The reviewed record of science sign in
Pith

arxiv: 2606.00490 · v1 · pith:AHZIJYNC · submitted 2026-05-30 · cs.NI

XOR Bidding and Knapsack Formulations for HPC Network Resource Allocation

Abrar Hossain , Kishwar Ahmed This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-28 18:27 UTCgrok-4.3pith:AHZIJYNCrecord.jsonopen to challenge →

classification cs.NI
keywords HPC network allocationbandwidth allocationauction mechanismsVCG auctionknapsack formulationXOR biddingsocial welfare maximizationresource allocation
0
0 comments X

The pith

Two auction mechanisms allocate HPC bandwidth using bids on scientific value and outperform FCFS by cutting delays over 80 percent in high-load simulations.

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

The paper develops dynamic auction-based methods for allocating network bandwidth in high-performance computing centers by modeling the problem with network and processing constraints. It introduces user bids that report data requirements and scientific value, then proposes two mechanisms to maximize the total value of completed transfers. A greedy value density auction offers computational efficiency while a VCG knapsack auction supplies strong theoretical guarantees. Simulations under high load show these approaches reduce average and tail delays by more than 80 percent, lower delay variation by 75-85 percent, and cut load volatility by 60-70 percent compared with first-come-first-served queuing. A sympathetic reader would care because better allocation could ease congestion in data-intensive scientific work and shift priority toward higher-value transfers.

Core claim

The paper claims that XOR bidding combined with knapsack formulations enables two auction mechanisms—the Greedy Value Density Auction and the VCG Knapsack Auction—to allocate bandwidth in HPC networks by maximizing the total scientific value of completed transfers, leading to over 80 percent reduction in average and tail completion delays under high-load conditions in simulations.

What carries the argument

The Greedy Value Density Auction and VCG Knapsack Auction, which select bids via XOR bidding and knapsack optimization to maximize social welfare while respecting network and processing constraints.

If this is right

  • Reduces average and tail completion delays by more than 80 percent under high-load conditions.
  • Decreases the coefficient of variation of delay by 75-85 percent.
  • Decreases load volatility measured by peak-to-average ratio by 60-70 percent.
  • Increases predictability and network stability while providing fairer access based on reported scientific value.

Where Pith is reading between the lines

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

  • The bidding model could be tested against real user behavior in live HPC systems to check whether reported values align with measured outcomes.
  • If the approach scales, similar knapsack auctions might apply to other constrained scientific resources such as storage or compute time.
  • The mechanisms might require integration with existing job schedulers, which could alter the observed performance gains.

Load-bearing premise

User bids accurately encode the true scientific value of their data transfers and the simulated network and processing constraints match real HPC workloads.

What would settle it

A real HPC deployment in which the auction mechanisms produce no measurable reduction in average or tail completion delays relative to FCFS or in which submitted bids show no correlation with actual scientific outcomes.

Figures

Figures reproduced from arXiv: 2606.00490 by Abrar Hossain, Kishwar Ahmed.

Figure 1
Figure 1. Figure 1: The scientific data pipeline, highlighting heteroge￾neous sources feeding into a common ingestion interface. • We formalize the HPC ingestion bandwidth allocation problem considering heterogeneous demands and pri￾orities. • We propose two novel, value-aware auction mecha￾nisms: Greedy Value-Density and VCG Knapsack for HPC data ingestion. • We detail the value-driven allocation and payment logic specific t… view at source ↗
Figure 2
Figure 2. Figure 2: Observed data ingestion characteristics at ALCF [13]. 2.2 Limitations of Existing Data Ingestion Strategies Existing tools for transferring data into HPC centers exhibit significant limitations when confronted with the scale and heterogeneity of modern scientific workflows, as evidenced by operational data (Figs. 2, 3, and 4). • SCP/SFTP and Rsync: Ubiquitous tools like SCP/SFTP[9], though secure, are cons… view at source ↗
Figure 3
Figure 3. Figure 3: Analysis of user behavior characteristics from ALCF GridFTP data [13]. Takeaway 3: The observed characteristics of HPC data ingestion (Figs. 2-4) and the limitations of current tools ne￾cessitate adaptive, resource-aware frameworks to optimize performance and utilization at the HPC-edge. 3 Core Concepts We briefly introduce core auction concepts pertinent to our bandwidth allocation mechanisms. Auction Mec… view at source ↗
Figure 4
Figure 4. Figure 4: Detailed characterization of data transfer patterns from ALCF GridFTP logs [13]. The allocation must respect physical limitations. Total allocated bandwidth cannot exceed available capacity, and may be limited by processing capacity 𝑃: ∑︁ 𝐾 𝑘=1 ∑︁ 𝐽𝑘 𝑗=1 𝑟 𝑗 𝑘 𝑥 𝑗 𝑘 ≤ min(𝑊 , 𝑃) (2) If experiment 𝑘’s 𝑗-th bid wins (𝑥 𝑗 𝑘 = 1), it pays 𝑝 𝑗 𝑘 . Its net utility based on true valuation is: 𝑢 𝑗 𝑘 = ( 𝑉 𝑗 𝑘 − 𝑝 … view at source ↗
Figure 5
Figure 5. Figure 5: Overview of Auction Based Bandwidth Allocation Each user 𝑖 submits a bid bid𝑖 = (request_details𝑖 , 𝑏𝑖), where request_details𝑖 includes the user identifier and file size (file_size𝑖 ), and 𝑏𝑖 represents their willingness-to-pay for completing the transfer. The required bandwidth is bw_req𝑖 = file_size𝑖/slot_duration. The auction mechanism selects a winning set 𝑊 such that Í 𝑖∈𝑊 bw_req𝑖 ≤ 𝐵 while optimiz￾i… view at source ↗
Figure 6
Figure 6. Figure 6: Average completion delay (hr) across varying band￾widths and load multipliers. 0 50 125.0 MB/s 0 100 500.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 10 1250.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0.0 2.5 2500.0 MB/s P95 Delay (hr) Greedy VCG FCFS (A) FCFS (C) [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: 95th Percentile (P95) completion delay (hr) across varying bandwidths and load multipliers. 0 100 125.0 MB/s 0 100 500.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 100 1250.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 100 2500.0 MB/s Success Rate (%) Greedy VCG FCFS (A) FCFS (C) [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Transfer success rate (%) across varying band￾widths and load multipliers. lower bandwidth capacities (125 MB/s, 500 MB/s), where the FCFS variants exhibit substantial delays, often exceed￾ing several hours on average and tens of hours for the P95 metric (note the different y-axis scales across facets in these [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: System throughput (GB/day) across varying band￾widths and load multipliers. 0 5000 125.0 MB/s 0 5000 500.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 5000 1250.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 5000 2500.0 MB/s Total Value Greedy VCG FCFS (A) FCFS (C) [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Total value of completed transfers across varying bandwidths and load multipliers. 0.0 2.5 125.0 MB/s 0.0 2.5 500.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0 2 1250.0 MB/s 0.8x 1.0x 1.5x 2.0x Load Multiplier 0.0 0.5 2500.0 MB/s CV of Delay Greedy VCG FCFS (A) FCFS (C) [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Coefficient of Variation (CV) of Delay across varying bandwidths and load multipliers. figures). This indicates that the auction mechanisms’ ability to prioritize transfers based on value (or value density) ef￾fectively mitigates congestion and prevents the system from becoming overwhelmed, leading to much faster completions for the transfers that are admitted. Interestingly, the delay performance differe… view at source ↗
Figure 12
Figure 12. Figure 12: Hours where offered load and consumed load (by each mechanism) exceeded nominal link capacity, across varying bandwidths and load multipliers. achieved by each mechanism across different loads and band￾widths. Complementing this, [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Load Volatility, measured by Peak-to-Average Ratio (PAR) of hourly consumed load, for each mechanism across varying bandwidths and load multipliers. transfers might complete near the average delay, a signifi￾cant portion can experience delays that are drastically dif￾ferent, making the service less predictable under these FCFS schemes. Even as overall system capacity increases (e.g., at 2500 MB/s), where … view at source ↗
read the original abstract

Modern High Performance Computing (HPC) centers face growing challenges in ingesting large and diverse data streams. These issues often create bottlenecks that limit bandwidth utilization and delay scientific progress. Traditional static allocation and simple queuing methods are often insufficient. This paper presents a dynamic, value-based approach to bandwidth allocation. We formalize the problem by incorporating both network and processing constraints. To address it, we introduce two auction-based mechanisms: the Greedy Value Density Auction, which is computationally efficient, and the Vickrey--Clarke--Groves (VCG) Knapsack Auction, which provides strong theoretical guarantees. Both mechanisms rely on user bids that specify data requirements and scientific value. The objective is to maximize the total value of successful transfers, commonly referred to as social welfare. Simulation results demonstrate that the proposed mechanisms significantly outperform First Come First Served (FCFS) baselines. Under high-load conditions, they reduce average and tail completion delays by more than 80%. Predictability also improves, with the coefficient of variation of delay decreasing by 75--85%. Network stability increases as well, with load volatility, measured by the peak-to-average ratio, decreasing by 60--70%. These results indicate that value-driven, adaptive bandwidth allocation can reduce congestion, improve resource utilization, and provide fairer access based on scientific importance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper formalizes bandwidth allocation in HPC centers as a knapsack-like problem with network and processing constraints, proposing two mechanisms: a computationally efficient Greedy Value Density Auction and a VCG Knapsack Auction that uses XOR bids encoding data volume and scientific value to maximize social welfare. Simulations under high-load conditions report >80% reductions in average and tail completion delays, 75-85% lower delay coefficient of variation, and 60-70% lower load volatility relative to FCFS baselines.

Significance. If the simulation results hold under realistic conditions, the work could advance value-based resource allocation in data-intensive HPC environments by providing both efficient heuristics and incentive-compatible mechanisms. However, the absence of external validation against production traces or elicited user valuations limits the strength of the claimed improvements in social welfare and stability.

major comments (2)
  1. [Simulation results] Simulation results section: The headline claim of >80% reduction in average and tail delays (and the associated stability metrics) is obtained exclusively from simulations whose workload generator, bid synthesis procedure, and network/processing constraint models are not described with sufficient detail to allow reproduction or sensitivity analysis; this is load-bearing because the performance gap is attributed to the mechanisms rather than to the choice of synthetic inputs.
  2. [§3] §3 (mechanism definitions): The assumption that user bids accurately encode true scientific value is used without qualification to justify the social-welfare objective, yet no section provides a mapping from real scientific priorities to bid values or tests robustness when bids are strategic or noisy; this directly affects whether the VCG guarantees translate to the claimed welfare gains.
minor comments (2)
  1. [Introduction] The abstract and introduction use 'XOR bidding' in the title but do not explicitly define the XOR semantics or contrast it with additive bids in the mechanism sections.
  2. No table or figure caption supplies the exact parameter settings (e.g., arrival rates, capacity values, bid distributions) used to generate the reported 80% figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major comment below and indicate the revisions we will make to improve reproducibility and clarify assumptions.

read point-by-point responses

  1. Referee: [Simulation results] Simulation results section: The headline claim of >80% reduction in average and tail delays (and the associated stability metrics) is obtained exclusively from simulations whose workload generator, bid synthesis procedure, and network/processing constraint models are not described with sufficient detail to allow reproduction or sensitivity analysis; this is load-bearing because the performance gap is attributed to the mechanisms rather than to the choice of synthetic inputs.

    Authors: We agree that the current description of the simulation setup is insufficient for full reproducibility. In the revised manuscript we will expand the simulation section to provide complete specifications of the workload generator (including arrival processes and data-volume distributions), the exact bid-synthesis procedure (how data volumes and scientific values are sampled and encoded as XOR bids), and the precise network and processing constraint models together with all numerical parameters used in the reported experiments. This will enable independent reproduction and sensitivity analysis. revision: yes

  2. Referee: [§3] §3 (mechanism definitions): The assumption that user bids accurately encode true scientific value is used without qualification to justify the social-welfare objective, yet no section provides a mapping from real scientific priorities to bid values or tests robustness when bids are strategic or noisy; this directly affects whether the VCG guarantees translate to the claimed welfare gains.

    Authors: The mechanisms are defined under the standard mechanism-design assumption that reported bids equal true valuations; the VCG auction therefore maximizes welfare with respect to the reported values and is strategy-proof. The manuscript does not contain an empirical mapping from actual scientific priorities to bid values nor robustness experiments under noisy or strategic bidding, because the focus is on the formal problem formulation and synthetic evaluation. We will add a dedicated discussion subsection that explicitly states this modeling assumption, notes the practical difficulty of valuation elicitation, and outlines future work on robustness to misreported or noisy bids. revision: partial

Circularity Check

0 steps flagged

No circularity; performance claims rest on independent simulation outputs

full rationale

The paper formalizes an allocation problem, proposes two auction mechanisms (Greedy Value Density Auction and VCG Knapsack Auction) that take user bids as inputs to maximize social welfare, and reports simulation results showing improvements over FCFS. No derivation chain, equation, or claim reduces a result to its own inputs by construction, renames a fitted quantity as a prediction, or relies on a load-bearing self-citation. The >80% delay reductions are simulation outputs, not tautological consequences of the model definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that user-submitted value bids are truthful and that the simulation model captures real constraints; no explicit free parameters, axioms, or invented entities are stated in the abstract.

pith-pipeline@v0.9.1-grok · 5761 in / 1124 out tokens · 15338 ms · 2026-06-28T18:27:33.967156+00:00 · methodology

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

23 extracted references · 1 canonical work pages

  1. [1]

    The Petascale DTN Project: High Performance Data Transfer for HPC Facilities,

    E. Dart, W. Allcock, W. Bhimji, T. Boerner, R. Cheema, A. Cherry, B. Draney, S. Habib, D. Hazen, J. Hill, M. Kollross, S. Parete-Koon, D. Pelfrey, A. Pope, J. Porter, and D. Wheeler, “The Petascale DTN Project: High Performance Data Transfer for HPC Facilities, ”arXiv e-print 2105.12880, 2021. XOR Bidding and Knapsack Formulations for HPC Network Resource...

    work page arXiv 2021

  2. [2]

    Globus: Recent Enhancements and Future Plans,

    K. Chard, S. Tuecke, and I. Foster, “Globus: Recent Enhancements and Future Plans, ” inProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale, pp. 24:1–24:8, 2016

    2016

  3. [3]

    CATCH: A Cloud-based Adaptive Data Transfer Service for HPC,

    H. M. Monti, A. R. Butt, and S. S. Vazhkudai, “CATCH: A Cloud-based Adaptive Data Transfer Service for HPC, ” inProceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium (IPDPS), pp. 126–137, 2011

    2011

  4. [4]

    Frontiers in Scientific Workflows: Pervasive Integration with High-Performance Computing,

    B. Enders, L. Ramakrishnan, W. Bhimji, E. Dart, and K. Wu, “Frontiers in Scientific Workflows: Pervasive Integration with High-Performance Computing, ”IEEE Computer, vol. 57, no. 8, pp. 46–55, 2024

    2024

  5. [5]

    Understanding Data Movement Patterns at HPC: A NERSC Case Study,

    A. Giannakou, D. Hazen, B. Enders, L. Ramakrishnan, and N. Wright, “Understanding Data Movement Patterns at HPC: A NERSC Case Study, ” inProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2024 (to appear)

    2024

  6. [6]

    Revisiting I/O Bandwidth-Sharing Strategies for HPC Applications,

    A. Benoit, T. Herault, L. Perotin, Y. Robert, and F. Vivien, “Revisiting I/O Bandwidth-Sharing Strategies for HPC Applications, ”Journal of Parallel and Distributed Computing, vol. 188, p. 104863, 2024

    2024

  7. [7]

    A Workflow Roofline Model for End- to-End Workflow Performance Analysis,

    N. Ding, B. Austin, Y. Liu, N. Mehta, J. P. Blaschke, L. Oliker, H. Ah Nam, N. J. Wright, and S. Williams, “A Workflow Roofline Model for End- to-End Workflow Performance Analysis, ” Tech. Rep. LBNL-2001423, Lawrence Berkeley National Laboratory (LBNL) Technical Report, 2023

    2023

  8. [8]

    Streaming Large-Scale Microscopy Data to a Supercom- puting Facility,

    S. S. Welborn, C. Harris, S. M. Ribet, G. Varnavides, C. Ophus, B. Enders, and P. Ercius, “Streaming Large-Scale Microscopy Data to a Supercom- puting Facility, ”Microscopy and Microanalysis, vol. 31, no. S1, pp. 17–20, 2025

    2025

  9. [9]

    Automatically encapsulating hpc best practices into data transfers,

    P. Z. Kolano, “Automatically encapsulating hpc best practices into data transfers, ” inProceedings of the Second International Workshop on HPC User Support Tools, pp. 1–12, 2015

    2015

  10. [10]

    Rsync for data transfers

    University of Arizona Research Computing, “Rsync for data transfers. ”https://hpcdocs.hpc.arizona.edu/storage_and_transfers/ transfers/rsync/, 2024. Accessed: 2025-05-05

    2024

  11. [11]

    Gridftp: High-performance data transfer pro- tocol

    Grid Community Forum, “Gridftp: High-performance data transfer pro- tocol. ”https://gridcf.org/gct-docs/latest/gridftp/key/index.html, 2024. Accessed: 2025-05-05

    2024

  12. [12]

    Ibm aspera: High-speed data transfer software

    IBM Corporation, “Ibm aspera: High-speed data transfer software. ” https://www.ibm.com/products/aspera, 2024. Accessed: 2025-05-05

    2024

  13. [13]

    Gridftp usage and performance at alcf

    A. L. C. Facility, “Gridftp usage and performance at alcf. ”https://reports. alcf.anl.gov/data/GRIDFTP.html, 2024. Accessed: 2025-05-05

    2024

  14. [14]

    Big Data Transfer Optimiza- tion Through Adaptive Parameter Tuning,

    E. Arslan, B. A. Pehlivan, and T. Kosar, “Big Data Transfer Optimiza- tion Through Adaptive Parameter Tuning, ”Journal of Parallel and Distributed Computing, vol. 120, pp. 89–100, 2018

    2018

  15. [15]

    Coordinating an operational data distribution network for cmip6 data,

    R. Petrie, S. Denvil, S. Ames, G. Levavasseur, S. Fiore, C. Allen, F. An- tonio, K. Berger, P.-A. Bretonnière, L. Cinquini,et al., “Coordinating an operational data distribution network for cmip6 data, ”Geoscientific Model Development Discussions, vol. 2020, pp. 1–22, 2020

    2020

  16. [16]

    Krishna,Auction theory

    V. Krishna,Auction theory. Academic press, 2009

    2009

  17. [17]

    Vickrey-clarke- groves (vcg) auctions,

    M. Caminati, M. Kerber, C. Lange, and C. Rowat, “Vickrey-clarke- groves (vcg) auctions, ”College Inf. Sci. Technol., Pennsylvania State Univ., State College, PA, USA, 2015

    2015

  18. [18]

    Cs6840: Algo- rithmic game theory – december 4 lecture

    Cornell University Department of Computer Science, “Cs6840: Algo- rithmic game theory – december 4 lecture. ”https://www.cs.cornell.edu/ courses/cs6840/2024fa/lectures/December04, 2024. Accessed: 2025-05- 05

    2024

  19. [19]

    Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales,

    I. Foster, M. Ainsworth, and et al., “Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales, ” inProceed- ings of the 23rd International Conference on Parallel and Distributed Computing (Euro-Par), pp. 3–19, 2017

    2017

  20. [20]

    Resilient Error- Bounded Lossy Compressor for Data Transfer,

    S. Li, S. Di, K. Zhao, X. Liang, Z. Chen, and F. Cappello, “Resilient Error- Bounded Lossy Compressor for Data Transfer, ” inProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 32:1–32:14, 2021

    2021

  21. [21]

    Gladier: Automation toolkit for scientific workflows

    G. G. Team, “Gladier: Automation toolkit for scientific workflows. ” https://github.com/globus-gladier/gladier, 2024. Accessed: 2025-05-05

    2024

  22. [22]

    Cross-facility science with the superfacility project at lbnl,

    B. Enders, D. Bard, C. Snavely, L. Gerhardt, J. Lee, B. Totzke, K. Antypas, S. Byna, R. Cheema, S. Cholia,et al., “Cross-facility science with the superfacility project at lbnl, ” in2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP), pp. 1–7, IEEE, 2020

    2020

  23. [23]

    Value- Based Scheduling for Oversubscribed Power-Constrained Homoge- neous HPC Systems,

    N. Kumbhare, H. Casanova, M. K. Nilsen, and H. J. Siegel, “Value- Based Scheduling for Oversubscribed Power-Constrained Homoge- neous HPC Systems, ”Proceedings of the IEEE International Conference on Cloud and Autonomic Computing (ICCAC), pp. 120–130, 2017

    2017