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REVIEW 3 major objections 2 minor 1 cited by

S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything without Supervision

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A fast agglomerative pooling algorithm and a query-wise self-distillation task turn self-supervised segmentation into a continuous, scalable pretraining loop with reported gains over UnSAM.

desk verdict The submission is a hollow shell: the abstract promises a segmentation method with big benchmark gains, but the full text is an unrelated power-capping paper, so there is nothing to review. read the letter →

arxiv 2508.06995 v2 pith:FN7VHESC submitted 2025-08-09 cs.CV

Huihui Xu , Jin Ye , Hongqiu Wang , Changkai Ji , Jiashi Lin , Ming Hu , Ziyan Huang , Ying Chen
show 5 more authors
Chenglong Ma Tianbin Li Lihao Liu Junjun He Lei Zhu
This is my paper · ORCID
classification cs.CV
keywords self-supervisedsegmentationpseudo-maskgenerationagglomerativepoolingself-distillationsemanticinstancecontinuouspretraininguniversal
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 remove the main scaling bottleneck in self-supervised segmentation: the slow, offline pseudo-mask generation that previous methods run between training epochs. It introduces UniAP, an agglomerative pooling step that finds groups of similar nodes in parallel at each layer, producing semantic, instance, and multi-granular pseudo-masks in tens of milliseconds per image. It then adds QuerySD, a self-distillation pretext task that lets a student network and a momentum teacher train continuously on those masks. The reported result is a S2-UniSeg model that outperforms UnSAM by +6.9 AP on COCO, +11.1 AR on UVO, +4.5 PixelAcc on COCOStuff-27, and +8.0 RQ on Cityscapes under the same setting, with further gains when pretraining on a 2M-image subset. If true, label-free universal segmentation pretraining becomes scalable and no longer hostage to a discontinuous optimization schedule.

What carries the argument

The two load-bearing components are UniAP and QuerySD. UniAP (Fast Universal Agglomerative Pooling) performs agglomerative grouping in parallel across layers, generating pseudo-masks at multiple granularities quickly enough to fit inside the training loop. QuerySD (Query-wise Self-Distillation) is the segmentation-oriented pretext task that aligns student and momentum-teacher features through local-to-global correspondences, enabling continuous pretraining. Together they remove the offline pseudo-mask generation step between epochs.

What would settle it

On a held-out set like COCO or UVO, compare UniAP's pseudo-masks directly with human annotations; then train the same S2-UniSeg pipeline with UniAP masks versus corrupted or random masks of matched granularity. If mask quality is poor, or if the corrupted-mask control reaches the same AP/AR, then the claimed mechanism—fast accurate pseudo-masks plus continuous self-distillation—is not what drives the reported results.

Watch

Extended reading notes

Core claim

The central claim is that fast, continuous self-supervised pretraining can replace the multi-stage offline pseudo-mask generation used by prior methods. UniAP identifies groups of similar nodes in parallel at every layer, so one pass yields semantic-level, instance-level, and multi-granular masks in tens of milliseconds per image. QuerySD then trains a student network against a momentum teacher using query-wise local-to-global correspondences, making the optimization continuous rather than epoch-by-epoch. The paper reports that this S2-UniSeg model outperforms UnSAM on four benchmarks under the same setting, and that scaling to a larger 2M-image subset of SA-1B brings further gains on all fo

Load-bearing premise

The method stands on the premise that UniAP's fast pseudo-masks are accurate enough, and the QuerySD self-distillation signal on them is informative enough, that the learned representations transfer to real semantic and instance segmentation benchmarks.

Editorial extensions

If this is right

  • Pretraining can scale to much larger unlabeled datasets because pseudo-masks are generated in tens of milliseconds rather than in a separate offline stage.
  • The student/teacher loop turns segmentation pretraining into a continuous optimization problem, potentially avoiding suboptimal solutions caused by intermittent mask updates.
  • One forward pass supplies semantic, instance, and multi-granular masks, so the same pretraining can feed multiple segmentation benchmarks.
  • The reported margins over UnSAM—+6.9 AP on COCO, +11.1 AR on UVO, +4.5 PixelAcc on COCOStuff-27, and +8.0 RQ on Cityscapes—imply that mask speed and training continuity, not just mask quality, drive transfer.
  • Scaling to a 2M-image subset of SA-1B yields further gains on all four benchmarks, suggesting the approach benefits from more unlabeled data.

Reading between the lines

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

  • The full text accompanying this paper is a different manuscript on cloud data-center power capping, so the S2-UniSeg benchmark numbers here are taken from the abstract and the experimental details behind them could not be inspected in this pass.
  • If the speed claim transfers to production, the same parallel agglomerative pooling could serve as a pre-labeling engine for interactive annotation, cutting human labeling cost for segmentation at scale—an application the paper does not discuss.
  • QuerySD's local-to-global correspondence objective may transfer to other dense prediction tasks, such as depth or boundary estimation, but the paper does not test this; a natural extension would be to pretrain on the same pseudo-masks and probe those tasks.
  • A testable separation is to hold pseudo-mask quality fixed and vary only training continuity, or vice versa, to attribute the reported gains to UniAP's speed versus QuerySD's continuous signal.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The submission is internally incoherent. The title and abstract describe S2-UniSeg, a self-supervised universal image segmentation method with two technical components (UniAP pseudo-mask generation and QuerySD self-distillation), and claim benchmark gains over UnSAM on COCO, UVO, COCOStuff-27, and Cityscapes. However, the full text supplied is an entirely different paper: "Learning-Enabled Adaptive Power Capping Scheme for Cloud Data Centers," an IEEE Transactions on Smart Grid manuscript about model-based reinforcement learning for data-center power capping. There is no description of UniAP, QuerySD, the student/teacher architecture, the SA-1B scaling experiment, or any segmentation experiment in the body. The claimed results therefore have no supporting technical content in the submission.

Significance. If the claimed results were substantiated, the work could be significant for scalable self-supervised segmentation: replacing a multi-stage offline pseudo-mask generation loop with a fast single-stage pooling algorithm would address a real scalability bottleneck, and the reported margins over UnSAM are large. The paper also advertises public code and pretrained models. However, none of this is evidenced in the submitted manuscript. The body text belongs to a different domain and contains no algorithmic details, no experimental setup, no tables, and no ablations for S2-UniSeg. As submitted, the significance cannot be assessed; the submission provides an abstract with numerical claims and a full text that does not support them.

major comments (3)
  1. [Title/Abstract vs. Full Text] The full text of the submission is not the paper described by the title and abstract. The abstract describes S2-UniSeg, UniAP, QuerySD, and segmentation benchmarks (COCO, UVO, COCOStuff-27, Cityscapes), but the body is an IEEE paper on adaptive power capping in cloud data centers by Yimeng Sun et al. This is a load-bearing mismatch: no content in Sections I-V pertains to image segmentation, pseudo-mask generation, self-distillation, or any of the four benchmarks referenced in the abstract.
  2. [Abstract, claimed gains] The central quantitative claims (AP+6.9 on COCO, AR+11.1 on UVO, PixelAcc+4.5 on COCOStuff-27, RQ+8.0 on Cityscapes) appear only in the abstract. The body contains no experimental section for segmentation, no comparison tables, no error bars, no evaluation protocol, and no definition of the baselines or the 'same setting' under which S2-UniSeg is compared to UnSAM. These numbers are therefore unverifiable from the submitted manuscript. This is not a matter of presentation; the evidence for the paper's thesis is absent.
  3. [Abstract, UniAP speed claim] The abstract states that UniAP generates semantic-, instance-, and multi-granular pseudo-masks 'within ens of milliseconds for one image.' The body provides no algorithm pseudocode, no complexity analysis, no runtime measurements, and no comparison with existing pseudo-mask generation costs. Since the speed of UniAP is one of the two pillars of the scalability argument, this claim is entirely unsupported.
minor comments (2)
  1. [Abstract] Typographical error: 'within ens of milliseconds' should be 'within tens of milliseconds.' More importantly, the abstract is the only place where the method and results are described; a reader would expect at least one corresponding section in the body.
  2. [Full text metadata] The arXiv identifier shown in the body footer (2508.06994) differs from the submission identifier (2508.06995). This is consistent with the body being a different manuscript and should be corrected or explained by the authors.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identifiable: the provided full text is an unrelated IEEE power-capping paper, so the S2-UniSeg abstract's claims have no derivational chain to audit; unverifiability is not circularity.

full rationale

The abstract of arXiv:2508.06995 claims that S2-UniSeg outperforms UnSAM by AP+6.9 on COCO, AR+11.1 on UVO, PixelAcc+4.5 on COCOStuff-27, and RQ+8.0 on Cityscapes, and that UniAP generates pseudo-masks within tens of milliseconds. However, the supplied full text is an entirely different manuscript, 'Learning-Enabled Adaptive Power Capping Scheme for Cloud Data Centers' by Yimeng Sun et al., published in IEEE Transactions on Smart Grid. It contains no mention of S2-UniSeg, UniAP, QuerySD, unsupervised segmentation, COCO, UVO, COCOStuff, Cityscapes, or any of the proposed method's components or equations. Consequently, there is no derivation chain to walk and no step in which a prediction reduces to an input by construction, a fitted parameter is renamed as a prediction, or a load-bearing claim rests on a self-citation. Hard rule 1 requires quoting the paper and exhibiting a specific reduction to claim circularity; here there is no such reduction to exhibit. The abstract's benchmark claims are unsupported by the body text, but that is a submission-integrity or verifiability problem, not a circularity problem. Therefore the honest finding is no significant circularity: score 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

With only the abstract available, no free parameters or invented entities can be identified from the text. The single axiom listed is the core dependency of the proposed pretraining pipeline. The full manuscript, if obtained, would allow a complete ledger.

assumptions (1)
  • domain assumption Pseudo-masks generated by UniAP are accurate enough to serve as training targets for segmentation pretraining.
    The entire S2-UniSeg pretraining depends on the quality of these unsupervised masks; the abstract asserts speed (tens of ms) but not mask quality validation.

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Cite this review

Pith. "Pith review of S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything without Supervision." pith.science (2026). https://pith.science/paper/FN7VHESC

@misc{pith2026250806995,
  author       = {Pith},
  title        = {Pith review of: S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything without Supervision},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FN7VHESC}},
  note         = {Machine review of arXiv:2508.06995}
}
read the original abstract

Recent self-supervised image segmentation models have achieved promising performance on semantic segmentation and class-agnostic instance segmentation. However, their pretraining schedule is multi-stage, requiring a time-consuming pseudo-masks generation process between each training epoch. This time-consuming offline process not only makes it difficult to scale with training dataset size, but also leads to sub-optimal solutions due to its discontinuous optimization routine. To solve these, we first present a novel pseudo-mask algorithm, Fast Universal Agglomerative Pooling (UniAP). Each layer of UniAP can identify groups of similar nodes in parallel, allowing to generate both semantic-level and instance-level and multi-granular pseudo-masks within ens of milliseconds for one image. Based on the fast UniAP, we propose the Scalable Self-Supervised Universal Segmentation (S2-UniSeg), which employs a student and a momentum teacher for continuous pretraining. A novel segmentation-oriented pretext task, Query-wise Self-Distillation (QuerySD), is proposed to pretrain S2-UniSeg to learn the local-to-global correspondences. Under the same setting, S2-UniSeg outperforms the SOTA UnSAM model, achieving notable improvements of AP+6.9 on COCO, AR+11.1 on UVO, PixelAcc+4.5 on COCOStuff-27, RQ+8.0 on Cityscapes. After scaling up to a larger 2M-image subset of SA-1B, S2-UniSeg further achieves performance gains on all four benchmarks. Our code and pretrained models are available at https://github.com/bio-mlhui/S2-UniSeg

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Reference graph

Works this paper leans on

51 extracted references · 48 canonical work pages · cited by 1 Pith paper

  1. [1]

    Deepjs: Job scheduling based on deep reinforcement learning in cloud data center,

    F. Li and B. Hu, “Deepjs: Job scheduling based on deep reinforcement learning in cloud data center,” in Proceedings of the 4th International Conference on Big Data and Computing , 2019, pp. 48–53

    work page 2019

  2. [2]

    Bin packing with linear usage costs–an application to energy management in data centres,

    H. Cambazard, D. Mehta, B. O’Sullivan, and H. Simonis, “Bin packing with linear usage costs–an application to energy management in data centres,” in Principles and Practice of Constraint Programming: 19th International Conference, CP 2013, Uppsala, Sweden, September 16-20,

    work page 2013

  3. [3]

    Data center demand response in deregulated electricity markets,

    S. Bahrami, V . W. Wong, and J. Huang, “Data center demand response in deregulated electricity markets,” IEEE Transactions on Smart Grid , vol. 10, no. 3, pp. 2820–2832, 2018

    work page 2018

  4. [4]

    Learning scheduling algorithms for data processing clusters,

    H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Al- izadeh, “Learning scheduling algorithms for data processing clusters,” in Proceedings of the ACM special interest group on data communication , 2019, pp. 270–288

    work page 2019

  5. [5]

    Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective,

    W. Liu, Y . Yan, Y . Sun, H. Mao, M. Cheng, P. Wang, and Z. Ding, “Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective,” Applied Energy , vol. 338, p. 120918, 2023. 12

    work page 2023

  6. [6]

    Modeling demand response capability by inter- net data centers processing batch computing jobs,

    J. Li, Z. Bao, and Z. Li, “Modeling demand response capability by inter- net data centers processing batch computing jobs,” IEEE Transactions on Smart Grid , vol. 6, no. 2, pp. 737–747, 2014

    work page 2014

  7. [7]

    A two-stage framework for the multi-user multi-data center job scheduling and resource allocation,

    J. Lin, D. Cui, Z. Peng, Q. Li, and J. He, “A two-stage framework for the multi-user multi-data center job scheduling and resource allocation,” IEEE Access, vol. 8, pp. 197 863–197 874, 2020

    work page 2020

  8. [8]

    Managing data center cluster as non-wire alternative: A case in balancing market,

    Y . Cao, F. Cao, Y . Wang, J. Wang, L. Wu, and Z. Ding, “Managing data center cluster as non-wire alternative: A case in balancing market,” Applied Energy, vol. 360, p. 122769, 2024

    work page 2024

Show all 51 references
  1. [9]

    Data center participation in demand response programs with quality-of-service guarantees,

    Y . Zhang, I. C. Paschalidis, and A. K. Coskun, “Data center participation in demand response programs with quality-of-service guarantees,” in Proceedings of the Tenth ACM International Conference on Future Energy Systems, 2019, pp. 285–302

    2019

  2. [10]

    Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management,

    Z. Ding, L. Xie, Y . Lu, P. Wang, and S. Xia, “Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management,” IEEE Transactions on Industry Applications, vol. 54, no. 6, pp. 5599–5608, 2018

    2018

  3. [11]

    Energy-aware virtual machine allocation in dvfs-enabled cloud data centers,

    J. Masoudi, B. Barzegar, and H. Motameni, “Energy-aware virtual machine allocation in dvfs-enabled cloud data centers,” IEEE Access , vol. 10, pp. 3617–3630, 2021

    2021

  4. [12]

    An energy- aware scheduling algorithm in dvfs-enabled networked data centers,

    M. Shojafar, C. Canali, R. Lancellotti, and S. Abolfazli, “An energy- aware scheduling algorithm in dvfs-enabled networked data centers,” in Special Session on Tools for an Energy Efficient Cloud , vol. 3. SciTePress, 2016, pp. 387–397

    2016

  5. [13]

    Energy and performance aware fog computing: A case of dvfs and green renewable energy,

    A. Toor, S. ul Islam, N. Sohail, A. Akhunzada, J. Boudjadar, H. A. Khattak, I. U. Din, and J. J. Rodrigues, “Energy and performance aware fog computing: A case of dvfs and green renewable energy,” Future Generation Computer Systems , vol. 101, pp. 1112–1121, 2019

    2019

  6. [14]

    Energy-efficient dag scheduling with dvfs for cloud data centers,

    W. Yang, M. Zhao, J. Li, and X. Zhang, “Energy-efficient dag scheduling with dvfs for cloud data centers,” The Journal of Supercomputing , vol. 80, no. 10, pp. 14 799–14 823, 2024

    2024

  7. [15]

    Resource man- agement with deep reinforcement learning,

    H. Mao, M. Alizadeh, I. Menache, and S. Kandula, “Resource man- agement with deep reinforcement learning,” in Proceedings of the 15th ACM workshop on hot topics in networks , 2016, pp. 50–56

    2016

  8. [16]

    An energy aware resource allocation based on combination of cnn and gru for virtual machine selection,

    Z. Khodaverdian, H. Sadr, S. A. Edalatpanah, and M. Nazari, “An energy aware resource allocation based on combination of cnn and gru for virtual machine selection,” Multimedia tools and applications , vol. 83, no. 9, pp. 25 769–25 796, 2024

    2024

  9. [17]

    Energy-aware qos-based dynamic virtual machine consolidation approach based on rl and ann,

    M. Rezakhani, N. Sarrafzadeh-Ghadimi, R. Entezari-Maleki, L. Sousa, and A. Movaghar, “Energy-aware qos-based dynamic virtual machine consolidation approach based on rl and ann,”Cluster Computing, vol. 27, no. 1, pp. 827–843, 2024

    2024

  10. [18]

    Virtual machine migration techniques for optimizing energy consumption in cloud data centers,

    Z. Ma, D. Ma, M. Lv, and Y . Liu, “Virtual machine migration techniques for optimizing energy consumption in cloud data centers,” IEEE Access, 2023

    2023

  11. [19]

    Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,

    A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future generation computer systems , vol. 28, no. 5, pp. 755–768, 2012

    2012

  12. [20]

    A survey of network isolation solutions for multi-tenant data centers,

    V . Del Piccolo, A. Amamou, K. Haddadou, and G. Pujolle, “A survey of network isolation solutions for multi-tenant data centers,” IEEE Communications Surveys & Tutorials , vol. 18, no. 4, pp. 2787–2821, 2016

    2016

  13. [21]

    Data center access control,

    G. Security, “Data center access control,” https://security.gallagher.com/ en/Blog/data-center-access-control, 2025, accessed: 2025-03-23

    2025

  14. [22]

    United states data center energy usage report,

    A. Shehabi, S. Smith, D. Sartor, R. Brown, M. Herrlin, J. Koomey, E. Masanet, N. Horner, I. Azevedo, and W. Lintner, “United states data center energy usage report,” 2016

    2016

  15. [23]

    Carbon- aware computing for datacenters,

    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, vol. 38, no. 2, pp. 1270–1280, 2022

    2022

  16. [24]

    {Prediction-Based} power oversubscription in cloud platforms,

    A. G. Kumbhare, R. Azimi, I. Manousakis, A. Bonde, F. Frujeri, N. Mahalingam, P. A. Misra, S. A. Javadi, B. Schroeder, M. Fontoura et al., “{Prediction-Based} power oversubscription in cloud platforms,” in 2021 USENIX Annual Technical Conference (USENIX ATC 21), 2021, pp. 473–487

    2021

  17. [25]

    Precise power capping for latency-sensitive applications in datacenter,

    S. Wu, Y . Chen, X. Wang, H. Jin, F. Liu, H. Chen, and C. Yan, “Precise power capping for latency-sensitive applications in datacenter,” IEEE Transactions on Sustainable Computing , vol. 6, no. 3, pp. 469–480, 2018

    2018

  18. [26]

    Dynamic application-aware power capping,

    B. Wang, D. Schmidl, C. Terboven, and M. S. M ¨uller, “Dynamic application-aware power capping,” in Proceedings of the 5th Interna- tional Workshop on Energy Efficient Supercomputing , 2017, pp. 1–8

    2017

  19. [27]

    Understanding the impact of dynamic power capping on appli- cation progress,

    S. Ramesh, S. Perarnau, S. Bhalachandra, A. D. Malony, and P. Beck- man, “Understanding the impact of dynamic power capping on appli- cation progress,” in 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS) . IEEE, 2019, pp. 793–804

    2019

  20. [28]

    Power capping: What works, what does not,

    P. Petoumenos, L. Mukhanov, Z. Wang, H. Leather, and D. S. Nikolopou- los, “Power capping: What works, what does not,” in 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2015, pp. 525–534

    2015

  21. [29]

    Classified power capping by network distribution trees for green computing,

    Z. Wu, C. Giles, and J. Wang, “Classified power capping by network distribution trees for green computing,” Cluster computing, vol. 16, pp. 17–26, 2013

    2013

  22. [30]

    Power modeling for effective datacenter planning and compute management,

    A. Radovanovic, B. Chen, S. Talukdar, B. Roy, A. Duarte, and M. Shah- bazi, “Power modeling for effective datacenter planning and compute management,” IEEE Transactions on Smart Grid , vol. 13, no. 2, pp. 1611–1621, 2021

    2021

  23. [31]

    Ai-driven job scheduling in cloud computing: a comprehensive review,

    Y . Sanjalawe, S. Al-E’mari, S. Fraihat, and S. Makhadmeh, “Ai-driven job scheduling in cloud computing: a comprehensive review,” Artificial Intelligence Review, vol. 58, no. 7, p. 197, 2025

    2025

  24. [32]

    A bdi agent-based task scheduling framework for cloud computing,

    Y . Yang, F. Ren, and M. Zhang, “A bdi agent-based task scheduling framework for cloud computing,” arXiv preprint arXiv:2401.02223 , 2024

    2024 arXiv

  25. [33]

    Deep reinforcement learning: An overview,

    Y . Li, “Deep reinforcement learning: An overview,” arXiv preprint arXiv:1701.07274, 2017

    2017 arXiv

  26. [34]

    Brief survey of model-based reinforcement learning techniques,

    C.-V . Pal and F. Leon, “Brief survey of model-based reinforcement learning techniques,” in 2020 24th International Conference on System Theory, Control and Computing (ICSTCC) . IEEE, 2020, pp. 92–97

    2020

  27. [35]

    Model-based reinforcement learning with kernels for resource allocation in ran slices,

    J. J. Alcaraz, F. Losilla, A. Zanella, and M. Zorzi, “Model-based reinforcement learning with kernels for resource allocation in ran slices,” IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 486– 501, 2022

    2022

  28. [36]

    Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters,

    W. Deng, F. Liu, H. Jin, X. Liao, and H. Liu, “Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters,” International Journal of Communication Systems , vol. 27, no. 4, pp. 623–642, 2014

    2014

  29. [37]

    Implicit quantile networks for distributional reinforcement learning,

    W. Dabney, G. Ostrovski, D. Silver, and R. Munos, “Implicit quantile networks for distributional reinforcement learning,” in International conference on machine learning . PMLR, 2018, pp. 1096–1105

    2018

  30. [38]

    Load balancing on cloud analyst using first come first serve scheduling algorithm,

    F. Saeed, N. Javaid, M. Zubair, M. Ismail, M. Zakria, M. H. Ashraf, and M. B. Kamal, “Load balancing on cloud analyst using first come first serve scheduling algorithm,” in Advances in Intelligent Networking and Collaborative Systems: The 10th International Conference on Intel...

    2018

  31. [39]

    Fair scheduling with tunable latency: a round-robin approach,

    H. M. Chaskar and U. Madhow, “Fair scheduling with tunable latency: a round-robin approach,” IEEE/ACM Transactions on networking, vol. 11, no. 4, pp. 592–601, 2003

    2003

  32. [40]

    J. A. Stankovic, M. Spuri, K. Ramamritham, and G. Buttazzo, Deadline scheduling for real-time systems: EDF and related algorithms. Springer Science & Business Media, 1998, vol. 460

    1998

  33. [41]

    Trace 201708,

    Alibaba Cluster Trace, “Trace 201708,” https://github.com/alibaba/ clusterdata/blob/master/cluster-trace-v2017/trace 201708.md, 2017, ac- cessed: 2024-01-23

    2017

  34. [42]

    Power consumption estimation models for processors, virtual machines, and servers,

    C. M ¨obius, W. Dargie, and A. Schill, “Power consumption estimation models for processors, virtual machines, and servers,”IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 6, pp. 1600–1614, 2013

    2013

  35. [43]

    Markets and operations,

    PJM Interconnection, “Markets and operations,” https://www.pjm.com/ markets-and-operations, 2025, accessed: 2025-03-2

    2025

  36. [44]

    Sla-driven dynamic resource management for multi-tier web applications in a cloud,

    W. Iqbal, M. N. Dailey, and D. Carrera, “Sla-driven dynamic resource management for multi-tier web applications in a cloud,” in 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Com- puting. IEEE, 2010, pp. 832–837

    2010

  37. [45]

    Analyzing alibaba’s co-located datacenter workloads,

    Y . Cheng, A. Anwar, and X. Duan, “Analyzing alibaba’s co-located datacenter workloads,” in 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 2018, pp. 292–297

    2018

  38. [46]

    Learning latent dynamics for planning from pixels,

    D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International conference on machine learning. PMLR, 2019, pp. 2555– 2565

    2019

  39. [47]

    Value-aware loss function for model learning in reinforcement learning,

    A.-m. Farahmand, A. M. Barreto, and D. N. Nikovski, “Value-aware loss function for model learning in reinforcement learning,” in 13th European Workshop on Reinforcement Learning (EWRL), vol. 1, no. 2, 2016, p. 36

    2016

  40. [48]

    Service level agreements and security sla: A comprehensive survey,

    S. Nicolazzo, A. Nocera, and W. Pedrycz, “Service level agreements and security sla: A comprehensive survey,”arXiv preprint arXiv:2405.00009, 2024

    2024

  41. [49]

    What is an sla? best practices for service-level agreements,

    S. Overby, L. Greiner, and L. G. Paul, “What is an sla? best practices for service-level agreements,” CIO Mag, 2017

    2017

  42. [50]

    Iterative value-aware model learning,

    A.-m. Farahmand, “Iterative value-aware model learning,” Advances in Neural Information Processing Systems , vol. 31, 2018. 13 Yimeng Sun (Graduate Student Member, IEEE) received the B.S. degree in the electrical engineering and automation in 2021 from North China Electric Pow...

    2018

  43. [2013]

    Springer, 2013, pp

    Proceedings 19 . Springer, 2013, pp. 47–62

    2013

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.