Cloud Computing: Survey on Energy Efficiency <s> VM Scheduling. <s> Data centers contain heterogeneous sets of machines. Some machines are faster and some - often the same ones - consume more energy and cost more to operate. The data center coordinator must decide how to allocate these machines to multiple applications of potentially many customers, each of which has different requirements. Given a stream of customer requests for machines, how does the data center provider decide which machines to give to whom and when? We propose new algorithms for a cost-aware provider to maximize its profit as it makes admission and scheduling decisions for the customer requests. We show that it matters which machines are assigned to each customer, especially when the data center is undersaturated. (Most data centers are.) Our new algorithms do best when they try to anticipate the ldquoriskinessrdquo of their decisions, that is, the likelihood that even higher-value requests will arrive later. We also show that turning unused machines off, rather than leaving them idle, even using simple heuristics like ldquoturn off a machine as soon as it becomes idle,rdquo can save a lot of money. Finally, we show that having heterogeneity in the data center is, in fact, beneficial. We demonstrate that the same set of customers can be satisfied at a lower cost and a higher profit in a heterogeneous data center rather than in a data center comprised solely of the newest, fastest, machines. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Scheduling. <s> Server virtualization opens up a range of new possibilities for autonomic datacenter management, through the availability of new automation mechanisms that can be exploited to control and monitor tasks running within virtual machines. This offers not only new and more flexible control to the operator using a management console, but also more powerful and flexible autonomic control, through management software that maintains the system in a desired state in the face of changing workload and demand. This paper explores in particular the use of server virtualization technology in the autonomic management of data centers running a heterogeneous mix of workloads. We present a system that manages heterogeneous workloads to their performance goals and demonstrate its effectiveness via real-system experiments and simulation. We also present some of the significant challenges to wider usage of virtual servers in autonomic datacenter management. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Scheduling. <s> Cloud computing offers 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 electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Scheduling. <s> Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm - GreFar - which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware. <s> BIB004
Finally, smart VM scheduling is also a source of energy savings. BIB001 handle the request scheduling in a heterogeneous data center scenario. They focus on the decision of where and when to deploy a customer's job, and when deployed, a job can't move. They employ economic models considering the varying patience of customers, job length, consumed energy, job revenue, cancellation costs, and more. Their conclusion is that even using very simple heuristics (e.g., shutting down a server that has been idle for the past few minutes) can save a significant amount of energy. BIB002 have investigated a similar scenario. BIB003 propose energy-efficiency management of clouds through architectural guidelines, as well as QoS-aware scheduling algorithms and resource allocation policies. They perform simulations on their CloudSim toolkit. The scheduling of applications is also investigated in Berral et al. [2010] and BIB004 .