An online parallel scheduling method with application to energy-efficiency in cloud computing

Authors:
Wenhong Tian;Qin Xiong;Jun Cao
Affiliations:
School of Computer Science and Software Engineering, University of Electronic Science and Technology of China, ChengDu, China 611731;School of Computer Science and Software Engineering, University of Electronic Science and Technology of China, ChengDu, China 611731;School of Computer Science and Software Engineering, University of Electronic Science and Technology of China, ChengDu, China 611731
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

This paper considers online energy-efficient scheduling of virtual machines (VMs) for Cloud data centers. Each request is associated with a start-time, an end-time, a processing time and a capacity demand from a Physical Machine (PM). The goal is to schedule all of the requests non-preemptively in their start-time-end-time windows, subjecting to PM capacity constraints, such that the total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on multiple machines; it has important applications in power-aware scheduling in cloud computing, optical network design, customer service systems, and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances and unit-size jobs using First-Fit algorithm where g is the total capacity of a machine. In this paper, a $(1+\frac{g-2}{k}-\frac{g-1}{k^{2}})$ -competitive algorithm, Dynamic Bipartition-First-Fit (BFF) is proposed and proved for general case, where k is the ratio of the length of the longest interval over the length of the second longest interval for k1 and g驴2. More results in general and special cases are obtained to improve the best-known bounds.