Pith. sign in

REVIEW 3 major objections 3 minor 38 references

Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper establishes a complexity dichotomy: carbon-aware scheduling with a fixed task mapping is polynomial on one processor and strongly NP-complete on two or more processors, and its greedy-plus-local-search heuristics cut median…

desk verdict Solid NP-hardness and heuristic contributions, but the single-processor DP drops idle power and its polynomial-time claim is not established. read the letter →

arxiv 2507.08725 v2 pith:3E4GFZEL submitted 2025-07-11 cs.DC

Dominik Schweisgut , Anne Benoit , Yves Robert , Henning Meyerhenke This is my paper · ORCID
classification cs.DC MSC 68M2068Q1790B35
keywords carbon-awareschedulingworkflowfixedmappingdeadlineconstraintdynamicprogrammingNP-completenessgreedyheuristicsgreenenergy
topics P versus NP
open problems P versus NP
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 paper tries to establish exactly how hard it is to cut a workflow's carbon emissions by shifting task execution into greener time intervals when the task-to-processor mapping and the order on each processor are already fixed. It proves that the single-processor version of this problem is solvable in polynomial time, while the version with two or more processors is strongly NP-complete even for independent tasks on identical processors. It then offers CaWoSched, a family of 16 greedy-plus-local-search heuristics, and reports that they cut median carbon cost to roughly 0.58–0.6 of the ASAP baseline, with near-optimal performance on small instances. If the complexity results hold, they tell practitioners which settings need exact methods and which can be left to heuristics.

What carries the argument

The argument is carried by the communication-enhanced DAG $G_c$, which replaces every inter-processor communication with a fictional task on a fictional link-processor, so that all scheduling decisions reduce to placing tasks on a timeline. The polynomial result uses the notion of an E-schedule: a single-processor schedule in which every block of consecutive tasks starts or ends at an interval boundary; Lemma 4.2 shows an optimal E-schedule always exists, which restricts the dynamic program's candidate end times to $O(n^3J)$ values. For the heuristics, the load-bearing mechanisms are the task scores (slack $s(v)$, pressure $\rho(v)=\omega(v)/(s(v)+\omega(v))$, and their power-weighted variants), the refinement of intervals into subintervals induced by blocks of up to $k=3$ tasks, and a local-search hill climber that moves tasks within $[\mathrm{EST}(v), \mathrm{LST}(v)]$.

What would settle it

Take a known unsolvable 3-Partition instance, build the multi-processor reduction instance from Theorem 4.3, and ask an exact ILP whether a zero-carbon schedule exists; a 'yes' answer would refute the strong NP-completeness proof. Alternatively, run CaWoSched with realized green budgets that differ from the forecast budgets and check whether its carbon cost stays below ASAP's; a case where it exceeds ASAP would show the exact-budget assumption is load-bearing.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a complexity dichotomy: with one processor and a fixed task order, an optimal carbon-minimizing schedule can be computed in polynomial time via a dynamic program that only needs to consider task end times aligned with interval boundaries (E-schedules); with at least two processors, the problem is strongly NP-complete, so no polynomial exact algorithm exists unless P=NP. In the hard regime, the paper's CaWoSched framework combines slack- and pressure-based greedy scores, refined interval subdivisions, and local search to produce schedules whose carbon cost is close to the best known and, on small workflows, close to the ILP optimum.

Load-bearing premise

That the green power budget in every time interval is known exactly and stays constant within the interval; if the forecast is wrong, the shifted schedule may no longer be better than ASAP, and the complexity results do not cover stochastic budgets.

Editorial extensions

If this is right

  • On a single processor, carbon-aware scheduling with a fixed order is tractable, so exact schedules can replace heuristics in that setting.
  • On clusters with two or more processors, the problem is strongly NP-complete, which justifies heuristic approaches and rules out a general polynomial exact algorithm.
  • CaWoSched's median carbon cost is about 0.58–0.6 times the ASAP baseline, roughly a 40 percent reduction, and the saving grows when the deadline is looser.
  • Several heuristic variants match the ILP-optimal cost on a meaningful share of small instances while running orders of magnitude faster than the exact solver.
  • Running times stay in seconds for most workflows and a few minutes for the largest tested workflows (up to 30,000 tasks), making the approach usable in planning tools.

Reading between the lines

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

  • Editorial inference: because mapping and ordering are taken as given, the reported savings are relative to a mapping chosen without carbon in mind; a carbon-aware mapping pass could push savings beyond the 0.58–0.6 ratio.
  • Editorial inference: the E-schedule lemma exploits exact interval boundaries, so extending the one-processor result to uncertain green budgets would likely require a stochastic or robust reformulation rather than a direct adaptation of the dynamic program.
  • Editorial inference: the local-search refinement accounts for a large share of the carbon improvement, so a cheaper stand-alone hill climber starting from an ASAP schedule might capture most of the benefit on very large DAGs.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper studies carbon-aware workflow scheduling with a fixed mapping and ordering of tasks on a heterogeneous cluster, where each processor has idle and work power, and the green energy budget varies over time. The total carbon cost is the time-integrated excess of total power over the green budget. The authors claim a polynomial-time dynamic program for the single-processor case, strong NP-completeness for the multiprocessor case, an ILP formulation, and a family of heuristics (CaWoSched) with experimental evaluation against an ASAP baseline and exact solutions on small instances. The theoretical claims are central to the paper: the uniprocessor polynomial-time result rests on a dynamic program (Theorem 4.1) and an E-schedule structural lemma (Lemma 4.2), and the multiprocessor hardness claim is Theorem 4.3.

Significance. If the theoretical claims were correct, the paper would provide a clean complexity dichotomy for a natural scheduling problem and a practical heuristic framework with strong experimental support. The paper's strengths include a rigorous 3-Partition reduction (for a variable number of processors), a correct time-indexed ILP that includes idle power, and an extensive, reproducible experimental study with public code and data. However, the central uniprocessor polynomial-time result is not established under the paper's own cost model because the dynamic program omits idle-power costs, and the hardness claim as stated for 'at least two processors' is stronger than what the reduction actually proves. These issues are load-bearing and require substantial revision.

major comments (3)
  1. [Section 4.1, Eq. (1)] The dynamic programming recurrence Opt(i,t) = min_{s ≤ t-ω(vi)} {Opt(i-1,s) + cc(vi,t)} charges only cc(vi,t), the cost of executing task vi during [t-ω(vi), t[, and never charges the idle intervals between the end of vi-1 and the start of vi, nor idle time after the last task. Under the cost model of Section 3 (CC_t = max(P_t - G_j, 0) with P_t = sum_i P_i(t), including idle power), this omitted idle cost is not a constant offset because G_j varies across intervals. For example, take P_idle = 2, P_work = 2, T = 6, one task of length 3, and G = (3,3,3,0,5,5). The true optimal schedule starts at time 3 with total cost 4 (idle cost 0, active cost 4), while the DP comparing only active costs returns start time 0 (active cost 3, true total cost 5). Thus Theorem 4.1 is not established by the published recurrence.
  2. [Appendix A.2, Lemma 4.2] The proof of Lemma 4.2 claims that shifting a block of tasks to an interval with a higher green power budget 'cannot have increased' the carbon cost. This argument moves only the active load and ignores the idle power that moves in the opposite direction. Since idle power contributes to P_t and the green budget varies, the net change in cost includes a term for the idle intervals vacated and occupied by the shift. The proof therefore does not establish the existence of an optimal E-schedule under the paper's cost model. This undermines the restriction to the polynomial-size set E' that is used to make the DP polynomial-time.
  3. [Section 4.2, Theorem 4.3] The 3-Partition reduction constructs an instance with P = 3n processors, one processor per task. This proves strong NP-completeness for the class of instances where the number of processors is part of the input, but it does not prove NP-hardness for any fixed number of processors P ≥ 2, such as P=2. The abstract and introduction claim the problem is NP-hard 'for at least two processors,' which is a stronger statement than the reduction supports. Please either revise the claim to 'when the number of processors is part of the input' or provide a separate reduction for fixed P ≥ 2.
minor comments (3)
  1. [Figure 4 caption] The caption says 'cost ratios obtained by dividing heuristics carbon cost by the carbon cost of the deadline'; this should read 'by the carbon cost of the baseline' (or similar), since the text describes a comparison against the ASAP baseline.
  2. [Section 5.2] The description of the block subdivision says every task starts at the beginning of an interval 'when we look at the special case of one processor.' This is motivated by Lemma 4.2, but since Lemma 4.2 is not established under the full cost model (see major comment 2), the heuristic's design rationale should be reworded or supported independently.
  3. [Section 6.1] The power values for communication links are drawn randomly between 1 and 2, but the exact seed or distribution is not specified; the paper states the data are available, so this is a minor reproducibility note.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the complexity proofs and the ILP benchmark are self-contained, and the few self-citations are not load-bearing.

full rationale

The paper's central results are derived from first principles rather than from the authors' prior work. Theorem 4.1 is proven by a dynamic program whose recurrence (Eq. 1) is built directly on the interval structure and task execution costs, and Lemma 4.2 is argued by an explicit block-shifting construction over the interval boundaries; neither step invokes a prior result by the same authors. Theorem 4.3 is proven by a self-contained reduction from 3-Partition with an explicit interval construction, so the strong NP-completeness claim does not reduce to an imported uniqueness or equivalence result. The ILP in Section 4.3 independently encodes the cost model of Eq. (2) and is used only as an external optimal benchmark for small instances, not as a fitted predictor of the heuristics' performance. Self-citations appear in non-load-bearing places: [22] is cited for deleting Nextflow pseudo-tasks in experimental preprocessing, and [23] appears in the related-work discussion of partitioning-based heuristics; neither citation supplies the paper's complexity or optimality claims. The heuristic parameters k and mu are fixed tuning choices, not fitted values renamed as predictions, and the reported cost ratios are computed against the ASAP baseline and the ILP, so the savings claim is not circular. One reviewer concern is that the DP in Theorem 4.1 charges only cc(vi,t) and omits idle power between tasks, even though idle power is part of the paper's own cost model in Eq. (2). That is a correctness or soundness gap in the proof, not circularity: the recurrence does not equate its output with its input by construction, it simply may optimize a different objective than the stated one. Since the required circularity patterns (self-definitional equivalence, fitted input called prediction, load-bearing self-citation, or renamed known results) are absent, the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claims rest on standard scheduling axioms and a deterministic green-budget model. The two hand-set heuristic parameters (k, mu) are the only genuine free parameters in the algorithm design; they do not affect the complexity results. The communication-processor construction is a modeling device, not an empirical entity.

free parameters (2)
  • k (block size for interval subdivision) = 3
    Limits the number of subintervals in the refined greedy scheduler; chosen based on authors' experiments as a trade-off between solution quality and running time.
  • mu (local search window) = 10
    Number of time units examined to the left and right when moving a task in local search; set by the authors, not derived from data or theory.
assumptions (4)
  • standard math 3-Partition is strongly NP-complete
    Used as the source problem in the NP-completeness reduction (Appendix A.3), standard result from Garey and Johnson.
  • domain assumption Carbon cost is the integral over time of max(total_power - green_budget, 0)
    Defines the objective in Section 3 ('Carbon cost'); assumes brown power above the green budget is linearly proportional to carbon emissions.
  • domain assumption Green power budget G_j is exactly known and constant within each interval I_j
    All algorithms, the DP, the NP-hardness construction, and the ILP assume deterministic budgets (Section 3, 'Power profile'); no forecast uncertainty is modeled.
  • domain assumption Communications are modeled as tasks on fictional processors assuming a fully connected, full-duplex network
    The communication-enhanced DAG G_c (Section 3) postulates no link contention; this is a modeling simplification used in the cost computation and ILP.
invented entities (1)
  • Fictional communication processors p_{P+1}...p_{P^2}
    purpose: Represent each communication link as a processor so that communication costs become task execution costs in the schedule.
    This is a purely analytical construction to simplify cost computation; it is not a physical entity and has no testable predictions outside the model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint." pith.science (2026). https://pith.science/paper/3E4GFZEL

@misc{pith2026250708725,
  author       = {Pith},
  title        = {Pith review of: Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3E4GFZEL}},
  note         = {Machine review of arXiv:2507.08725}
}
read the original abstract

Large data and computing centers consume a significant share of the world's energy consumption. A prominent subset of the workloads in such centers are workflows with interdependent tasks, usually represented as directed acyclic graphs (DAGs). To reduce the carbon emissions resulting from executing such workflows in centers with a mixed (renewable and non-renewable) energy supply, it is advisable to move task executions to time intervals with sufficient green energy when possible. To this end, we formalize the above problem as a scheduling problem with a given mapping and ordering of the tasks. We show that this problem can be solved in polynomial time in the uniprocessor case. For at least two processors, however, the problem becomes NP-hard. Hence, we propose a heuristic framework called CaWoSched that combines several greedy approaches with local search. To assess the 16 heuristics resulting from different combinations, we also devise a simple baseline algorithm and an exact ILP-based solution. Our experimental results show that our heuristics provide significant savings in carbon emissions compared to the baseline.

Figures

Figures reproduced from arXiv: 2507.08725 by the authors.

Figure 1
Figure 1. The distribution shows for which percentage of the instances each [PITH_FULL_IMAGE:figures/full_fig_p015_1.png] view at source ↗
Figure 2
Figure 2. The ratio is the best cost found divided by the algorithm variants’ [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. The evolution of the performance profiles when adding more tolerance [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: The median of the cost ratios obtained by dividing heuristics carbon [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]
Figure 5
Figure 5. Figure 5: The evolution of the median of the cost ratios when adding more [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Boxplot of the cost ratios obtained by dividing the heuristics carbon [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: Cost ratio obtained by dividing the ILP result through the heuristic [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]
Figure 8
Figure 8. Figure 8: Time (in seconds) for each algorithm variant. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Illustration of the block shift to create an [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: Performance profile for tolerance factor 2 for the deadline (extends [PITH_FULL_IMAGE:figures/full_fig_p034_10.png]
Figure 11
Figure 11. Figure 11: Cost ratios for tolerance factor 2 for the deadline (extends Figure 5). [PITH_FULL_IMAGE:figures/full_fig_p035_11.png]
Figure 12
Figure 12. Figure 12: Time (in seconds) for each algorithm variant only for large workflows. [PITH_FULL_IMAGE:figures/full_fig_p035_12.png]
Figure 13
Figure 13. Figure 13: The evolution of the running time when adding more tolerance to [PITH_FULL_IMAGE:figures/full_fig_p036_13.png]
Figure 14
Figure 14. Figure 14: Cost ratio for different cluster sizes. 37 [PITH_FULL_IMAGE:figures/full_fig_p037_14.png]
Figure 15
Figure 15. Figure 15: Cost ratio for different power profiles. [PITH_FULL_IMAGE:figures/full_fig_p038_15.png]
Figure 16
Figure 16. Figure 16: Cost ratio for different sized workflows. Small workflows have be [PITH_FULL_IMAGE:figures/full_fig_p039_16.png]
Figure 17
Figure 17. Figure 17: Performance profile for different cluster sizes. [PITH_FULL_IMAGE:figures/full_fig_p040_17.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 37 canonical work pages

  1. [1]

    D. Abts, M. R. Marty, P. M. Wells, P. Klausler, and H. Liu. Energy proportional datacenter networks. InProc. of the 37th Annual International Symposium on Computer Architecture, page 338–347, 2010

    work page 2010

  2. [2]

    Adhikari, T

    M. Adhikari, T. Amgoth, and S. N. Srirama. A survey on scheduling strate- gies for workflows in cloud environment and emerging trends. ACM Com- put. Surv. , 52(4), aug 2019

    work page 2019

  3. [3]

    K. M. U. Ahmed, M. H. J. Bollen, and M. Alvarez. A review of data cen- ters energy consumption and reliability modeling. IEEE Access, 9:152536– 152563, 2021

    work page 2021

  4. [4]

    Angriman, A

    E. Angriman, A. van der Grinten, M. von Looz, H. Meyerhenke, M. N¨ ollen- burg, M. Predari, and C. Tzovas. Guidelines for experimental algorithmics: A case study in network analysis. Algorithms, 12(7):127, 2019

    work page 2019

  5. [5]

    Arabnejad and J

    H. Arabnejad and J. G. Barbosa. List scheduling algorithm for heteroge- neous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems , 25(3):682–694, 2013

    work page 2013

  6. [6]

    Bader, F

    J. Bader, F. Lehmann, L. Thamsen, U. Leser, and O. Kao. Lotaru: Locally predicting workflow task runtimes for resource management on heteroge- neous infrastructures. Future Generation Computer Systems, 150:171–185, 2024

    work page 2024

  7. [7]

    Predicting the Performance of Scientific Workflow Tasks for Cluster Resource Management: An Overview of the State of the Art

    J. Bader, K. West, S. Becker, S. Kulagina, F. Lehmann, L. Thamsen, H. Meyerhenke, and O. Kao. Predicting the performance of scientific work- flow tasks for cluster resource management: An overview of the state of the art, 2025. https://arxiv.org/abs/2504.20867

    work page Pith review arXiv 2025

  8. [8]

    J. G. Barbosa and B. Moreira. Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters. Parallel computing, 37(8), 2011

    work page 2011

Show all 38 references
  1. [9]

    Breukelman, S

    E. Breukelman, S. Hall, G. Belgioioso, and F. D¨ orfler. Carbon-aware computing in a network of data centers: A hierarchical game-theoretic approach. In 2024 European Control Conference (ECC) , pages 798–803. IEEE, 2024

    2024

  2. [10]

    Z. Cao, X. Zhou, H. Hu, Z. Wang, and Y. Wen. Toward a systematic survey for carbon neutral data centers. IEEE Communications Surveys & Tutorials, 24(2):895–936, 2022

    2022

  3. [11]

    Coleman, H

    T. Coleman, H. Casanova, L. Pottier, M. Kaushik, E. Deelman, and R. Fer- reira da Silva. Wfcommons: A framework for enabling scientific workflow research and development. Future Generation Computer Systems , 128:16– 27, 2022. 24

    2022

  4. [12]

    Di Tommaso, M

    P. Di Tommaso, M. Chatzou, E. W. Floden, P. P. Barja, E. Palumbo, and C. Notredame. Nextflow enables reproducible computational workflows. Nature biotechnology, 35(4):316–319, 2017

    2017

  5. [13]

    J. J. Durillo, V. Nae, and R. Prodan. Multi-objective energy-efficient work- flow scheduling using list-based heuristics. Future Generation Computer Systems, 36:221–236, July 2014

    2014

  6. [14]

    J. J. Durillo, R. Prodan, and J. G. Barbosa. Pareto tradeoff scheduling of workflows on federated commercial Clouds. Simulation Modelling Practice and Theory, 58:95–111, Nov. 2015

    2015

  7. [15]

    M. R. Garey and D. S. Johnson. Computers and Intractability, a Guide to the Theory of NP-Completeness . W.H. Freeman and Company, 1979

    1979

  8. [16]

    Gurobi Optimizer Reference Manual, 2024

    Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2024

    2024

  9. [17]

    A. A. Hagberg, D. A. Schult, and P. J. Swart. Exploring network structure, dynamics, and function using networkx. In G. Varoquaux, T. Vaught, and J. Millman, editors, Proceedings of the 7th Python in Science Conference , pages 11 – 15, Pasadena, CA USA, 2008

    2008

  10. [18]

    S. Hall, F. Micheli, G. Belgioioso, A. Radovanovi´ c, and F. D¨ orfler. Carbon- aware computing for data centers with probabilistic performance guaran- tees. arXiv preprint arXiv:2410.21510 , 2024

    2024

  11. [19]

    W. A. Hanafy, Q. Liang, N. Bashir, D. Irwin, and P. Shenoy. CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency. Proc. ACM Meas. Anal. Comput. Syst. , 7(3):57:1–57:28, Dec. 2023

    2023

  12. [20]

    Measuring Processor Power - TDP vs

    Intel Corporation. Measuring Processor Power - TDP vs. ACP. White paper, Intel Corporation, 2011

    2011

  13. [21]

    A. B. Kahn. Topological sorting of large networks. Communications of the ACM, 5(11):558–562, Nov. 1962

    1962

  14. [22]

    Kulagina, A

    S. Kulagina, A. Benoit, and H. Meyerhenke. Memory-aware adaptive scheduling of scientific workflows on heterogeneous architectures, 2025. https://arxiv.org/abs/2503.22365

    2025 arXiv

  15. [23]

    Kulagina, H

    S. Kulagina, H. Meyerhenke, and A. Benoit. Mapping large memory- constrained workflows onto heterogeneous platforms. In 53rd Int. Con- ference on Parallel Processing (ICPP) , 2024

    2024

  16. [24]

    H. Lavi. Measuring greenhouse gas emissions in data cen- tres: the environmental impact of cloud computing, 2023. https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon- data-centres-cloud-computing. 25

    2023

  17. [25]

    J. Liu, E. Pacitti, and P. Valduriez. A survey of scheduling frameworks in big data systems. International Journal of Cloud Computing , 7(2):103–128, 2018

    2018

  18. [26]

    A. H. Mahmud and S. S. Iyengar. A distributed framework for carbon and cost aware geographical job scheduling in a hybrid data center infrastruc- ture. In IEEE Int. Conference on Autonomic Computing (ICAC) , pages 75–84, 2016

    2016

  19. [27]

    M. Y. ¨Ozkaya, A. Benoit, B. U¸ car, J. Herrmann, and¨U. V. C ¸ ataly¨ urek. A scalable clustering-based task scheduler for homogeneous processors using DAG partitioning. In 33rd IEEE Int. Parallel and Distributed Processing Symp., 2019

    2019

  20. [28]

    P. Pop, K. H. Poulsen, V. Izosimov, and P. Eles. Scheduling and volt- age scaling for energy/reliability trade-offs in fault-tolerant time-triggered embedded systems. In Proceedings of the 5th IEEE/ACM International Conference on Hardware/Software Codesign and System Synthesis...

    2007

  21. [29]

    Radovanovi´ c, R

    A. Radovanovi´ c, R. Koningstein, I. Schneider, B. Chen, A. Duarte, B. Roy, D. Xiao, M. Haridasan, P. Hung, N. Care, et al. Carbon-aware computing for datacenters. IEEE Transactions on Power Systems , 38(2):1270–1280, 2022

    2022

  22. [30]

    Samadi, M

    Y. Samadi, M. Zbakh, and C. Tadonki. E-heft: Enhancement hetero- geneous earliest finish time algorithm for task scheduling based on load balancing in cloud computing. In 2018 International Conference on High Performance Computing & Simulation (HPCS) , pages 601–609, 2018

    2018

  23. [31]

    Sandokji and F

    S. Sandokji and F. Eassa. Dynamic Variant Rank HEFT Task Schedul- ing Algorithm Toward Exascale Computing. Procedia Computer Science, 163:482–493, 2019

    2019

  24. [32]

    Shi and J

    Z. Shi and J. J. Dongarra. Scheduling workflow applications on proces- sors with different capabilities. Future Generation Computer Systems , 22(6):665–675, 2006

    2006

  25. [33]

    O. Sinnen. Task scheduling for parallel systems , volume 60. John Wiley & Sons, 2007

    2007

  26. [34]

    Topcuoglu, S

    H. Topcuoglu, S. Hariri, and M.-Y. Wu. Performance-effective and low- complexity task scheduling for heterogeneous computing. IEEE Transac- tions on Parallel and Distributed Systems , 13(3):260–274, 2002

    2002

  27. [35]

    Versluis and A

    L. Versluis and A. Iosup. Taskflow: An energy-and makespan-aware task placement policy for workflow scheduling through delay management. In Proc. of the 2022 ACM/SPEC Int. Conference on Performance Engineer- ing, pages 81–88, 2022. 26

    2022

  28. [36]

    Viil and S

    J. Viil and S. N. Srirama. Framework for automated partitioning and exe- cution of scientific workflows in the cloud. The Journal of Supercomputing, 74:2656–2683, 2018

    2018

  29. [37]

    Z. Wen, S. Garg, G. S. Aujla, K. Alwasel, D. Puthal, S. Dustdar, A. Y. Zomaya, and R. Ranjan. Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm. IEEE Transactions on Industrial Informatics , 17(8...

    2021

  30. [38]

    Wiesner, I

    P. Wiesner, I. Behnke, D. Scheinert, K. Gontarska, and L. Thamsen. Let’s wait awhile: How temporal workload shifting can reduce carbon emissions in the cloud. In Proc. of the 22nd Int. Middleware Conference , pages 260– 272, 2021. 27 A Appendix A.1 Cost of a Schedule In this s...

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.