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An Interaction Balance Based Approach for Autonomic Performance Management in a Cloud Computing Environment

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Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8907))

Abstract

In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation for deploying a set of services over a federated cloud computing infrastructure by considering both, the availability and the demand of the cloud computing resources. This distributed control based approach is developed by using an interaction balance (decomposition-coordination) methodology for interactive bidding of computing resources in cloud computing environment. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for both, the service providers and the cloud brokers. The cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers to sublet the cloud resources that the cloud brokers rent or lease from a number of cloud providers. The developed approach is novel in applying interaction balance methodology, and giving priority to the profit maximization for both the cloud broker and service providers, while assigning the cloud computing resources.

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Acknowledgments

This work was supported in part by the National Science Foundation (NSF) under grant numbers NSF IIP-\(1127978\) and NSF IIP-\(1034897\) at the NSF Center for Cloud and Autonomic Computing, Mississippi State University, and by C-FAR, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA at the University of Virginia, Charlottesville, VA.

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Authors and Affiliations

  1. Department of Computer Science, University of Virginia, Charlottesville, VA, USA

    Rajat Mehrotra

  2. Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA

    Srishti Srivastava & Ioana Banicescu

  3. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

    Sherif Abdelwahed

Authors
  1. Rajat Mehrotra
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  2. Srishti Srivastava
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  3. Ioana Banicescu
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  4. Sherif Abdelwahed
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Corresponding author

Correspondence to Rajat Mehrotra .

Editor information

Editors and Affiliations

  1. Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Sorbonne Universités, Université Pierre et Marie CURIE, Paris, France

    Maria Potop-Butucaru

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Mehrotra, R., Srivastava, S., Banicescu, I., Abdelwahed, S. (2014). An Interaction Balance Based Approach for Autonomic Performance Management in a Cloud Computing Environment. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2014. Lecture Notes in Computer Science(), vol 8907. Springer, Cham. https://doi.org/10.1007/978-3-319-13464-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-13464-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13463-5

  • Online ISBN: 978-3-319-13464-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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