Multi-objective Optimization Method for Resource Scaling in Cloud Computing
A-Young Son, Seungwan Goh, Eui‐Nam Huh · 2018
With the rapid growth of data centers, thousands of large data centers with lots of computing nodes are established. Development of huge Cloud Data Centers (CDCs) has led to enormous energy consumption. Thus it is necessary for data centers to periodically resource-scaling for VM. For this reason, numerous schemes of resource scaling based on migration method are designed in the CDCs. However, cloud service providers (CSPs) have not yet provided sufficient efficiency to meet user requirements such as energy efficiency, performance and cost. The performance that migration takes depends on multi-metric like the resource utilization, and latency. And most of the previous researches did not consider the multi-metric such as server state transition, its effect to performance and power. In this paper, we proposed resource-scaling method based on migration. We performed resource scaling based on fuzzy system and analyzed effect of metric to maximize energy efficiency. Finally, we demonstrated the proposed method by using different resource scaling strategies.