A decision‐making solution for cloud storage system. (5th September 2018)

• Record Type: Journal Article
• Authors: Gao, Yang; Qi, Heng; Jin, Yingwei; Li, Keqiu
• Other Names: Ricci Laura guestEditor.; Iosup Alexandru guestEditor.; Prodan Radu guestEditor.; Dong Fang guestEditor.; Shen Jun guestEditor.; He Qiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 20(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-05
• Subjects: cloud storage -- decision‐making -- net present value; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4717
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Enterprise IT departments must decide whether to build a private cloud themselves or lease space in a cloud instead. Actually, most enterprise cloud systems are heterogeneous simultaneously consisting partly of private cloud and partly of public cloud. An enterprise needs to know the optimal mixing ratio to achieve the lowest cost. However, the optimal ratio is a dynamic value because the popularity of data changes constantly and the data increases rapidly. In this paper, we formulate maximizing the net present value (NPV) of the investment revenue as a dynamic decision‐making problem and propose a model to simplify the decision‐making problem. We use the K‐means algorithm to cluster the devices into groups. In addition, we also provide a solution with the kNN algorithm for locating data. Furthermore, our system provides a cost‐saving solution that requires no additional devices or investment. Subsequently, as the system grows larger, the decision‐making solution can be employed again to determine how to best extend the cloud system at the lowest cost.