Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

Cost-Effective Resource Provisioning for MapReduce in a Cloud

This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. First, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Second, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Third, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura's core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80 percent reduction in the cloud compute infrastructure cost with upto 65 percent reduction in job response times