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Energy management has become increasingly necessary in large-scale cloud data centers to address high operational costs and carbon footprints to the environment. In this work, we combine three management techniques that can be used to control cloud data centers in an energy-efficient manner: changing the number of virtual machines, the number of cores, and scaling the CPU frequencies. We present a feedback controller that determines an optimal configuration to minimize energy consumption while meeting performance objectives. The controller can be configured to accomplish these goals in a stable manner, without causing large oscillations in the resource allocations. To meet the needs of individual applications under different workload conditions, the controller parameters are automatically adjusted at runtime based on a system model that is learned online. The potential of the proposed approach is evaluated in a video encoding scenario. The results show that our combined approach achieves up to 34% energy savings compared to the constituent approaches—core change, virtual machine change, and CPU frequency change policies, while meeting the performance target.

We present an autonomic resource management framework that takes advantage of both virtual machine resizing (CPU and memory) and physical CPU frequency scaling to reduce the power consumption of servers while meeting performance requirements of colocated applications. We design online performance and power model estimators that capture the complex relationships between applications' performance and server power (respectively), and resource utilization. Based on these models, we devise two optimization strategies to determine the most power efficient configuration. We also show that an operator can tune the tradeoff between power and performance. Our evaluation using a set of cloud benchmarks compares the proposed solution in power savings against the Linux ondemand and performance CPU governors. The results show that our solution achieves power savings between 12% to 20% compared to the baseline performance governor, while still meeting applications' performance goals.

Energy management has become increasingly necessary in data centers to address all energy-related costs, including capital costs, operating expenses, and environmental impacts. Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field-Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy-aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data-intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance-energy ratio on a mix of multimedia and e-commerce applications.