vGreen: A System for Energy-Efficient Management of Virtual Machines
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Optimal resource provisioning for cloud computing environment
The Journal of Supercomputing
Dynamic Rightsizing with Quality-Controlled Algorithms in Virtualization Environments
International Journal of Grid and High Performance Computing
Self-adaptive workload classification and forecasting for proactive resource provisioning
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
VM consolidation: A real case based on OpenStack Cloud
Future Generation Computer Systems
Multi-Layer Resource Management in Cloud Computing
Journal of Network and Systems Management
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A cloud can be defined as a pool of computer resources that can host a variety of different workloads, ranging from long-running scientific jobs (e.g., modeling and simulation) to transactional work (e.g., web applications). A cloud computing platform dynamically provisions, configures, reconfigures, and de-provisions servers as needed. Servers in the cloud can be physical machines or virtual machines. Cloud-hosting facilities, including many large businesses that run clouds in-house, became more common as businesses tend to out-source their computing needs more and more. For large-scale clouds power consumption is a major cost factor. Modern computing devices have the ability to run at various frequencies each one with a different power consumption level. Hence, the possibility exists to choose frequencies at which applications run to optimize total power consumption while staying within the constraints of the Service Level Agreements (SLA) that govern the applications. In this paper, we analyze the mathematical relationship of these SLAs and the number of servers that should be used and at what frequencies they should be running. We discuss a proactive provisioning model that includes hardware failures, devices available for services, and devices available for change management, all as a function of time and within constraints of SLAs. We provide scenarios that illustrate the mathematical relationships for a sample cloud and that provides a range of possible power consumption savings for different environments.