Theoretical Analysis for Scale-down-Aware Service Allocation in Cloud Storage Systems

Abstract

Servcie allocation algorithms have been drawing popularity in cloud
computing research community. There has been lots of research on
improvingservice allocation schemes for high utilization, latency reduction
and VM migration enfficient, but few work focus on energy consumption
affected by instance placement in data centers. In this paper we propose an algorithm in which to maximize the number of freed-up machines in data centers, machines that host purely scale-down instances, which are reuiqred to be shut down for energy saving at certain points of time. We intuitively employ a probability partitioning mechanism to schedule services such that the goal of the maximization can be achieved. Furthermore we perform a set of experiments to test the partitioning rules, which show that the proposed algorithms can dynamically increase the number of freed-up machines substantially.

DOI:http://dx.doi.org/10.11591/ijece.v3i1.1792