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We explore a novel theoretical model for studying the performance ofdistributed storage management systems where the data-centers have limitedcapacities (as compared to storage space requested by the users). Prior schemessuch as Balls-into-bins (used for load balancing) neither consider bin(consumer) capacities (multiple balls into a bin) nor the future performance ofthe system after, balls (producer requests) are allocated to bins and restrictnumber of balls as a function of the number of bins. Our problem consists offinding an optimal assignment of the online producer requests to consumers (viaweighted edges) in a complete bipartite graph while ensuring that the totalsize of request assigned on a consumer is limited by its capacity. The metricused to measure the performance in this model is the (minimization of) weightedsum of the requests assigned on the edges (loads) and their correspondingweights. We first explore the optimal offline algorithms followed bycompetitive analysis of different online techniques. Using oblivious adversary.LP and Primal-Dual algorithms are used for calculating the optimal offlinesolution in O(r*n) time (where r and n are the number of requests and consumersrespectively) while randomized algorithms are used for the online case.  For the simplified model with equal consumer capacities an average-casecompetitive ratio of AVG(d) / MIN(d) (where d is the edge weight / distance) isachieved using an algorithm that has equal probability for selecting any of theavailable edges with a running time of $O(r)$. In the extending the model toarbitrary consumer capacities we show an average case competitive ratio ofAVG(d*c) / (AVG(c) *MIN(d)).

Balls into Bins: strict Capacities and Edge Weights