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Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization
Author(s) -
Gokulraj S.,
Geetha B.G.
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3771
Subject(s) - cloud computing , computer science , firefly protocol , firefly algorithm , provisioning , resource allocation , database , distributed computing , computer network , operating system , algorithm , particle swarm optimization , biology , zoology
Summary Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation‐based firefly resource cost optimization (PSC‐FRCO) technique is designed. Pearson service correlation‐based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation‐based firefly resource cost optimization technique initially uses Pearson service correlation in which the user‐required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost‐optimized cloud resources to users to afford required service from the cloud server. Thus, PSC‐FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC‐FRCO technique enhances enhances RUE and lessens RT as compared to state‐of‐the‐art works.