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- W4386228766 abstract "A cloud scheduler packs tasks onto machines with contradictory goals of (1) using the machines as efficiently as possible while (2) avoiding overloading that might result in CPU throttling or out-of-memory errors. We take a stochastic approach that models the uncertainty of tasks' resource requirements by random variables. We focus on a little-explored case of items, each having a Bernoulli distribution that corresponds to tasks that are either idle or need a certain CPU share. RPAP, our online approximation algorithm, upper-bounds a subset of items by Poisson distributions. Unlike existing algorithms for Bernoulli items that prove the approximation ratio only up to a multiplicative constant, we provide a closed-form expression. We derive RPAPC, a combined approach having the same theoretical guarantees as RPAP. In simulations, RPAPC's results are close to FFR, a greedy heuristic with no worst-case guarantees; RPAPC slightly outperforms FFR on datasets with small items." @default.
- W4386228766 created "2023-08-29" @default.
- W4386228766 creator A5023725899 @default.
- W4386228766 creator A5092682768 @default.
- W4386228766 date "2023-08-25" @default.
- W4386228766 modified "2023-10-17" @default.
- W4386228766 title "A Poisson-Based Approximation Algorithm for Stochastic Bin Packing of Bernoulli Items" @default.
- W4386228766 doi "https://doi.org/10.48550/arxiv.2308.13512" @default.
- W4386228766 hasPublicationYear "2023" @default.
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