Sharing Incentives and Fair Division for Multiprocessors

Zahedi, Seyed Majid; Lee, Benjamin C.

doi:10.1109/mm.2015.49

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…Game-theoretic notions of fairness can incentivize strategic users when sharing hardware. 6,12,19,20 Markets and price theory can allocate and manage heterogeneous servers. 8,9,17 Demand response models can handle power emergencies.…”

Section: Resultsmentioning

confidence: 99%

Distributed strategies for computational sprints

2019

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Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips' thermal limits and the rack's power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4-6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

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Section: Resultsmentioning

confidence: 99%

Distributed strategies for computational sprints

2019

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“…Our allocation policy and its analysis easily extend to some canonical settings where there are D divisible resources each with unit supply, and the rate of a job is a function of the resources allocated to it. One canonical rate model is Cobb-Douglas [25], where job j has a substitutability vector α jd , d = [D] with D d=1 α jd = 1. Given allocation x jd in dimension d, the rate of execution is:…”

Section: Weighted Fairness With Only Arrivalsmentioning

confidence: 99%

“…A proportionally fair allocation [13,25] maximizes j y j . It is easy to check that the resulting allocation has a closed form where:…”

Section: Weighted Fairness With Only Arrivalsmentioning

confidence: 99%

Dynamic Weighted Fairness with Minimal Disruptions

Im¹,

Moseley²,

Munagala³

et al. 2020

Preprint

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In this paper, we consider the following dynamic fair allocation problem: Given a sequence of job arrivals and departures, the goal is to maintain an approximately fair allocation of the resource against a target fair allocation policy, while minimizing the total number of disruptions, which is the number of times the allocation of any job is changed. We consider a rich class of fair allocation policies that significantly generalize those considered in previous work.We first consider the models where jobs only arrive, or jobs only depart. We present tight upper and lower bounds for the number of disruptions required to maintain a constant approximate fair allocation every time step. In particular, for the canonical case where jobs have weights and the resource allocation is proportional to the job's weight, we show that maintaining a constant approximate fair allocation requires Θ(log * n) disruptions per job, almost matching the bounds in prior work for the unit weight case. For the more general setting where the allocation policy only decreases the allocation to a job when new jobs arrive, we show that maintaining a constant approximate fair allocation requires Θ(log n) disruptions per job. We then consider the model where jobs can both arrive and depart. We first show strong lower bounds on the number of disruptions required to maintain constant approximate fairness for arbitrary instances. In contrast we then show that there there is an algorithm that can maintain constant approximate fairness with O(1) expected disruptions per job if the weights of the jobs are independent of the jobs arrival and departure order. We finally show how our results can be extended to the setting with multiple resources.

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