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Contention Resolution with Heterogeneous Job Sizes
Author(s) -
Michael A. Bender,
Jeremy T. Fineman,
Seth Gilbert
Publication year - 2006
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-38875-3
DOI - 10.1007/11841036_13
Subject(s) - job shop scheduling , computer science , exponential function , upper and lower bounds , binary number , constant (computer programming) , resolution (logic) , simple (philosophy) , channel (broadcasting) , exponential backoff , discrete mathematics , algorithm , combinatorics , mathematics , artificial intelligence , throughput , computer network , arithmetic , mathematical analysis , philosophy , telecommunications , routing (electronic design automation) , epistemology , wireless , programming language
We study the problem of contention resolution for different-sized jobs on a simple channel. When a job makes a run attempt, it learns only whether the attempt succeeded or failed. We first analyze binary exponential backoff, and show that it achieves a makespan of V2Θ(√logn) with high probability, where V is the total work of all n contending jobs. This bound is significantly larger than when jobs are constant sized. A variant of exponential backoff, however, achieves makespan O(V logV) with high probability. Finally, we introduce a new protocol, size-hashed backoff, specifically designed for jobs of multiple sizes that achieves makespan O(V log3logV). The error probability of the first two bounds is polynomially small in n and the latter is polynomially small in logV.

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