Sketch-based data placement among geo-distributed datacenters for cloud storages

With the increasing demand of big data applications, a variety of problems on how to operate the supporting infrastructures more intelligently and efficiently have attracted much attention in the literature. To optimize the data placement among distributed network locations is one of the fundamental problems, which aims at facilitating the data storage and access. However, traditional schemes meet challenges on the running time and the overhead introduced due to the increasing scale of datasets. Therefore, we propose a novel data placement scheme based on sketches to overcome these challenges. We first justify the effectiveness of applying the hypergraph sparsification on the data placement problem, and then present the method of constructing sparsifiers through the sketches of request traffic. Besides, the scheme features on the support of aggregating distributed sketches to make the decision and capturing the pattern of recent traffic through sliding windows. Finally, we obtain numerical results through simulations which confirm that the proposed scheme can place data effectively while reducing the introduced overhead in terms of algorithm running time, space and network traffic.