Characterizing Dynamic Load Balancing in Cloud Environments Using Virtual Machine Deployment Models

Abstract

The ever growing computational demands of users call for efficient cloud resource management to avoid service-level agreement (SLA) violation. Virtualization co-locates multiple virtual machines (VMs) on a single physical server to share the underlying resources for efficient resource management. However, the decision about “what” and “where” to place workloads significantly impacts performance of hosted workloads. Existing cloud schedulers consider a single resource (RAM) to co-locate workloads that as a result lead to SLA violation due to non-optimal VM placement. To handle this issue, current study has updated nova scheduler to propose a multi-resource based VM placement approach to improve application performance in terms of central processing unit (CPU) utilization and execution time. Experimentally we have shown that our proposed method has lessened application execution time by 50% when compared with one of the well-known technique.

Keywords

• Date center
• initial VM deployment
• OpenStack
• scheduling
• virtualization