com/dev/blog/multi-datacenter- replication, 2012

Multi-datacenter replication in cassandra. http://www.datastax.com/dev/blog/multi-datacenter- replication, 2012

2012
[63]

https://azure.microsoft.com/en-us/blog/azure-sql-database-now-supports-powerful-geo- replication-features-on-all-service-tiers/ , 2016

Azure sql database now supports powerful geo-replication features for all service tiers. https://azure.microsoft.com/en-us/blog/azure-sql-database-now-supports-powerful-geo- replication-features-on-all-service-tiers/ , 2016

2016
[64]

Mesa: A geo-replicated online data warehouse for google’s advertising system

Ashish Gupta, Fan Yang, Jason Govig, Adam Kirsch, Kelvin Chan, Kevin Lai, Shuo Wu, Sandeep Dhoot, Abhilash Rajesh Kumar, Ankur Agiwal, Sanjay Bhansali, Mingsheng Hong, Jamie Cameron, Masood Siddiqi, David Jones, Jeﬀ Shute, Andrey Gubarev, Shivakumar Venkataraman, and Divyakant...

2016
[65]

Mdcc: Multi-data center consistency

Tim Kraska, Gene Pang, Michael J Franklin, Samuel Madden, and Alan Fekete. Mdcc: Multi-data center consistency. In EuroSys, pages 113–126. ACM, 2013

2013
[66]

Experimental assessment of inter-datacenter multicast connectivity for ethernet services in ﬂexgrid networks

Ll Gifre, F Paolucci, J Marhuenda, et al. Experimental assessment of inter-datacenter multicast connectivity for ethernet services in ﬂexgrid networks. In ECOC, pages 1–3, 2014

2014
[67]

An overlay architecture of global inter-data center networking for fast content delivery

Yasuhiro Miyao. An overlay architecture of global inter-data center networking for fast content delivery. In ICC, pages 1–6. IEEE, 2011. 161

2011
[68]

Highly eﬃcient data migration and backup for big data applications in elastic optical inter-data-center networks

Ping Lu, Liang Zhang, Xiahe Liu, Jingjing Yao, and Zuqing Zhu. Highly eﬃcient data migration and backup for big data applications in elastic optical inter-data-center networks. IEEE Network , 29(5):36–42, 2015

2015
[69]

A ﬁrst look at inter-data center traﬃc characteristics via yahoo! datasets

Yingying Chen, Sourabh Jain, Vijay Kumar Adhikari, Zhi-Li Zhang, and Kuai Xu. A ﬁrst look at inter-data center traﬃc characteristics via yahoo! datasets. In INFOCOM, pages 1620–1628. IEEE, 2011

2011
[70]

Y. Wu, Z. Zhang, C. Wu, C. Guo, Z. Li, and F. C. M. Lau. Orchestrating bulk data transfers across geo-distributed datacenters. IEEE Transactions on Cloud Computing , PP(99):1–1, 2015

2015
[71]

Steiner tree problems

FK Hwang and Dana S Richards. Steiner tree problems. Networks, 22(1):55–89, 1992

1992
[72]

https://github.com/ mouton5000/DSTAlgoEvaluation

Evaluation of approximation algorithms for the directed steiner tree problem. https://github.com/ mouton5000/DSTAlgoEvaluation. visited on Apr 27, 2017

2017
[73]

Online deadline-aware bulk transfer over inter- datacenter wans

Long Luo, Hongfang Yu, Zilong Ye, and Xiaojiang Du. Online deadline-aware bulk transfer over inter- datacenter wans. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications , pages 630–638. IEEE, 2018

2018
[74]

Bwe: Flexible, hierarchical bandwidth allocation for wan distributed computing

Alok Kumar, Sushant Jain, Uday Naik, et al. Bwe: Flexible, hierarchical bandwidth allocation for wan distributed computing. In SIGCOMM, pages 1–14, 2015

2015
[75]

Carousel: Scalable traﬃc shaping at end hosts

Ahmed Saeed, Nandita Dukkipati, Vytautas Valancius, Carlo Contavalli, Amin Vahdat, et al. Carousel: Scalable traﬃc shaping at end hosts. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication , pages 404–417. ACM, 2017

2017
[76]

Software-deﬁned networking

Nick McKeown. Software-deﬁned networking. INFOCOM keynote talk , 17(2):30–32, 2009

2009
[77]

L. Luo, Z. Li, J. Wang, and H. Yu. Simplifying ﬂow updates in software-deﬁned networks using atoman. IEEE Access, 7:39083–39097, 2019

2019
[78]

Measuring control plane latency in sdn- enabled switches

Keqiang He, Junaid Khalid, Aaron Gember-Jacobson, et al. Measuring control plane latency in sdn- enabled switches. In SOSR, pages 25:1–25:6. ACM, 2015

2015
[79]

Kodialam, T

M. Kodialam, T. V. Lakshman, and S. Sengupta. Online multicast routing with bandwidth guarantees: a new approach using multicast network ﬂow.IEEE/ACM Transactions on Networking, 11(4):676–686, 2003

2003
[80]

http://www.omnisecu.com/cisco-certified-network-associate-ccna/what-is-routing- metric-value.php
[81]

Fortz and M

B. Fortz and M. Thorup. Optimizing OSPF/IS-IS weights in a changing world. IEEE Journal on Selected Areas in Communications , 20(4):756–767, 2002

2002
[82]

Walking the Tightrope: Responsive Yet Stable Traﬃc Engineering

Srikanth Kandula et al. Walking the Tightrope: Responsive Yet Stable Traﬃc Engineering. SIG- COMM, 35(4):253–264, 2005

2005
[83]

Noormohammadpour, C

M. Noormohammadpour, C. S. Raghavendra, S. Rao, and S. Kandula. Dccast: Eﬃcient point to multipoint transfers across datacenters. In HotCloud. USENIX Association, 2017

2017
[84]

Quick- cast: Fast and eﬃcient inter-datacenter transfers using forwarding tree cohorts

Mohammad Noormohammadpour, Cauligi S Raghavendra, Srikanth Kandula, and Sriram Rao. Quick- cast: Fast and eﬃcient inter-datacenter transfers using forwarding tree cohorts. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications , pages 225–233. IEEE, 2018

2018
[85]

Data networks, 1987

Dimitri Bertsekas and Robert Gallager. Data networks, 1987. 162

1987
[86]

Finishing ﬂows quickly with preemptive scheduling

Chi-Yao Hong, Matthew Caesar, and P Godfrey. Finishing ﬂows quickly with preemptive scheduling. SIGCOMM, 42(4):127–138, 2012

2012
[87]

Noormohammadpour, C

M. Noormohammadpour, C. S. Raghavendra, S. Rao, and A. M. Madni. Rcd: Rapid close to deadline scheduling for datacenter networks. In World Automation Congress (WAC) , pages 1–6. IEEE, 2016

2016
[88]

Dcroute: Speeding up inter-datacenter traﬃc allocation while guaranteeing deadlines

Mohammad Noormohammadpour, Cauligi S Raghavendra, and Sriram Rao. Dcroute: Speeding up inter-datacenter traﬃc allocation while guaranteeing deadlines. In High Performance Computing, Data, and Analytics (HiPC) . IEEE, 2016

2016
[89]

Laor and L

M. Laor and L. Gendel. The eﬀect of packet reordering in a backbone link on application throughput. IEEE Network , 16(5):28–36, Sep 2002

2002
[90]

Companies like facebook and google have multiple data centers. do these datacenters all store copies of the same information? https://www.quora.com/Companies-like-Facebook-and-Google-have- multiple-data-centers-Do-these-datacenters-all-store-copies-of-the-same-information . vi...

2017
[91]

visited on March 3, 2017

Where are the facebook servers located worldwide? https://www.quora.com/Where-are-the- Facebook-servers-located-worldwide . visited on March 3, 2017

2017
[92]

Holistic conﬁguration management at facebook

Chunqiang Tang, Thawan Kooburat, Pradeep Venkatachalam, Akshay Chander, Zhe Wen, Aravind Narayanan, Patrick Dowell, and Robert Karl. Holistic conﬁguration management at facebook. In Symposium on Operating Systems Principles , pages 328–343. ACM, 2015

2015
[93]

http://cloudbasic.net/documentation/geo-replication- active/

Geo-replication/multi-ar (active). http://cloudbasic.net/documentation/geo-replication- active/. visited on March 5, 2017

2017
[94]

https://docs.microsoft.com/en-us/azure/sql- database/sql-database-geo-replication-overview

Overview: Sql database active geo-replication. https://docs.microsoft.com/en-us/azure/sql- database/sql-database-geo-replication-overview . visited on March 5, 2017

2017
[95]

https://docs.oracle.com/cd/E20295 01/html/821- 1217/fpcoo.html#aalgm

Using replication across multiple data centers. https://docs.oracle.com/cd/E20295 01/html/821- 1217/fpcoo.html#aalgm. visited on March 11, 2017

2017
[96]

https://docs.oracle.com/cd/E28280 01/admin

Understanding oracle internet directory replication. https://docs.oracle.com/cd/E28280 01/admin. 1111/e10029/oid replic.htm#OIDAG2201. visited on March 11, 2017

2017
[97]

https://www.sumologic.com/blog-amazon-web-services/ aws-route-53-global-load-balancing/

Global load balancing using aws route 53. https://www.sumologic.com/blog-amazon-web-services/ aws-route-53-global-load-balancing/ . visited on March 14, 2017

2017
[98]

Dissecting video server selection strategies in the youtube cdn

Ruben Torres, Alessandro Finamore, Jin Ryong Kim, Marco Mellia, Maurizio M Munafo, and Sanjay Rao. Dissecting video server selection strategies in the youtube cdn. In ICDCS, pages 248–257. IEEE, 2011

2011
[99]

https://www.slideshare

How netﬂix leverages multiple regions to increase availability (arc305). https://www.slideshare. net/AmazonWebServices/arc305-28387146. visited on March 3, 2017

2017
[100]

Multiple bulk data transfers scheduling among datacenters

Yiwen Wang, Sen Su, et al. Multiple bulk data transfers scheduling among datacenters. Computer Networks, 68:123–137, 2014

2014
[101]

Ssnf: Shared datacenter mechanism for inter-datacenter bulk transfer

Yang Yu, Wang Rong, and Wang Zhijun. Ssnf: Shared datacenter mechanism for inter-datacenter bulk transfer. In International Conference on Advanced Cloud and Big Data (CBD) , pages 184–189. IEEE, 2014. 163

2014
[102]

Inter-datacenter bulk transfers with netstitcher

Nikolaos Laoutaris, Michael Sirivianos, Xiaoyuan Yang, and Pablo Rodriguez. Inter-datacenter bulk transfers with netstitcher. In SIGCOMM, pages 74–85. ACM, 2011

2011
[103]

Postcard: Minimizing costs on inter-datacenter traﬃc with store- and-forward

Yuan Feng, Baochun Li, and Bo Li. Postcard: Minimizing costs on inter-datacenter traﬃc with store- and-forward. In International Conference on Distributed Computing Systems Workshops , pages 43–50. IEEE, 2012

2012
[104]

Lowering inter-datacenter bandwidth costs via bulk data scheduling

Thyaga Nandagopal and Krishna PN Puttaswamy. Lowering inter-datacenter bandwidth costs via bulk data scheduling. In Cluster, Cloud and Grid Computing (CCGrid) , pages 244–251. IEEE, 2012

2012
[105]

On fast and coordinated data backup in geo- distributed optical inter-datacenter networks

Jingjing Yao, Ping Lu, Long Gong, and Zuqing Zhu. On fast and coordinated data backup in geo- distributed optical inter-datacenter networks. Journal of Lightwave Technology , 33(14):3005–3015, 2015

2015
[106]

Cotton, L

M. Cotton, L. Vegoda, and D. Meyer. IANA guidelines for IPv4 multicast address assignments. Internet Requests for Comments, 2010

2010
[107]

Liang and D

S. Liang and D. Cheriton. Tcp-smo: extending tcp to support medium-scale multicast applications. In Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, volume 3, pages 1356–1365, 2002

2002
[108]

Nack-oriented reliable multicast (norm) transport protocol, 2009

Brian Adamson, Carsten Bormann, Mark Handley, and Joe Macker. Nack-oriented reliable multicast (norm) transport protocol, 2009

2009
[109]

Kodialam, T

M. Kodialam, T. V. Lakshman, and S. Sengupta. Online multicast routing with bandwidth guarantees: a new approach using multicast network ﬂow.IEEE/ACM Transactions on Networking, 11(4):676–686, Aug 2003

2003
[110]

L. H. Huang, H. C. Hsu, S. H. Shen, D. N. Yang, and W. T. Chen. Multicast traﬃc engineering for software-deﬁned networks. In INFOCOM, pages 1–9. IEEE, 2016

2016
[111]

Nagata, Y

A. Nagata, Y. Tsukiji, and M. Tsuru. Delivering a ﬁle by multipath-multicast on openﬂow networks. In International Conference on Intelligent Networking and Collaborative Systems , pages 835–840, 2013

2013
[112]

Ogawa, T

K. Ogawa, T. Iwamoto, and M. Tsuru. One-to-many ﬁle transfers using multipath-multicast with coding at source. In IEEE International Conference on High Performance Computing and Communi- cations, pages 687–694, 2016

2016
[113]

J. Cao, C. Guo, G. Lu, Y. Xiong, Y. Zheng, Y. Zhang, Y. Zhu, C. Chen, and Y. Tian. Datacast: A scalable and eﬃcient reliable group data delivery service for data centers. IEEE Journal on Selected Areas in Communications , 31(12):2632–2645, 2013

2013
[114]

Avalanche: Data center multicast using software deﬁned networking

Aakash Iyer, Praveen Kumar, and Vijay Mann. Avalanche: Data center multicast using software deﬁned networking. In COMSNETS, pages 1–8. IEEE, 2014

2014
[115]

Mctcp: Congestion-aware and robust multicast tcp in software-deﬁned networks

Tingwei Zhu, Fang Wang, Yu Hua, Dan Feng, et al. Mctcp: Congestion-aware and robust multicast tcp in software-deﬁned networks. In International Symposium on Quality of Service , pages 1–10, June 2016

2016
[116]

D. Li, M. Xu, M. c. Zhao, C. Guo, Y. Zhang, and M. y. Wu. Rdcm: Reliable data center multicast. In 2011 Proceedings IEEE INFOCOM , pages 56–60, 2011

2011
[117]

Scalable application layer multicast

Suman Banerjee, Bobby Bhattacharjee, and Christopher Kommareddy. Scalable application layer multicast. In SIGCOMM, pages 205–217. ACM, 2002. 164

2002
[118]

Rodriguez, D

A. Rodriguez, D. Kostic, and A. Vahdat. Scalability in adaptive multi-metric overlays. In International Conference on Distributed Computing Systems , pages 112–121, 2004

2004
[119]

Hierarchy-aware distributed overlays in data centers using dc2

Karthik Nagaraj, Hitesh Khandelwal, Charles Killian, and Ramana Rao Kompella. Hierarchy-aware distributed overlays in data centers using dc2. In COMSNETS, pages 1–10. IEEE, 2012

2012
[120]

Splitstream: High-bandwidth multicast in cooperative environments

Miguel Castro, Peter Druschel, Anne-Marie Kermarrec, Animesh Nandi, Antony Rowstron, and Atul Singh. Splitstream: High-bandwidth multicast in cooperative environments. In SOSP, pages 298–313. ACM, 2003

2003
[121]

BDS: A Centralized Near-optimal Overlay Network for Inter-datacenter Data Replication

Yuchao Zhang, Junchen Jiang, Ke Xu, et al. BDS: A Centralized Near-optimal Overlay Network for Inter-datacenter Data Replication. In EuroSys, pages 10:1–10:14, 2018

2018
[122]

Jeacle and J

K. Jeacle and J. Crowcroft. Tcp-xm: unicast-enabled reliable multicast. In ICCCN, pages 145–150, 2005

2005
[123]

L. H. Lehman, S. J. Garland, and D. L. Tennenhouse. Active reliable multicast. In INFOCOM, volume 2, pages 581–589 vol.2, Mar 1998

1998
[124]

Comprehensive view of a live network coding p2p system

Christos Gkantsidis, John Miller, and Pablo Rodriguez. Comprehensive view of a live network coding p2p system. In IMC, pages 177–188. ACM, 2006

2006
[125]

Shokrollahi

A. Shokrollahi. Raptor codes. IEEE Transactions on Information Theory , 52(6):2551–2567, 2006

2006
[126]

Byers, Michael Luby, Michael Mitzenmacher, and Ashutosh Rege

John W. Byers, Michael Luby, Michael Mitzenmacher, and Ashutosh Rege. A digital fountain approach to reliable distribution of bulk data. In SIGCOMM, pages 56–67. ACM, 1998

1998
[127]

L. Rizzo. Pgmcc: A tcp-friendly single-rate multicast congestion control scheme. In SIGCOMM, 2000

2000
[128]

C. A. C. Marcondes, T. P. C. Santos, A. P. Godoy, C. C. Viel, and C. A. C. Teixeira. Castﬂow: Clean-slate multicast approach using in-advance path processing in programmable networks. In IEEE Symposium on Computers and Communications , pages 94–101, 2012

2012
[129]

J. Ge, H. Shen, E. Yuepeng, Y. Wu, and J. You. An openﬂow-based dynamic path adjustment algorithm for multicast spanning trees. In IEEE International Conference on Trust, Security and Privacy in Computing and Communications , pages 1478–1483, 2013

2013
[130]

Rosen, Andrew Dolganow, Tony Przygienda, and Sam Aldrin

IJsbrand Wijnands, Eric C. Rosen, Andrew Dolganow, Tony Przygienda, and Sam Aldrin. Multicast Using Bit Index Explicit Replication (BIER). RFC 8279, November 2017

2017
[131]

Hefeeda, A

M. Hefeeda, A. Habib, B. Botev, et al. Promise: Peer-to-peer media streaming using collectcast. In MULTIMEDIA, pages 45–54. ACM, 2003

2003
[132]

The bittorrent p2p ﬁle-sharing sys- tem: Measurements and analysis

Johan Pouwelse, Pawe lGarbacki, Dick Epema, and Henk Sips. The bittorrent p2p ﬁle-sharing sys- tem: Measurements and analysis. In Proceedings of the 4th International Conference on Peer-to-Peer Systems, IPTPS’05, pages 205–216, Berlin, Heidelberg, 2005. Springer-Verlag

2005
[133]

Sherwood, R

R. Sherwood, R. Braud, and B. Bhattacharjee. Slurpie: a cooperative bulk data transfer protocol. In INFOCOM, volume 2, pages 941–951, 2004

2004
[134]

Eﬃcient algorithms for scheduling multiple bulk data transfers in inter-datacenter networks

Sen Su, Yiwen Wang, Sujuan Jiang, Kai Shuang, and Peng Xu. Eﬃcient algorithms for scheduling multiple bulk data transfers in inter-datacenter networks. International Journal of Communication Systems, 27(12), 2014

2014
[135]

BDS: A Centralized Near-optimal Overlay Network for Inter-datacenter Data Replication

Yuchao Zhang, Junchen Jiang, Ke Xu, et al. BDS: A Centralized Near-optimal Overlay Network for Inter-datacenter Data Replication. In EuroSys, EuroSys ’18, pages 10:1–10:14, 2018. 165

2018
[136]

Noormohammadpour and C

M. Noormohammadpour and C. S. Raghavendra. DDCCast: Meeting Point to Multipoint Trans- fer Deadlines Across Datacenters using ALAP Scheduling Policy. Technical Report, Department of Computer Science, University of Southern California , Report No. 17-972, 2017

2017
[137]

S. Ji, S. Liu, and B. Li. Deadline-Aware Scheduling and Routing for Inter-Datacenter Multicast Transfers. In 2018 IEEE International Conference on Cloud Engineering (IC2E) , pages 124–133, 2018

2018
[138]

Deadline-guaranteed Point-to-Multipoint Bulk Transfers in Inter-Datacenter Networks

Long Luo, Hongfang Yu, and Zilong Ye. Deadline-guaranteed Point-to-Multipoint Bulk Transfers in Inter-Datacenter Networks. ICC, 2018

2018
[139]

Dartree: Deadline-aware multicast transfers in reconﬁgurable wide-area networks

Long Luo, Klaus-Tycho Foerster, Stefan Schmid, and Hongfang Yu. Dartree: Deadline-aware multicast transfers in reconﬁgurable wide-area networks. In 27th IEEE/ACM International Symposium on Quality of Service (IWQoS 2019) , June 2019

2019
[140]

Juggler: a practical reordering resilient network stack for datacenters

Yilong Geng, Vimalkumar Jeyakumar, et al. Juggler: a practical reordering resilient network stack for datacenters. In EuroSys, page 20. ACM, 2016

2016
[141]

How hard can it be? designing and implementing a deployable multipath tcp

Costin Raiciu, Christoph Paasch, et al. How hard can it be? designing and implementing a deployable multipath tcp. In NSDI, pages 29–29. USENIX Association, 2012

2012
[142]

Tighter bounds for graph steiner tree approximation

Gabriel Robins and Alexander Zelikovsky. Tighter bounds for graph steiner tree approximation. SIAM Journal on Discrete Mathematics , 19(1):122–134, 2005

2005
[143]

A Practical Greedy Approximation for the Directed Steiner Tree Problem, pages 200–215

Dimitri Watel and Marc-Antoine Weisser. A Practical Greedy Approximation for the Directed Steiner Tree Problem, pages 200–215. Springer International Publishing, Cham, 2014

2014
[144]

Is tail-optimal scheduling possible? Operations research, 60(5):1249– 1257, 2012

Adam Wierman and Bert Zwart. Is tail-optimal scheduling possible? Operations research, 60(5):1249– 1257, 2012

2012
[145]

Gurobi Optimization

Inc. Gurobi Optimization. Gurobi optimizer reference manual, 2016

2016
[146]

https://github

Adaptive tree selection for eﬃcient point to multipoint transfers across datacenters. https://github. com/noormoha/DCCast
[147]

McKeown, T

N. McKeown, T. Anderson, et al. Openﬂow: Enabling innovation in campus networks. SIGCOMM, 38(2):69–74, 2008

2008
[148]

Openﬂow switch speciﬁcation, version 1.3.1 (wire pro- tocol 0x04)

Ben Pfaﬀ, Bob Lantz, Brandon Heller, et al. Openﬂow switch speciﬁcation, version 1.3.1 (wire pro- tocol 0x04). https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf- specifications/openflow/openflow-spec-v1.3.1.pdf, 2012

2012
[149]

S. H. Shen, L. H. Huang, D. N. Yang, and W. T. Chen. Reliable multicast routing for software-deﬁned networks. In INFOCOM, pages 181–189, April 2015

2015
[150]

Clustering Methods, pages 321–352

Lior Rokach and Oded Maimon. Clustering Methods, pages 321–352. Springer US, 2005

2005
[151]

T. Lan, D. Kao, M. Chiang, and A. Sabharwal. An Axiomatic Theory of Fairness in Network Resource Allocation. In 2010 Proceedings IEEE INFOCOM , pages 1–9, 2010

2010
[152]

pFabric: Minimal Near-optimal Datacenter Transport

Mohammad Alizadeh, Shuang Yang, Milad Sharif, et al. pFabric: Minimal Near-optimal Datacenter Transport. SIGCOMM Comput. Commun. Rev. , 43(4):435–446, August 2013

2013
[153]

PIAS: Practical information-agnostic ﬂow scheduling for data center networks

Wei Bai, Li Chen, Kai Chen, et al. PIAS: Practical information-agnostic ﬂow scheduling for data center networks. Proceedings of the 13th ACM Workshop on Hot Topics in Networks , page 25, 2014. 166

2014
[154]

Y. Lu, G. Chen, L. Luo, et al. One more queue is enough: Minimizing ﬂow completion time with explicit priority notiﬁcation. INFOCOM, pages 1–9, 2017

2017
[155]

http://www.topology-zoo.org/files/Uninett2011.gml

The Internet Topology Zoo (UNINETT). http://www.topology-zoo.org/files/Uninett2011.gml
[156]

Openﬂow switch speciﬁcation, version 1.1.0 implemented (wire protocol 0x02)

Ben Pfaﬀ, Bob Lantz, Brandon Heller, et al. Openﬂow switch speciﬁcation, version 1.1.0 implemented (wire protocol 0x02). http://archive.openflow.org/documents/openflow-spec-v1.1.0.pdf, 2011

2011
[157]

http://topology-zoo.org/dataset.html

The internet topology zoo (dataset). http://topology-zoo.org/dataset.html
[158]

https://www.cloudlab.us/

CloudLab. https://www.cloudlab.us/
[159]

https://www.nrl.navy.mil/itd/ncs/products/norm

NACK-Oriented Reliable Multicast (NORM). https://www.nrl.navy.mil/itd/ncs/products/norm
[160]

Youtube traﬃc characterization: A view from the edge

Phillipa Gill, Martin Arlitt, Zongpeng Li, and Anirban Mahanti. Youtube traﬃc characterization: A view from the edge. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement , IMC ’07, pages 15–28, New York, NY, USA, 2007. ACM

2007
[161]

OpenFlow Switch Speciﬁcation Version 1.1.0 Implemented (Wire Protocol 0x02)

Ben Pfaﬀ et al. OpenFlow Switch Speciﬁcation Version 1.1.0 Implemented (Wire Protocol 0x02). http://archive.openflow.org/documents/openflow-spec-v1.1.0.pdf, 2011

2011
[162]

http://h20565.www2.hpe.com/hpsc/ doc/public/display?sp4ts.oid=5221896&docId=emr%5Fna-c04089449&docLocale=en%5FUS

HP 5920 & 5900 Switch Series OpenFlow Command Reference. http://h20565.www2.hpe.com/hpsc/ doc/public/display?sp4ts.oid=5221896&docId=emr%5Fna-c04089449&docLocale=en%5FUS
[163]

http://h20565.www2.hpe.com/hpsc/doc/ public/display?sp4ts.oid=7399420&docLocale=en%5FUS&docId=emr%5Fna-c04771714

HP 5130 EI Switch Series OpenFlow Conﬁguration Guide. http://h20565.www2.hpe.com/hpsc/doc/ public/display?sp4ts.oid=7399420&docLocale=en%5FUS&docId=emr%5Fna-c04771714
[164]

http://h20564.www2

HP OpenFlow 1.3 Administrator Guide Wired Switches K/KA/KB/WB 15.15. http://h20564.www2. hpe.com/hpsc/doc/public/display?docId=c04217797&lang=en-us&cc=us
[165]

https://www.juniper

OpenFlow v1.3.1 Compliance Matrix for Devices Running Junos OS. https://www.juniper. net/documentation/en%5FUS/junos/topics/reference/general/junos-sdn-openflow-v1.3.1- compliance-matrix.html#table-openflow-compliance-matrix-group-types
[166]

http://enterprise.alcatel-lucent.com/ assets/documents/omniswitch-8-switch-management-guide.pdf

OmniSwitch AOS Release 8 Switch Management Guide. http://enterprise.alcatel-lucent.com/ assets/documents/omniswitch-8-switch-management-guide.pdf
[167]

http://www- 01.ibm.com/support/docview.wss?uid=isg3T7000679&aid=1

IBM System Networking RackSwitch G8264 Application Guide For Networking OS 7.9. http://www- 01.ibm.com/support/docview.wss?uid=isg3T7000679&aid=1
[168]

http://www.brocade.com/content/dam/common/documents/content-types/configuration- guide/nos-700-sdnguide.pdf

Network OS Software Deﬁned Networking (SDN) Conﬁguration Guide Supporting Network OS 7.0.0. http://www.brocade.com/content/dam/common/documents/content-types/configuration- guide/nos-700-sdnguide.pdf. 167

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

http://www.topology-zoo.org/files/Cogentco.gml

The internet topology zoo (cogent). http://www.topology-zoo.org/files/Cogentco.gml. visited on July 19, 2017

2017

[2] [2]

B4: Experience with a globally-deployed software deﬁned wan

Sushant Jain, Alok Kumar, et al. B4: Experience with a globally-deployed software deﬁned wan. SIGCOMM, 43(4):3–14, 2013

2013

[3] [3]

http://www.topology-zoo.org/files/Agis.gml

[4] [4]

http://www.topology-zoo.org/files/Ans.gml

[5] [5]

http://www.topology-zoo.org/files/AttMpls.gml

The internet topology zoo (att north america). http://www.topology-zoo.org/files/AttMpls.gml. visited on July 19, 2017

2017

[6] [6]

http://www.topology-zoo.org/files/Geant2009.gml

The Internet Topology Zoo (GEANT). http://www.topology-zoo.org/files/Geant2009.gml

[7] [7]

W. Xia, P. Zhao, Y. Wen, and H. Xie. A survey on data center networking (dcn): Infrastructure and operations. IEEE Communications Surveys Tutorials , 19(1):640–656, Firstquarter 2017

2017

[8] [8]

Zhang, F

J. Zhang, F. R. Yu, S. Wang, T. Huang, Z. Liu, and Y. Liu. Load balancing in data center networks: A survey. IEEE Communications Surveys Tutorials , 20(3):2324–2352, thirdquarter 2018

2018