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Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud services

Koutsandria, G., Skevakis, E., Sayegh, A.A. and Koutsakis, P.ORCID: 0000-0002-4168-0888 (2016) Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud services. Journal of Parallel and Distributed Computing, 96 . pp. 202-217.

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The rapid development of Cloud Computing provides consumers and service providers with a wide range of opportunities and challenges. Considering the substantial infrastructure investments being made by cloud providers, the reduction of operating expenses (OPEX) while maximizing the profit of the provided services is of great importance. One way to achieve this is by maximizing the efficiency of resource utilization. However, profit maximization does not necessarily coincide with the improvement of a user's Quality of Service (QoS); users generating higher profit for the provider may be scheduled first, causing high delays to low-paying users. Further, the contradictory nature of users’ and providers’ needs also extends to the energy consumption problem, as the minimization of service delays could cause cloud resources to be constantly “on”, leading to high energy consumption, high costs for providers and undue environmental impact. The objective of our work is to analyze this multi-dimensional trade-off. We first investigate the problem of efficient resource allocation strategies for time-varying traffic, and propose a new algorithm, MinDelay, which aims at achieving the minimum service delay while taking into account provider's profit. Then, we propose E-MinDelay, an energy-efficient approach for CPU-intensive tasks in cloud systems. Furthermore, we propose an improved version of the Energy Conscious Task Consolidation (ECTC) algorithm, which combines task consolidation and migration techniques with E-MinDelay. Our results demonstrate that energy consumption and service delays corresponding to profit loss can be simultaneously decreased using an efficient scheduling algorithm.

Item Type: Journal Article
Murdoch Affiliation(s): School of Engineering and Information Technology
Publisher: Elsevier
Copyright: © 2016 Elsevier Inc.
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