Fast load balancing with the most to least loaded policy in dynamic networks

Abstract

Load balancing a distributed/parallel system consists in allocating work (load) to its processors so that they have to process approximately the same amount of work or amounts in relation with their computation power. In this paper, we present a new distributed algorithm that implements the Most to Least Loaded (M2LL) policy. This policy aims at indicating pairs of processors, that will exchange loads, taking into account actually broken edges as well as the current load distribution in the system. The M2LL policy fixes the pairs of neighboring processors by selecting in priority the most loaded and the least loaded processor of each neighborhood. Our first and main result is that the M2LL distributed implementation terminates after at most (n/2)?d t iterations where n and d t are respectively the number of nodes and the degree of the system at time t. We then present a performance comparison between Generalized Adaptive Exchange (GAE) that uses M2LL and Relaxed First Order Scheme (RFOS), two load balancing algorithms for dynamic networks in which only link failures are considered. The comparison is carried out on a dedicated test bed that we have designed and implemented to this end. Our second important result is that although generating more communications, the GAE algorithm with the M2LL policy is faster than RFOS in balancing the system load. In addition, GAE M2LL is able to achieve a more stable balanced state than RFOS and scales well.