NDLI: An Innovative Self-Adaptive Configuration Optimization System in Cloud Computing

Content Provider	IEEE Xplore Digital Library
Author	Jing Jiang Jie Lu Guangquan Zhang
Copyright Year	2011
Abstract	Cloud computing has emerging as an extremely popular and cost-effective computational service model using pay-as-you-go executing environments that scale transparently to the user. However, cloud providers should tackle the challenge of configuring their systems to provide maximal performance while minimizing customer's cost of computing resources, which satisfy the customers' various workload requirements. To solve the above challenge, in this paper, we propose an innovative architecture of self-adaptive configuration optimization system which supports dynamic reconfiguration when workloads change. In addition, we develop an optimization algorithm by using genetic algorithm for this system. By using queuing theory and statistic techniques, we model and compute the SLAs metrics which are defined as the fitness function in the optimization algorithm. This optimization system can guide cloud customers to purchase appropriate resources and make decision of deployment configuration such as scale, scheduling and capacity.
Starting Page	621
Ending Page	627
File Size	499245
Page Count	7
File Format	PDF
ISBN	9781467300063
DOI	10.1109/DASC.2011.112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Cloud computing Analytical models Computational modeling optimization cloud service configuration Predictive models Throughput cloud computing resource scheduling SLAs Optimization
Content Type	Text
Resource Type	Article

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