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A decision‐making solution for cloud storage system
Author(s) -
Gao Yang,
Qi Heng,
Jin Yingwei,
Li Keqiu
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4717
Subject(s) - cloud computing , lease , computer science , revenue , investment (military) , operations research , mathematical optimization , popularity , engineering , business , mathematics , operating system , finance , psychology , social psychology , politics , political science , law
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.