XOR Bidding and Knapsack Formulations for HPC Network Resource Allocation

2026-05-29

AI Summary

This work addresses the low bandwidth utilization and high transmission latency in high-performance computing centers caused by static allocation and simplistic queuing. To overcome these limitations, the authors propose a scientific-value-driven dynamic bandwidth allocation mechanism that jointly models network and computational constraints. Users participate in resource allocation through XOR bids specifying their data requirements and associated scientific value. The approach introduces two novel auction mechanisms—the greedy value-density auction and the VCG knapsack auction—balancing practical efficiency with theoretical optimality. Experimental results demonstrate that under high load, the proposed method reduces both average and tail task completion latency by over 80% compared to first-come-first-served scheduling, decreases the coefficient of variation in latency by 75–85%, and lowers the peak-to-mean network load ratio by 60–70%, substantially enhancing system stability and resource utilization efficiency.

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.

Problem

• HPC
• network resource allocation
• bandwidth allocation
• data scheduling
• social welfare

Innovation

• XOR bidding
• knapsack auction
• value-based allocation
• VCG mechanism
• HPC network resource allocation