Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> The dimension of modern distributed systems is growing everyday. This phenomenon has generated several management problems due to the increase in complexity and in the needs of energy. Self-organizing architectures showed to be able to deal with this complexity by making global system features emerge without central control or the need of excessive computational power. Up to now research has been mainly focusing on identifying self-* techniques that operate during the achievement of the regular functional goals of software. Little effort, however, has been put on finding effective methods for energy usage optimization. Our work focuses specifically on this aspect and proposes a bio-inspired self-organization algorithm to redistribute load among servers in data centers. We show, first, how the algorithm redistributes the load, thus allowing a better energy management by turning off servers, and, second, how it may be integrated in a self-organizing architecture. The approach naturally complements existing self-management capabilities of a distributed self-organizing architecture, and provides a solution that is able to work even for very large systems. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. We propose an efficient resource management policy for virtualized Cloud data centers. The objective is to continuously consolidate VMs leveraging live migration and switch off idle nodes to minimize power consumption, while providing required Quality of Service. We present evaluation results showing that dynamic reallocation of VMs brings substantial energy savings, thus justifying further development of the proposed policy. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> Sleeping is an important method to reduce energy consumption in many information and communication systems. In this paper we focus on a typical server under dynamic load, where entering and leaving sleeping mode incurs an energy and a response time penalty. We seek to understand under what kind of system configuration and control method will sleep mode obtain a Pareto Optimal tradeoff between energy saving and average response time. We prove that the optimal "sleeping" policy has a simple hysteretic structure. Simulation results then show that this policy results in significant energy savings, especially for relatively delay insensitive applications and under low traffic load. However, we demonstrate that seeking the maximum energy saving presents another tradeoff: it drives up the peak temperature in the server, with potential reliability consequences. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> An increasing number of large-scale server clusters are being deployed in data centers for supporting many different web-based application services in a seamless fashion. In this scenario, the rising energy costs for keeping up those web clusters is becoming an important concern for many business. In this paper we present an optimization solution for power and performance management in a platform running multiple independent web applications. Our approach assumes a virtualized server environment and includes an optimization model and strategy to dynamically manage the cluster power consumption, while meeting the application's workload demands. <s> BIB004 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions. <s> BIB005 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Placement. <s> In this paper we study the problem of energy-aware resource allocation for hosting long-term services or on-demand computing jobs in clusters, e.g., deployed as part of computing infrastructures. We formalize the problem as three constrained optimization problems: maximize job performance under power consumption constraints, minimize power consumption under job performance constraints, and optimize a linear combination of power consumption and job performance. These problems are NP-hard but, given an instance, a bound on the optimal solution can be computed via a rational linear program. We propose polynomial heuristics for all three problems. Simulation experiments show that in all three cases some heuristics can achieve results close to optimal, i.e., lead to good job performance while conserving energy. <s> BIB006
On the VM placement side, BIB002 presented an architecture for mapping VMs to servers, applying an adapted version of the Best Fit Decreasing heuristic, a family of heuristics designed originally for bin-packing. Solutions given by heuristics can be far from optimal, especially in the presence of heterogeneity. And, a solution using the minimum number of servers is not necessarily the solution requiring less energy. Sorting criteria are also required for the servers to decide which bins are to be filled first: A VM is mapped to the server that shows the least increase in energy consumption. Similarly, BIB006 proposed several means to sort the servers and the VM for the mapping phase, using several sorts of best-fit and first-fit algorithms together with an ad-hoc algorithm derived from vector packing. In BIB001 , the authors use bio-inspired heuristics to find the most energy-efficient hosts, whereas BIB005 propose to maximize revenues in a cloud by turning on and off physical machines. Interestingly, a number of works not dedicated to CMS can easily be adapted. Jobs are handled in a cluster infrastructure, but seeing these as VMs does not change the approach. For instance, BIB003 utilize a Markov decision process in order to find an optimal policy for powering nodes on and off, which makes it possible to find an optimal tradeoff between performance and energy. In BIB004 , the problem of job placement is described as a linear program. They solve it periodically using a control loop. They focus on a heterogeneous cluster enabling DVFS, and they propose a set of constraints for energy reduction while allowing task migration. Similarly BIB006 use linear program modeling, taking into account some SLA for jobs, and they propose vector packing heuristics to solve it. In Hoyer et al. [2010] , statistical allocation planning is proposed through two methods. The first approach allocates pessimistically to each job the maximum resource ratio it might need, developing an allocation directed by vector packing. The optimistic second approach overbooks each node while still guaranteeing to each job a certain performance threshold with dynamic monitoring of VM instances.