Genetic Based Data Placement for Geo-Distributed Data-Intensive Applications in Cloud Computing

Running data-intensive applications across the geo-distributed data centers in cloud computing needs to address the problem of how to place the data items to the appropriate data centers. The general methods are mainly hash-based which could be understood as random placement intuitively when the query needs distributed data items. In this paper, We propose an genetic based data placement (GBDP) scheme in which a tripartite graph based model is constructed to formulate the data replica placement problem by leveraging the genetic algorithm, and decompose the original problem into two simplified subproblems, which are solved alternately. Through extensive experiments with synthesized and realistic data items, the performance of the proposed scheme is proved validated.