SIAM Journal on Computing
Balanced allocations: the heavily loaded case
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
How asymmetry helps load balancing
Journal of the ACM (JACM)
Multiple choice tries and distributed hash tables
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Balls and bins with structure: balanced allocations on hypergraphs
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Cassandra: a decentralized structured storage system
ACM SIGOPS Operating Systems Review
The (1 + β)-choice process and weighted balls-into-bins
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
Sparrow: distributed, low latency scheduling
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
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In the multiple choice balls into bins problem, each ball is placed into the least loaded one out of d bins chosen independently and uniformly at random (i.u.r.}). It is known that the maximum load after n balls are placed into n bins is ln ln n/ln d + O(1). In this paper, we consider a variation of the standard multiple choice process. For kd, we place k balls at a time into k least loaded bins among d possible locations chosen i.u.r. We provide the maximum load in terms of k, d and n. The maximum load in the standard multiple choice problem can be derived from our general formulation as a special case with k=1. More interestingly, our result indicates that, for any d ≤ (ln n)Θ(1) and k d, the maximum load is still O(ln ln n). Our allocation scheme can be employed as optimal file replication and data partition policies in distributed file systems and databases. When a new file is created, k copies/fragments of the file are stored into k least loaded among d randomly chosen servers, where k is a tunable parameter that may depend on the level of load balance, file availability, fault tolerance, and popularity or size of a file.