Optimization of Resource Allocation and Energy Efficiency in Heterogeneous Cloud Data Centers

Abstract

Performance and energy efficiency are major concerns in cloud computing data centers. More often, they carry conflicting requirements making optimization a challenge. Further complications arise when heterogeneous hardware and data center management technologies are combined. For example, heterogeneous hardware such as General Purpose Graphics Processing Units (GPGPUs) improve performance at the cost of greater power consumption while virtualization technologies improve resource management and utilization at the cost of degraded performance. In this paper, we focus on exploiting heterogeneity introduced by GPUs to reduce power budget requirements for servers while maintaining performance. To maintain or improve overall server performance at reduced power budget, we propose two enhancements: (a) We borrow power from co-located multithreaded virtual machines (VMs) and reallocate it to GPU VMs. (b) To compensate multi-threaded VMs and re-boost their performance, we propose to borrow virtual computing resources from GPU VMs and reallocate them to CPU VMs. Combining the two techniques minimizes server power budget while maintaining overall server performance. Our results show that server power budget can be reduced by almost 18% at the average cost of 13% performance degradation per virtual machine. In addition, reallocating virtual resources improves the performance of multi-threaded applications by 30% without affecting GPU applications. Combining both techniques reduces server energy consumption by 47 % with minimum performance degradation.