Online algorithms for uploading deferrable big data to the cloud

Linquan Zhang, Zongpeng Li, Chuan Wu, Minghua Chen

Abstract: This work studies how to minimize the bandwidth cost for uploading deferral big data to a cloud computing platform, for processing by a MapReduce framework, assuming the Internet service provider (ISP) adopts the MAX contract pricing scheme. We first analyze the single ISP case and then generalize to the MapReduce framework over a cloud platform. In the former, we design a Heuristic Smoothing algorithm whose worst-case competitive ratio is proved to fall between 2−1/(D+1) and 2(1 − 1/e), where D is the maximum tolerable delay. In the latter, we employ the Heuristic Smoothing algorithm as a building block, and design an efficient distributed randomized online algorithm, achieving a constant expected competitive ratio. The Heuristic Smoothing algorithm is shown to outperform the best known algorithm in the literature through both theoretical analysis and empirical studies. The efficacy of the randomized online algorithm is also verified through simulation studies.