CCRP: Customized cooperative resource provisioning for high resource utilization in clouds

Abstract

In cloud systems, efficient resource provisioning is needed to maximize the resource utilization while reducing the Service Level Objective (SLO) violation rate, which is important to cloud providers for high profit. Several methods have been proposed to provide efficient provisioning. However, the previous methods do not consider leveraging the complementary of jobs' requirements on different resource types and job size concurrently to increase the resource utilization. Also, by simply packing complementary jobs without considering job size in the job packing, it can decrease the resource utilization. Therefore, in this paper, we consider both jobs' demands on different resource types (in the spatial space) and jobs' execution time (in the temporal space); we pack the complementary jobs (whose demands on multiple resource types are complementary to each other) belonging to the same type and assign them to a Virtual Machine (VM) to increase the resource utilization. Moreover, the previous methods do not provide efficient resource allocation for heterogeneous jobs in current cloud systems and do not offer different SLO degrees for different job types to achieve higher resource utilization and lower SLO violation rate. Therefore, we propose a Customized Cooperative Resource Provisioning (CCRP) scheme for the heterogeneous jobs in clouds. CCRP uses the hybrid resource allocation and provides SLO availability customization for different job types. To test the performance of CCRP, we compared CCRP with existing methods under various scenarios. Extensive experimental results based on a real cluster and Amazon EC2 show that CCRP achieves 50% higher or more resource utilization and 50% lower or less SLO violation rate compared to the previous resource provisioning strategies.