
Asymptotic response time analysis for multitask parallel jobs
The response time of jobs with multiple parallel tasks is a critical per...
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Dynamic Weighted Fairness with Minimal Disruptions
In this paper, we consider the following dynamic fair allocation problem...
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Optimal Resource Allocation for Elastic and Inelastic Jobs
Modern data centers are tasked with processing heterogeneous workloads c...
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When Two is Worse Than One
This note is concerned with the impact on job latency of splitting a tok...
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DISPATCH: An Optimal Algorithm for Online Perfect Bipartite Matching with i.i.d. Arrivals
This work presents the first algorithm for the problem of weighted onlin...
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DISPATCH: An OptimallyCompetitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals
This work presents the first algorithm for the problem of weighted onlin...
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Derandomized Load Balancing using Random Walks on Expander Graphs
In a computing center with a huge amount of machines, when a job arrives...
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NonParametric Stochastic Sequential Assignment With Random Arrival Times
We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decisionmaker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that they may only accept at most n jobs over some reference time period. The decisionmaker only has access to M independent realisations of the job arrival process. We propose an algorithm, NonParametric Sequential Allocation (NPSA), for solving this problem. Moreover, we prove that the expected reward returned by the NPSA algorithm converges in probability to optimality as M grows large. We demonstrate the effectiveness of the algorithm empirically on synthetic data and on public frauddetection datasets, from where the motivation for this work is derived.
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