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Improving virtualization and migration in combinatorial dynamic mapping for cloud services
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DOI：10.1007/s10586-022-03720-1.png)
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Cloud computing is a new approach to provide computing resources from infrastructure to software as a service which have many challenges. One of the most important challenges is pricing and resource allocation issues in the cloud environment. Static pricing schemes do not meet supply and demand, and therefore prompted researchers to look for a dynamic, low-overhead approach that could manage such situations. Usually two views are applied which are based on providing profit and benefit to the user and the provider. In cloud computing, resource availability and workload are dynamically changing, this behavior poses a challenge between users and providers, so resource allocation should be optimized. They are more biased towards providers. In this study, a meta-heuristic method named genetic algorithm is used for win-win allocation of virtual machine to the user of the cloud environment in a double-sided combinatorial auction. The purpose of this study is to provide a method for dynamic mapping of cloud services by combining multi-unit auction algorithm and genetic algorithm that tries to increase the profit of service providers and greater customer welfare, so that it can maximize the profit of service providers by reducing costs and on the other hand increase the level of customer satisfaction by providing some solutions.
Keyword:
GA algorithm
Particle swarm optimization
Virtual machine
Virtual machine positioning
Service providers
GAMultiUnit
期刊
C
IF:
4.1
论文数:
5.1K
被引数:
7.5K
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