Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology |
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| Affiliation: | 1. Applied Innovation Center for Advanced Analytics, Desert Research Institute, USA;2. Department of Computer Science and Engineering, Mississippi State University, USA;3. Department of Electrical and Computer Engineering, Mississippi State University, USA;1. Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226 031, UP, India;2. Academy of Scientific and Innovative Research (AcSIR), Chennai, India;3. Natural Products Chemistry Division, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India;4. Electron Microscopy Unit, Sophisticated and Analytical Instruments Facility, CSIR-Central Drug Research Institute, Lucknow 226 031, UP, India;1. Department of Pediatric Surgery, Ankara Bilkent City Hospital, Children Hospital, Ankara, Turkey;2. Department of Pediatric Surgery, School of Medicine, Kirikkale University, Kirikkale, Turkey;3. Department of Pathology, School of Medicine, Kirikkale University, Kirikkale, Turkey;4. Faculty of Veterinary, Department of Anatomy, Kirikkale University, Kirikkale, Turkey;5. Department of Histology and Embryology, School of Medicine, Istinye University, ?stanbul, Turkey;6. Faculty of Veterinary, Department of Histology, Kirikkale University, Kirikkale, Turkey;7. Department of Pediatric Surgery, School of Medicine, Ankara Y?ld?r?m Beyaz?t University, Ankara, Turkey;1. Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA;2. Mass Spectrometry and Proteomics Facility, Department of Biological Chemistry, Johns Hopkins School of Medicine, 733 N. Broadway Street, Baltimore, MD 21205, USA;3. Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA;2. Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada;3. Joint Department of Medical Imaging UHN, MSH and WCH, Toronto, Ontario, Canada |
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| Abstract: | Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach. |
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| Keywords: | Decomposition–coordination Interaction balance Autonomic computing Cloud broker Cloud computing |
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