Pith. sign in

REVIEW 3 major objections 7 minor 96 references

A three-layer taxonomy with formulas and acquisition rules makes continuum performance comparable across cloud, edge, and IoT.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.5

2026-07-31 08:19 UTC pith:3M4LRJ6C

load-bearing objection Solid DCCS metric catalog with useful acquisition tags; the novel composites are defined, not validated. the 3 major comments →

arxiv 2607.28407 v1 pith:3M4LRJ6C submitted 2026-07-30 cs.DC cs.NIcs.PF

A Taxonomy of Performance Metrics for the Distributed Computing Continuum

Praveen Kumar Donta , Boris Sedlak , Alfreds Lapkovskis , Alaa Saleh , Ying Li , Victor Casamayor Pujol , Ilir Murturi , Manuel Otero Barbasan
show 1 more author
Schahram Dustdar
This is my paper
classification cs.DC cs.NIcs.PF
keywords distributed computing continuumperformance metricstaxonomyedge computingmetric acquisitionsustainabilityobservabilitycontinuum fragmentation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Distributed computing continuum systems spread work from sensors and phones up through edge and fog to the cloud, so no single metric can describe how well they run. This paper argues that evaluation has stayed fragmented—CPU here, latency there, energy somewhere else—and offers a structured taxonomy that groups metrics into computing-level, network-level, and application/user-level categories, then adds emerging ones for carbon, heat, observability, adaptivity, data locality, fragmentation, and migration stability. For representative metrics it supplies mathematical definitions and states where the data must come from (one node, many nodes, or the whole system), when (live operation or controlled experiment), and how (standard telemetry, custom code, or special instrumentation). The practical claim is that this shared map lets architects pick metrics that match their goals and compare architectures fairly instead of talking past one another.

Core claim

Transparent, consistent evaluation of heterogeneous continuum systems requires a taxonomy that jointly organizes classical metrics (execution time, throughput, latency, packet loss, task success, SLO compliance, and so on) and novel continuum-specific ones (sustainability, observability, adaptivity, data locality, continuum fragmentation, migration stability), each with a formula and an acquisition profile of scope, phase, and method.

What carries the argument

The taxonomy itself (Figure 1), plus per-metric acquisition triples (scope × phase × method) in Tables 2–5: these fix what is measured, how hard it is to collect, and whether it belongs in operations or experiments.

Load-bearing premise

The new composite indices (fragmentation, adaptivity, observability, trustworthiness, and similar scores) are assumed to be well-defined and useful in real systems without empirical calibration against running deployments.

What would settle it

Deploy the taxonomy’s novel indices on a multi-layer continuum testbed under controlled load, mobility, and failure scenarios and check whether they change scheduling or placement decisions and correlate with operator-visible outcomes better than classical metrics alone.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Metric choice can be justified by acquisition cost: single-node standard telemetry for ops, multi-node or experimental instrumentation for design-time comparison.
  • Cross-paper continuum results become easier to compare when authors report the same layer-tagged metrics and formulas.
  • Sustainability (CO₂, heat, water per useful task) enters the same evaluation frame as latency and throughput.
  • Fragmentation, data locality, and migration stability become first-class checks for orchestration and placement algorithms.
  • Future continuum simulators can implement the listed formulas as a common measurement layer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Without a reference open implementation of the novel indices, the taxonomy risks becoming a checklist rather than a working measurement standard.
  • The acquisition tables could double as a design filter: if a claimed ‘online adaptive’ controller needs Experimental Instrumentation and Full System scope, it is not yet ops-ready.
  • Carbon- and water-per-task metrics will pressure cloud and edge providers to expose footprint counters the way they already expose CPU and bandwidth.
  • Observability and trustworthiness scores point toward treating explainability and causal consistency as runtime QoS dimensions, not only offline audit traits.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The paper proposes a structured taxonomy of performance metrics for Distributed Computing Continuum Systems (DCCS), spanning cloud–fog–edge–mobile–IoT–sensor layers. Metrics are organized into computing-level, network-level, and application/user-level categories (Fig. 1; §§4–6), with mathematical formulations tied to a heterogeneous system model (§2). A distinctive contribution is the acquisition-requirement framing—scope (single-node / multi-node / full system / full app), phase (operational vs experimental), and method (standard telemetry / custom / experimental instrumentation)—summarized in Tables 2–5. Section 7 introduces emerging composite metrics aimed at sustainability, observability, adaptivity, data locality, continuum fragmentation, migration stability, and trustworthiness. The central claim is organizational and definitional: that this catalog, with formulations and acquisition tags, supports more transparent and consistent cross-layer DCCS evaluation than isolated single-dimension practices.

Significance. If adopted, the taxonomy would give the DCCS community a shared vocabulary and measurement checklist at a time when AI/edge workloads make single-layer benchmarks inadequate. The acquisition triple (scope/phase/method) is a practical contribution that goes beyond listing formulas and can guide monitoring design and experimental planning. Classical metrics are stated consistently with common usage and with the §2 model. The novel indices (CFI, Adaptivity Quotient, Observability score E, DLI, MSI, Trustworthiness, carbon/water efficiency) correctly name real continuum concerns that standard cloud or HPC suites under-emphasize. As a taxonomy paper, its value is catalog completeness and operational guidance rather than new theorems or empirical superiority proofs; on that standard the work is useful reference material for architects, benchmark designers, and continuum simulators (as the authors themselves flag in §8).

major comments (3)
  1. [§7; Table 5; Abstract; §1] §7 (esp. §§7.4–7.5, 7.8–7.10, 7.15) and Table 5: Several novel composites (Observability E in Eq. (192), Adaptivity Quotient Q in Eq. (193), Continuum Fragmentation Index CFI in Eq. (196), Migration Stability Index MSI in Eq. (197), Adaptation Cost Efficiency ACE in Eq. (198), Trustworthiness TS_j in Eq. (208)) are introduced by definition only. There is no sensitivity analysis, toy example, or comparison against operator decisions or existing baselines showing that the indices discriminate continuum quality or change placement/orchestration choices. For a taxonomy this is acceptable if framed as proposals, but the Abstract and §1 currently present them as part of the evaluation solution on equal footing with classical metrics. Please either (i) add minimal worked examples / synthetic scenarios that show how each index moves under controlled continuum conditions, or (ii) clearly demote §
  2. [§3; Eqs. (23), (44), (106), (174), (197), (207), (208)] Free parameters without selection guidance undermine comparability—the stated goal of the taxonomy. Composite weights appear repeatedly (ya_i in Eq. (23), η_i in Eq. (44), a in congestion Eq. (106), yb_i in Eq. (174), w_i / α_r in flexibility and trustworthiness, M_max_k in MSI Eq. (197), AoI_max_i in freshness Eq. (207)). Section 3 discusses metric selection criteria (relevance, sensitivity, consistency, etc.) but does not address how to set or report these weights so that two studies remain comparable. Add a short subsection (likely under §3) on weight reporting conventions, recommended defaults, or normalization procedures, and note in §7 which novel metrics are parameter-free (e.g., CFI) versus weight-dependent.
  3. [§1; before §2 or new related-work section] Related-work positioning is thin relative to the claim of filling a gap in “transparent and consistent” DCCS evaluation. The introduction cites orchestration and offloading work but does not systematically contrast this taxonomy with existing cloud/edge/IoT benchmark suites, metric surveys, or standardization efforts (e.g., SPEC, TPC-style, EdgeBench-class tools, ISO/IEC sustainability metrics, or prior metric taxonomies in fog/edge). Without that map, it is hard for readers to see what is new beyond aggregation and the acquisition tags. A compact related-work or “gap analysis” subsection—even a table of prior taxonomies vs. coverage of continuum-specific dimensions (fragmentation, migration stability, data locality, AoI)—would make the contribution claim load-bearing rather than asserted.
minor comments (7)
  1. [§1] Duplicate/near-duplicate introductory paragraphs in §1 (the DCCS paradigm is described twice with overlapping cloud/edge/IoT wording). Tighten to a single narrative.
  2. [Table 1; Eq. (7); Tables 2–5] Notation collisions and typos: Table 1 and §2 use both ι and mixed Greek/Latin; Eq. (7) averages RT_i while the left-hand side is TCT; “ya_1” style weights are unusual—prefer w_i or α_i consistently. “T able” line breaks in table captions; “CC F ragmentation”, “C. Efficiency”, “W ater”, “T rustworthiness” spacing artifacts in Table 5.
  3. [Fig. 1] Fig. 1 is central but dense; consider splitting classical vs. novel branches or using a second figure for §7 metrics so acquisition tags can be visually linked.
  4. [§4.3; §7.6; Keywords] Amdahl/Gustafson appear in §4.3 and again conceptually in §7.6; cross-reference rather than re-motivate. Keywords list “Amdahl’s Law” though it is only one of many formulations—consider broader keywords (taxonomy, benchmarking, continuum metrics).
  5. [Tables 2–5] Several acquisition rows mark Method as “Custom Instrumentation*” or Phase as “Experimental*” with asterisks explained only in prose; add a table footnote defining “*”.
  6. [§5.8; §4.15; §6.2] §5.8 title is “Response Time” while the text says “Network response time” and Table 3 says “Network Response Time”—align names. §4.15 Cost Efficiency vs §6.2 Cost: briefly state how they differ to avoid double-counting in multi-layer studies.
  7. [§8; §3] Future simulator claim in §8 is welcome; if any metric list, ontology, or machine-readable appendix is planned, mention format (e.g., whether formulations will be released as a catalog). Even a one-page checklist mapping evaluation goals → recommended metric subsets would increase immediate usability.

Circularity Check

0 steps flagged

No significant circularity: a definitional taxonomy with standard and composite metric formulas, not fitted predictions or self-justifying uniqueness claims.

full rationale

This paper catalogs and formulates performance metrics for DCCS (computing-, network-, and application-level, plus novel composites). Its central contribution is organizational and definitional: taxonomies (Fig. 1), mathematical expressions (e.g., ET, throughput, Amdahl/Gustafson speedup, Jain fairness, CFI, Adaptivity Quotient), and acquisition tags (scope/phase/method in Tables 2–5). Definitions of metrics are not circular when presented as definitions rather than as external predictions derived from those same quantities. There is no fitting of free parameters to data that are then relabeled as predictions; no uniqueness theorem imported from overlapping authors to forbid alternatives; and no ansatz smuggled in via self-citation that forces the main result. Self-citations to prior DCCS/orchestration work by overlapping authors supply system framing and motivation (§1–2, related work), which is normal and not load-bearing for the metric identities themselves. Classical formulas (Amdahl, Gustafson, Little’s law, Newton cooling, Jain index) are standard external references. The unvalidated status of novel indices is an empirical/adoption limitation, not circular reasoning. Score 0 is appropriate.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 7 invented entities

As a taxonomy paper, load-bearing content is mostly standard performance-evaluation mathematics plus domain modeling choices for DCCS. The main additions are invented composite indices and the three acquisition dimensions. No parameters are fitted to data; free parameters appear only as author-chosen weights inside composite scores.

free parameters (2)
  • Composite metric weights (ya_i, η_i, yb_i, w_i, α_r, a in congestion)
    Multiple sections define weighted sums for resource utilization, elasticity, QoE, flexibility, trustworthiness, and congestion; weights are required to sum to 1 but are not derived or fitted—left to the evaluator.
  • M_max_k (max acceptable migrations) and AoI_max_i
    Normalization caps in Migration Stability Index and freshness score are application-chosen thresholds, not measured constants.
axioms (5)
  • domain assumption DCCS can be modeled as fixed sets C,F,E,M,Γ,Ψ with binary connectivity matrix C_ij and binary task assignment x_ij
    §2 system model; underpins almost every aggregate formula.
  • domain assumption Sensor nodes Ψ produce data but have no compute; compute-capable set is ζ = c+f+e+m+ι
    §2; shapes which nodes enter computing-level metrics.
  • standard math Classical definitions (Amdahl, Gustafson, Little’s law, MTTF/MTTR availability, Jain fairness, AoI) transfer unchanged to continuum settings
    Used throughout §§4–7 without continuum-specific correction terms beyond additive overheads.
  • ad hoc to paper Metric feasibility is adequately captured by the triple (Scope ∈ {Single-Node, Multi-Node, Full System, Full App}, Phase ∈ {Operational, Experimental}, Method ∈ {Standard Telemetry, Custom, Experimental Instrumentation})
    §3.2; organizing device for all tables; not derived from measurement theory.
  • domain assumption Constant failure rates yield exponential reliability R(t)=e^{-λt}
    §4.8, §5.13; standard reliability assumption, often false under wear-out or correlated failures.
invented entities (7)
  • Continuum Fragmentation Index (CFI) no independent evidence
    purpose: Quantify how scattered free capacity is across continuum nodes for placement difficulty
    Defined in §7.8 as 1 - max free / total free; no external validation or prior standard under this name.
  • Adaptivity Quotient (Q) no independent evidence
    purpose: Score speed and magnitude of performance recovery across adaptation events
    §7.5 average of (P_post/P_base)/T_adapt; definitional composite.
  • Observability / explainability score E no independent evidence
    purpose: Combine local explainability with symmetry of inter-node causal influence
    §7.4; depends on Granger-style CI_ij and per-node E_local without specifying measurement protocol.
  • Migration Stability Index (MSI) no independent evidence
    purpose: Penalize excessive task/service migration during adaptation
    §7.9; normalized against author-chosen M_max.
  • Data Locality Index (DLI) no independent evidence
    purpose: Measure closeness of execution to data source
    §7.7; distance normalized by dist_max.
  • Trustworthiness Score (TS_j) no independent evidence
    purpose: Scalar combining reliability, availability, security, privacy, history for node selection
    §7.15 weighted sum; component scores largely unspecified.
  • Acquisition requirement triple (scope, phase, method) no independent evidence
    purpose: Classify how hard each metric is to obtain in DCCS
    §3.2 and Tables 2–5; paper-specific feasibility schema.

pith-pipeline@v1.2.0-daily-grok45 · 42431 in / 3575 out tokens · 71511 ms · 2026-07-31T08:19:24.248754+00:00 · methodology

0 comments
read the original abstract

Performance evaluation is essential for understanding, comparing, and improving computing systems, including Distributed Computing Continuum Systems (DCCS). In recent years, computational requirements have changed substantially with the growth of artificial intelligence and large-scale data-driven applications. These application tasks are increasingly distributed between resource-intensive data centers and resource-constrained edge environments. In this context, novel computing continuum architectures and algorithms are emerging, creating a need for transparent and consistent performance evaluation. However, existing evaluation practices often focus on isolated dimensions, such as computation, networking, energy efficiency, or application-level quality, and therefore provide only a partial view of cross-layer DCCS behavior. This paper presents a structured taxonomy of performance metrics for DCCS. The taxonomy organizes metrics into computing-level, network-level, and application/user-level categories, while also highlighting emerging dimensions such as sustainability, observability, adaptability, data locality, migration awareness, and continuum fragmentation. Further, we provide mathematical formulations and discuss their relevance to heterogeneous and dynamic continuum environments. We also summarize metric acquisition requirements in terms of acquisition scope, acquisition phase, and measurement method. These requirements help clarify whether a metric can be collected from a single node, multiple nodes, or the full system, and whether it is more suitable for operational monitoring or experimental evaluation.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

96 extracted references · 10 canonical work pages

  1. [1]

    In: International Conference on Service-oriented Computing, pp

    Casamayor Pujol, V., Donta, P.K., Morichetta, A., Murturi, I., Dustdar, S.: Dis- tributed computing continuum systems–opportunities and research challenges. In: International Conference on Service-oriented Computing, pp. 405–407 (2022). Springer

    2022

  2. [2]

    Computers12(10), 198 (2023) 60

    Donta, P.K., Murturi, I., Casamayor Pujol, V., Sedlak, B., Dustdar, S.: Exploring the potential of distributed computing continuum systems. Computers12(10), 198 (2023) 60

    2023

  3. [3]

    IEEE Internet Computing27(4), 53–74 (2023) https://doi.org/10.1109/MIC

    Pujol, V.C., Donta, P.K., Morichetta, A., Murturi, I., Dustdar, S.: Edge intel- ligence—research opportunities for distributed computing continuum systems. IEEE Internet Computing27(4), 53–74 (2023) https://doi.org/10.1109/MIC. 2023.3284693

    arXiv 2023

  4. [4]

    ACM computing surveys (CSUR)51(5), 1–38 (2018)

    Buyya, R., Srirama, S.N., Casale, G., Calheiros, R., Simmhan, Y., Varghese, B., Gelenbe, E., Javadi, B., Vaquero, L.M., Netto, M.A.,et al.: A manifesto for future generation cloud computing: Research directions for the next decade. ACM computing surveys (CSUR)51(5), 1–38 (2018)

    2018

  5. [5]

    ACM Computing Surveys 55(9), 1–35 (2023)

    Hua, H., Li, Y., Wang, T., Dong, N., Li, W., Cao, J.: Edge computing with artificial intelligence: A machine learning perspective. ACM Computing Surveys 55(9), 1–35 (2023)

    2023

  6. [6]

    In: 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), pp

    Dustdar, S., Murturi, I.: Towards distributed edge-based systems. In: 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), pp. 1–9 (2020). https://doi.org/10.1109/CogMI50398.2020.00021 . IEEE

    arXiv 2020

  7. [7]

    Next-Gen Digital Services

    Dustdar, S., Murturi, I.: Towards iot processes on the edge. Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future: Essays Dedicated to Michael Papazoglou on the Occasion of His 65th Birthday and His Retirement, 167–178 (2021)

    2021

  8. [8]

    Information systems frontiers17, 243–259 (2015)

    Li, S., Xu, L.D., Zhao, S.: The internet of things: a survey. Information systems frontiers17, 243–259 (2015)

    2015

  9. [9]

    IEEE Transactions on Knowledge and Data Engineering35(4), 4092–4105 (2022) https://doi.org/10.1109/TKDE.2022.3142856

    Dustdar, S., Pujol, V.C., Donta, P.K.: On distributed computing continuum sys- tems. IEEE Transactions on Knowledge and Data Engineering35(4), 4092–4105 (2022) https://doi.org/10.1109/TKDE.2022.3142856

    arXiv 2022

  10. [10]

    Information14(3), 198 (2023) https://doi.org/10.3390/info14030198

    Casamayor Pujol, V., Morichetta, A., Murturi, I., Kumar Donta, P., Dustdar, S.: Fundamental research challenges for distributed computing continuum systems. Information14(3), 198 (2023) https://doi.org/10.3390/info14030198

    work page doi:10.3390/info14030198 2023

  11. [11]

    Cognitive Computation17(1), 18 (2025)

    Rawlley, O., Gupta, S., Chandrakar, J., Johnson, M.K., Kalra, C.: Artificial intelligence inspired task offloading and resource orchestration in intelligent transportation systems. Cognitive Computation17(1), 18 (2025)

    2025

  12. [12]

    Computer Communications, 108504 (2026)

    Gong, T., Huang, S., Wang, Z., Yi, B., Kumari, S., Chen, C.M., Lv, J.: Intel- ligent orchestration of ai service chains in wireless edge networks. Computer Communications, 108504 (2026)

    2026

  13. [13]

    IEEE Transactions on Mobile Computing24(10), 10927–10944 (2025) 61

    Fan, W., Yu, Y., Bao, C., Liu, Y.: Vehicular edge intelligence: Drl-based resource orchestration for task inference in vehicle-rsu-edge collaborative networks. IEEE Transactions on Mobile Computing24(10), 10927–10944 (2025) 61

    2025

  14. [14]

    IEEE Internet of Things Journal (2026)

    Shahid, H.F., Akdemir, B., Islam, J., Ahmad, I., Harjula, E.: Iot service orchestra- tion in edge-cloud continuum with 6g: A review. IEEE Internet of Things Journal (2026)

    2026

  15. [15]

    ACM Computing Surveys58(8), 1–35 (2026)

    Kumar, B., Bhardwaj, A., Prasad Sahu, D.: Task offloading for cavs edge com- puting environment: Taxonomy, critical review, and future road map. ACM Computing Surveys58(8), 1–35 (2026)

    2026

  16. [16]

    IEEE Transactions on Network and Service Management (2026)

    Mohajer, A., Mirzaei, A., Darabi, M., Fernando, X.: Joint sla-aware task offloading and adaptive service orchestration with graph-attentive multi-agent reinforcement learning. IEEE Transactions on Network and Service Management (2026)

    2026

  17. [17]

    IEEE Transactions on Cloud Computing14(1), 276–292 (2026) https://doi.org/10.1109/TCC.2026

    Ren, Y., Yu, S., Guo, A.: Joint scheduling mechanism for dynamic slice resource allocation and task offloading in user-edge-cloud systems. IEEE Transactions on Cloud Computing14(1), 276–292 (2026) https://doi.org/10.1109/TCC.2026. 3653217

    work page doi:10.1109/tcc.2026 2026

  18. [18]

    Journal of Grid Computing24(2), 5 (2026)

    Salimi, R., Azizi, S., Shojafar, M.: Dynamic and resource-aware task scheduling in fog-cloud environments via an improved priority-aware genetic algorithm. Journal of Grid Computing24(2), 5 (2026)

    2026

  19. [19]

    Journal of Cloud Computing (2026)

    Ayouni, S., Sliti, M., Alosaimi, L.K., Maddeh, M.: Resource-aware edge–cloud continuum for lightweight wearable smart-healthcare analytics. Journal of Cloud Computing (2026)

    2026

  20. [20]

    Computer Networks257, 111021 (2025)

    Hua, K., Su, S., Wang, Y.: Intelligent service migration for the internet of vehicles in edge computing: A mobility-aware deep reinforcement learning framework. Computer Networks257, 111021 (2025)

    2025

  21. [21]

    IEEE Transactions on Mobile Computing (2025)

    Liu, Y., Bi, Y., Liu, Y., Niyato, D., Yang, K., Zhao, L., Hawbani, A.: Service satisfaction-aware adaptive service migration and resource allocation in vehicular edge computing. IEEE Transactions on Mobile Computing (2025)

    2025

  22. [22]

    IEEE Transactions on Vehicular Technology74(5), 8063–8078 (2025)

    Du, A., Jia, J., Chen, J., Wang, X., Huang, M.: Online queue-aware service migra- tion and resource allocation in mobile edge computing. IEEE Transactions on Vehicular Technology74(5), 8063–8078 (2025)

    2025

  23. [23]

    IEEE Internet of Things Journal (2025)

    Zeng, L., Zhang, C., Wang, Z., Du, H., Jia, X.: Towards collaborative and latency- aware microservice migration in mobile edge computing. IEEE Internet of Things Journal (2025)

    2025

  24. [24]

    In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), pp

    Bozkaya-Aras, E.: Optimizing service migration in iot edge networks: Digital twin-based computation and energy-efficient approach. In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2025). IEEE

    2025

  25. [25]

    IEEE Pervasive Computing24(1), 32–42 (2025) https://doi.org/10.1109/MPR V.2025

    Saleh, A., Donta, P.K., Morabito, R., Motlagh, N.H., Tarkoma, S., Lov´ en, L.: 62 Follow-me ai: Energy-efficient user interaction with smart environments. IEEE Pervasive Computing24(1), 32–42 (2025) https://doi.org/10.1109/MPR V.2025. 3539421

    work page doi:10.1109/mpr 2025

  26. [26]

    In: 2025 IEEE International Conference on Edge Computing and Com- munications (EDGE), pp

    Wang, Y., Saleh, A., Donta, P.K., Hossein Motlagh, N., Lov´ en, L., Tarkoma, S., Dustdar, S.: ikafka: Intelligent storage management for adaptive event streaming in kafka. In: 2025 IEEE International Conference on Edge Computing and Com- munications (EDGE), pp. 34–43 (2025). https://doi.org/10.1109/EDGE67623. 2025.00013

    arXiv 2025

  27. [27]

    Saleh, A., Tarkoma, S., Donta, P.K., Lindgren, A., Motlagh, N.H., Dustdar, S., Pirttikangas, S., Lov´ en, L.: UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces (2026)

    2026

  28. [28]

    Ye, P., Lapkovskis, A., Saleh, A., Zhang, Q., Donta, P.K.: NeSy-Edge: Neuro- Symbolic Trustworthy Self-Healing in the Computing Continuum (2026)

    2026

  29. [29]

    ACM Transactions on Autonomous and Adaptive Systems

    Saleh, A., Tarkoma, S., Lindgren, A., Donta, P.K., Dustdar, S., Pirttikangas, S., Lov´ en, L.: Memindex: Agentic event-based distributed memory management for multi-agent systems. ACM Transactions on Autonomous and Adaptive Systems

  30. [30]

    In: 2025 IEEE 11th World Forum on Internet of Things (WF-IoT), pp

    Donta, P.K., Zhang, Q., Dustdar, S.: Performance measurements in the ai-centric computing continuum systems. In: 2025 IEEE 11th World Forum on Internet of Things (WF-IoT), pp. 1–6 (2025). https://doi.org/10.1109/WF-IoT64238.2025. 11270701

    arXiv 2025

  31. [31]

    In: 2024 IEEE International Conference on Edge Computing and Com- munications (EDGE), Shenzhen, China, pp

    Sedlak, B., Pujol, V.C., Donta, P.K., Dustdar, S.: Markov Blanket Composition of SLOs. In: 2024 IEEE International Conference on Edge Computing and Com- munications (EDGE), Shenzhen, China, pp. 128–138 (2024). https://doi.org/10. 1109/EDGE62653.2024.00025

    arXiv 2024

  32. [32]

    Interna- tional Edition, ??? (1992)

    Kant, K.: Introduction to Computer System Performance Evaluation. Interna- tional Edition, ??? (1992)

    1992

  33. [33]

    In: Proceedings of the 2024 Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evalu- ating Algorithms for Distributed systems

    Casamayor Pujol, V., Sedlak, B., Xu, Y., Donta, P.K., Dustdar, S.: DeepSLOs for the Computing Continuum. In: Proceedings of the 2024 Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evalu- ating Algorithms for Distributed systems. ApPLIED’24, pp. 1–10. Association for Computing Machinery, New York, NY, USA (2024). http...

    arXiv 2024

  34. [34]

    In: Proceedings of the 24th Interna- tional Middleware Conference, New York, USA (2023)

    Cao, Y.: Better Orchestration for SLO-Oriented Cross-site Microservices in Multi-tenant Cloud/Edge Continuum. In: Proceedings of the 24th Interna- tional Middleware Conference, New York, USA (2023). https://doi.org/10.1145/ 3626564.3629091 63

    arXiv 2023

  35. [35]

    IEEE Transactions on Services Computing17(1), 113–126 (2024) https://doi.org/10.1109/TSC.2023.3335412

    Lu, S., Wu, J., Lu, P., Wang, N., Liu, H., Fang, J.: QoS-Aware Online Ser- vice Provisioning and Updating in Cost-Efficient Multi-Tenant Mobile Edge Computing. IEEE Transactions on Services Computing17(1), 113–126 (2024) https://doi.org/10.1109/TSC.2023.3335412

    arXiv 2024

  36. [36]

    Sensors23(8), 4008 (2023) https://doi.org/10.3390/s23084008

    ˇCili´ c, I., Krivi´ c, P., PodnarˇZarko, I., Kuˇ sek, M.: Performance Evaluation of Con- tainer Orchestration Tools in Edge Computing Environments. Sensors23(8), 4008 (2023) https://doi.org/10.3390/s23084008

    work page doi:10.3390/s23084008 2023

  37. [37]

    IEEE Internet Computing30(1), 81–90 (2026) https://doi

    Herrera, J.L., Sedlak, B., Dustdar, S.: Active Inference for Sustainable Computing Continuum Systems. IEEE Internet Computing30(1), 81–90 (2026) https://doi. org/10.1109/MIC.2025.3590621

    arXiv 2026

  38. [38]

    Cambridge university press, ??? (2005)

    Lilja, D.J.: Measuring Computer Performance: a Practitioner’s Guide. Cambridge university press, ??? (2005)

    2005

  39. [39]

    Journal of Network and Computer Applications61, 115–132 (2016)

    Cheikhrouhou, O.: Secure group communication in wireless sensor networks: a survey. Journal of Network and Computer Applications61, 115–132 (2016)

    2016

  40. [40]

    Information Fusion49, 1–25 (2019) https: //doi.org/10.1016/j.inffus.2018.09.013

    Kumar, D.P., Amgoth, T., Annavarapu, C.S.R.: Machine learning algorithms for wireless sensor networks: A survey. Information Fusion49, 1–25 (2019) https: //doi.org/10.1016/j.inffus.2018.09.013

    work page doi:10.1016/j.inffus.2018.09.013 2019

  41. [41]

    ACM Computing Surveys (CSUR)52(5), 1–37 (2019)

    Hong, C.-H., Varghese, B.: Resource management in fog/edge computing: a sur- vey on architectures, infrastructure, and algorithms. ACM Computing Surveys (CSUR)52(5), 1–37 (2019)

    2019

  42. [42]

    Donta, P.K., Monteiro, E., Dehury, C.K., Murturi, I.: Learning-driven ubiquitous mobile edge computing:: Network management challenges for future generation Internet of Things (2023)

    2023

  43. [43]

    ACM Computing Surveys (2023) https://doi.org/10.1145/3579992

    Wang, X., Li, J., Ning, Z., Song, Q., Guo, L., Guo, S., Obaidat, M.S.: Wireless powered mobile edge computing networks: A survey. ACM Computing Surveys (2023) https://doi.org/10.1145/3579992

    work page doi:10.1145/3579992 2023

  44. [44]

    IEEE Communications Sur- veys & Tutorials19(4), 2322–2358 (2017) https://doi.org/10.1109/COMST.2017

    Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: The communication perspective. IEEE Communications Sur- veys & Tutorials19(4), 2322–2358 (2017) https://doi.org/10.1109/COMST.2017. 2745201

    work page doi:10.1109/comst.2017 2017

  45. [45]

    Nature614(1), 214–216 (2023)

    Jo, A.: The promise and peril of generative ai. Nature614(1), 214–216 (2023)

    2023

  46. [46]

    Journal of AI7(1), 52–62 (2023)

    Baidoo-Anu, D., Ansah, L.O.: Education in the era of generative artificial intelli- gence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning. Journal of AI7(1), 52–62 (2023)

    2023

  47. [47]

    Nature medicine, 1–11 (2023)

    Thirunavukarasu, A.J., Ting, D.S.J., Elangovan, K., Gutierrez, L., Tan, T.F., 64 Ting, D.S.W.: Large language models in medicine. Nature medicine, 1–11 (2023)

    2023

  48. [48]

    Learning and individual differences103, 102274 (2023)

    Kasneci, E., Seßler, K., K¨ uchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G¨ unnemann, S., H¨ ullermeier, E.,et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences103, 102274 (2023)

    2023

  49. [49]

    Performance Evaluation49(1), 57–76 (2002) https:// doi.org/10.1016/S0166-5316(02)00131-1

    Chaintreau, A., De Vleeschauwer, D.: A closed form formula for long-lived tcp connections throughput. Performance Evaluation49(1), 57–76 (2002) https:// doi.org/10.1016/S0166-5316(02)00131-1 . Performance 2002

    work page doi:10.1016/s0166-5316(02)00131-1 2002

  50. [50]

    Acta Informatica34(8), 637–652 (1997)

    Acz´ el, J., Ertel, W.: A new formula for speedup and its characterization. Acta Informatica34(8), 637–652 (1997)

    1997

  51. [51]

    In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference

    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference. AFIPS ’67 (Spring), pp. 483–485. ACM, New York, NY, USA (1967). https://doi.org/10.1145/1465482.1465560

    arXiv 1967

  52. [52]

    Gustafson, J.L.: Reevaluating amdahl’s law. Commun. ACM31(5), 532–533 (1988) https://doi.org/10.1145/42411.42415

    arXiv 1988

  53. [53]

    Google Patents

    Alexander, B.D., Simsick, J.L., Dillon, R.A., Giedt, J.W., Mellott, M.M., Wiles, J.E., Olson, T.L.: System and method for application resource utilization meter- ing and cost allocation in a utility computing environment. Google Patents. US Patent 7,499,968 (2009)

    2009

  54. [54]

    Internet Technology Letters9(3), 70250 (2026)

    Murturi, I., Sedlak, B., Farahani, R., Dustdar, S.: Performance evaluation of privacy models for data streams on the edge. Internet Technology Letters9(3), 70250 (2026)

    2026

  55. [55]

    In: European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, pp

    Shipman, G.M., Brightwell, R., Barrett, B., Squyres, J.M., Bloch, G.: Investi- gations on infiniband: Efficient network buffer utilization at scale. In: European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, pp. 178–186 (2007). Springer

    2007

  56. [56]

    EAI Endorsed Transactions on Cloud Systems2(5), 172 (2016)

    Beltr´ an, M.: Defining an elasticity metric for cloud computing environments. EAI Endorsed Transactions on Cloud Systems2(5), 172 (2016)

    2016

  57. [57]

    IEEE Transactions on Services Computing11(2), 430–447 (2018) https://doi.org/10.1109/TSC.2017.2711009

    Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Elasticity in cloud com- puting: State of the art and research challenges. IEEE Transactions on Services Computing11(2), 430–447 (2018) https://doi.org/10.1109/TSC.2017.2711009

    arXiv 2018

  58. [58]

    In: 10th International Conference on Autonomic Computing (ICAC 13), pp

    Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: What it is, and what it is not. In: 10th International Conference on Autonomic Computing (ICAC 13), pp. 23–27 (2013) 65

    2013

  59. [59]

    ACM Transactions on Internet Technology (TOIT)22(3), 1–21 (2022)

    Murturi, I., Dustdar, S.: Decent: A decentralized configurator for controlling elasticity in dynamic edge networks. ACM Transactions on Internet Technology (TOIT)22(3), 1–21 (2022)

    2022

  60. [60]

    Future Generation Computer Systems160, 92–108 (2024)

    Sedlak, B., Pujol, V.C., Donta, P.K., Dustdar, S.: Equilibrium in the computing continuum through active inference. Future Generation Computer Systems160, 92–108 (2024)

    2024

  61. [61]

    ACM Computing Surveys (CSUR)54(11s), 1–37 (2022)

    Muralidhar, R., Borovica-Gajic, R., Buyya, R.: Energy efficient computing sys- tems: Architectures, abstractions and modeling to techniques and standards. ACM Computing Surveys (CSUR)54(11s), 1–37 (2022)

    2022

  62. [62]

    Renewable and Sustainable Energy Reviews58, 674–691 (2016)

    Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews58, 674–691 (2016)

    2016

  63. [63]

    Cluster Computing28(1), 42 (2025)

    Prieto, A., Prieto, B., Escobar, J.J., Lampert, T.: Evolution of computing energy efficiency: Koomey’s law revisited. Cluster Computing28(1), 42 (2025)

    2025

  64. [64]

    Energy Reports13, 1739–1763 (2025)

    Alsharif, M.H., Jahid, A., Kannadasan, R., Singla, M.K., Gupta, J., Nisar, K.S., Abdel-Aty, A.-H., Kim, M.-K.: Survey of energy-efficient fog computing: Techniques and recent advances. Energy Reports13, 1739–1763 (2025)

    2025

  65. [65]

    In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE), pp

    Govori, E., Murturi, I., Dustdar, S.: A comprehensive performance evaluation of procedural geometry workloads on resource-constrained devices. In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE), pp. 271–279 (2023). IEEE

    2023

  66. [66]

    IEEE Sensors Journal20(4), 2224–2233 (2020) https://doi.org/10.1109/JSEN.2019.2949146

    Donta, P.K., Rao, B.S.P., Amgoth, T., Annavarapu, C.S.R., Swain, S.: Data col- lection and path determination strategies for mobile sink in 3d wsns. IEEE Sensors Journal20(4), 2224–2233 (2020) https://doi.org/10.1109/JSEN.2019.2949146

    arXiv 2020

  67. [67]

    Sustainable Computing: Informatics and Systems46, 101100 (2025) https://doi.org/10.1016/j.suscom.2025.101100

    Otero, M., Garc ´ ıa, J.M., Fernandez, P.: An extensible lightweight framework for distributed telemetry of microservices. Sustainable Computing: Informatics and Systems46, 101100 (2025) https://doi.org/10.1016/j.suscom.2025.101100

    arXiv 2025

  68. [68]

    ACM Trans

    Lamport, L.: Specifying concurrent program modules. ACM Trans. Program. Lang. Syst.5(2), 190–222 (1983) https://doi.org/10.1145/69624.357207

    arXiv 1983

  69. [69]

    Lamport, L.: Concurrent Algorithms, 1st edn., pp. 47–80. Asso- ciation for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3544585.3544591

    arXiv 2022

  70. [70]

    Lamport, L.: Turing lecturethe computer science of concurrency: the early years. Commun. ACM58(6), 71–76 (2015) https://doi.org/10.1145/2771951

    work page doi:10.1145/2771951 2015

  71. [71]

    IEEE Transactions on Mobile Computing24(9), 9222–9237 (2025) https: //doi.org/10.1109/TMC.2025.3559707

    Chen, Q., Song, X., Song, T., Yang, Y.: Vehicular edge computing networks 66 optimization via drl-based communication resource allocation and load balanc- ing. IEEE Transactions on Mobile Computing24(9), 9222–9237 (2025) https: //doi.org/10.1109/TMC.2025.3559707

    arXiv 2025

  72. [72]

    Jain, R., Chiu, D., Hawe, W.: A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems (1998)

    1998

  73. [73]

    Wiley Online Library (2026)

    Murturi, I., Dehury, C.K., Monteiro, E., Donta, P.K.: Intelligent Ubiquitous Mobile Edge Network Management. Wiley Online Library (2026)

    2026

  74. [74]

    Google Patents

    Link, C.A., Im, H.: Method and system for peer-to-peer network latency measurement. Google Patents. US Patent 6,012,096 (2000)

    2000

  75. [75]

    https://arxiv.org/abs/2607.18273

    Hansson, T., Donta, P.K.: Predictive RTO for CoAP using Lightweight Support Vector Regression in Internet of Things (2026). https://arxiv.org/abs/2607.18273

    Pith/arXiv arXiv 2026

  76. [76]

    RFC 3393 (2002)

    Demichelis, C., Chimento, P.: IP Packet Delay Variation Metric for IP Perfor- mance Metrics. RFC 3393 (2002). https://doi.org/10.17487/RFC3393

    work page doi:10.17487/rfc3393 2002

  77. [77]

    IEEE network17(6), 27–35 (2003)

    Prasad, R., Dovrolis, C., Murray, M., Claffy, K.: Bandwidth estimation: metrics, measurement techniques, and tools. IEEE network17(6), 27–35 (2003)

    2003

  78. [78]

    IEEE/ACM Transactions on Networking 16(2), 307–320 (2008) https://doi.org/10.1109/TNET.2007.900412

    Sommers, J., Barford, P., Duffield, N., Ron, A.: A geometric approach to improv- ing active packet loss measurement. IEEE/ACM Transactions on Networking 16(2), 307–320 (2008) https://doi.org/10.1109/TNET.2007.900412

    arXiv 2008

  79. [79]

    Computer Networks93, 408–422 (2015)

    Persico, V., Marchetta, P., Botta, A., Pescap` e, A.: Measuring network throughput in the cloud: The case of amazon ec2. Computer Networks93, 408–422 (2015)

    2015

  80. [80]

    arXiv preprint arXiv:2605.09508 (2026)

    Xie, H., Zhang, C., Luo, P., Zhang, Z., Yang, G., Zhang, X., Soong, B.-H.: Risk-aware safe throughput forecasting for starlink networks. arXiv preprint arXiv:2605.09508 (2026)

    Pith/arXiv arXiv 2026

Showing first 80 references.