Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Abstract

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.

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

“…As one of the key links of rail transit planning, it directly determines the quality of urban rail transit network planning, which is deeply related to the success or failure of the whole urban rail transit system [ 8 ]. Therefore, it is absolutely necessary to evaluate the urban rail transit network planning scientifically and effectively, and it has profound practical significance [ 9 ]. At present, most of the research work is focused on the resource allocation of complete channel information.…”