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- W3114611066 abstract "In this paper we study the classical problem of throughput maximization. In this problem we have a collection $J$ of $n$ jobs, each having a release time $r_j$, deadline $d_j$, and processing time $p_j$. They have to be scheduled non-preemptively on $m$ identical parallel machines. The goal is to find a schedule which maximizes the number of jobs scheduled entirely in their $[r_j,d_j]$ window. This problem has been studied extensively (even for the case of $m=1$). Several special cases of the problem remain open. Bar-Noy et al. [STOC1999] presented an algorithm with ratio $1-1/(1+1/m)^m$ for $m$ machines, which approaches $1-1/e$ as $m$ increases. For $m=1$, Chuzhoy-Ostrovsky-Rabani [FOCS2001] presented an algorithm with approximation with ratio $1-frac{1}{e}-varepsilon$ (for any $varepsilon>0$). Recently Im-Li-Moseley [IPCO2017] presented an algorithm with ratio $1-1/e-varepsilon_0$ for some absolute constant $varepsilon_0>0$ for any fixed $m$. They also presented an algorithm with ratio $1-O(sqrt{log m/m})-varepsilon$ for general $m$ which approaches 1 as $m$ grows. The approximability of the problem for $m=O(1)$ remains a major open question. Even for the case of $m=1$ and $c=O(1)$ distinct processing times the problem is open (Sgall [ESA2012]). In this paper we study the case of $m=O(1)$ and show that if there are $c$ distinct processing times, i.e. $p_j$'s come from a set of size $c$, then there is a $(1-varepsilon)$-approximation that runs in time $O(n^{mc^7varepsilon^{-6}}log T)$, where $T$ is the largest deadline. Therefore, for constant $m$ and constant $c$ this yields a PTAS. Our algorithm is based on proving structural properties for a near optimum solution that allows one to use a dynamic programming with pruning." @default.
- W3114611066 created "2021-01-05" @default.
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- W3114611066 date "2020-01-01" @default.
- W3114611066 modified "2023-09-23" @default.
- W3114611066 title "Approximations for Throughput Maximization." @default.
- W3114611066 hasPublicationYear "2020" @default.
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