SIGMETRICS 2025

Meta Info

Homepage: https://www.sigmetrics.org/sigmetrics2025/

Paper List

• https://www.sigmetrics.org/sigmetrics2025/program.html

• https://dl.acm.org/doi/proceedings/10.1145/3726854

Papers

Scheduling

• Improving Multiresource Job Scheduling with Markovian Service Rate Policies [Paper]

  • UNC & NEU

  • MSR: Markovian Service Rate

• Robust Gittins for Stochastic Scheduling [Paper]

  • CMU & Pittsburgh & Microsoft

  • How to design a robust scheduling policy to produce nearly optimal schedules even if there are modest discrepancies between the predicted distributions and the underlying real distributions?

  • The standard Gittins index policy is not robust in this sense.

• A Gittins Policy for Optimizing Tail Latency [Paper]

  • Cornell

  • A new scheduling policy to achieve strong tail optimality in the light-tailed M/G/1 with unknown job sizes.

  • The optimal policy turns out to be a variant of the Gittins policy.

Resource Allocation

• Online Allocation with Multi-Class Arrivals: Group Fairness vs Individual Welfare [Paper]

  • Alberta & Waterloo

• Allocating Public Goods via Dynamic Max-Min Fairness: Long-Run Behavior and Competitive Equilibria [Paper]

  • Cornell

• Tight Bounds for Dynamic Bin Packing with Predictions [Paper]

  • Northwestern & NTU

  • MinUsageTime Dynamic Bin Packing (DBP)

• Online Fair Allocation of Reusable Resources [Paper]

  • UMass Amherst

• Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints [Paper]

  • UMass Amherst & Caltech & Waterloo & MIT

  • Spatiotemporal Online Allocation with Deadline Constraints (SOAD)

Serverless Computing

• Exploring Function Granularity for Serverless Machine Learning Application with GPU Sharing [Paper]

  • NCSU & UCSC

  • Adaptive granularity → Improvement in SLO hit rates & reduction in resource coasts

• PipeCo: Pipelining Cold Start of Deep Learning Inference Services on Serverless Platforms [Paper]

  • SJTU

  • Three techniques

    • Divide each inference services into multiple slices & prewarm slices in a sequential and overlapping manner.

    • Estimate periodic patterns in requests and idle containers for scheduling slices.

    • Incorporate a similarity-based container matcher for the reuse of idle containers.

  • Implemented upon OpenFaaS

Autoscaling

• Exploiting Kubernetes Autoscaling for Economic Denial of Sustainability [Paper]

  • Boston University & UMD