Abstract
With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.
Highlights
In the past decade, traditional cloud computing technology, which relies heavily on the centralized cloud data center (CDC), has been widely used
We propose an service placement (SP) scheme based on a fuzzy-analytical hierarchical process (FAHP) and attempt to conduct research on the SP scheme for a multi-objective in distributed cloud data centers (DDCs)
We studied the effect of the Number of migrations (NM) on the energy efficiency metric with the following scenario
Summary
Traditional cloud computing technology, which relies heavily on the centralized cloud data center (CDC), has been widely used. Cloud services have been offered in the form of centralized CDCs that can be remote from mobile users. This sometimes entails network delay due to the centralized CDC structure. Significant progress has been made in the deployment of cloud services nearer to users, which provides higher reliability and faster response time. By placing and migrating to the cloud services that have a higher resource near to the mobile users, the distributed cloud offers faster response time, along with higher scalability and reliability than centralized CDCs [1,2,3]
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