Context Aware VM Placement Optimization Technique for Heterogeneous IaaS Cloud

Ever increasing demand for cloud adoption is prompting researchers and engineers around the world to make cloud computing more efficient and beneficial for cloud service providers and users. Cloud computing brings profits for all when the cloud infrastructure is used efficiently, and its services are made affordable to businesses of all scales. Managing cloud data center incurs a significant cost, which includes investing in IT infrastructure at the beginning and data center management costs for power, repair, space, and so on at later stages. The power costs are contributing to a significant share in overall data center management costs, and saving in power consumption can help reduce management costs for data center owners. This paper proposes an efficient context-aware adaptive heuristic-based solution for the virtual machine (VM) placement optimization in the heterogeneous cloud data centers. The proposed VM placement technique takes into the account of physical machine characteristics and load (peak and non-peak) conditions in the heterogeneous data centers to save power and also improve performance efficiency for data center owners. The experiments conducted with real cloud workloads and also synthetic workloads against a well-known adaptive heuristic-based technique indicate significant performance improvements and energy saving with our proposed solution.