Balanced allocation of online balls-into-bins has long been an active area of research for efficient load balancing and hashing applications.There exists a large number of results in this domain for different settings, such as parallel allocations parallel , multi-dimensional allocations multi , weighted balls weight etc. For sequential multi-choice allocation, where @math balls are thrown into @math bins with each ball choosing @math (constant) bins independently uniformly at random, the maximum load of a bin is @math with high probability heavily_load . This offers the current best known allocation scheme. However, for @math , the gap reduces to @math soda08 .A similar constant gap bound has been established for parallel allocations with @math communication rounds lenzen . In this paper we propose a novel multi-choice allocation algorithm, ( @math ) achieving a constant gap with a high probability for the sequential single-dimensional online allocation problem with constant @math . We achieve a maximum load of @math with high probability for constant @math choice scheme with constant number of retries or rounds per ball. We also show that the bound holds even for an arbitrary large number of balls, @math . Further, we generalize this result to (i) the weighted case, where balls have weights drawn from an arbitrary weight distribution with finite variance, (ii) multi-dimensional setting, where balls have @math dimensions with @math randomly and uniformly chosen filled dimension for @math , and (iii) the parallel case, where @math balls arrive and are placed parallely in the bins. We show that the gap in these case is also a constant w.h.p. (independent of @math ) for constant value of @math with expected constant number of retries per ball.
For offline balls-into-bins problem, using maximum flow computations it was shown that the maximum load of a bin w.h.p. is @math . @cite_24 showed that for @math balls, where @math is a sufficiently large constant, a perfect distribution of the balls was possible w.h.p. However, no such similar result is found in the literature for the online sequential case for constant @math choice.
@cite_24
1577307925
We investigate randomized processes underlying load balancing based on the multiple-choice paradigm: m balls have to be placed in n bins, and each ball can be placed into one out of 2 randomly selected bins. The aim is to distribute the balls as evenly as possible among the bins. Previously, it was known that a simple process that places the balls one by one in the least loaded bin can achieve a maximum load of m n + Θ(loglogn) with high probability. Furthermore, it was known that it is possible to achieve (with high probability) a maximum load of at most ⌈m n ⌉ + 1 using maximum flow computations.