Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Dynamic migration of virtual machines on a cluster of physical machines is designed to maximize resource utilization by balancing loads across the cluster. When the utilization of a physical machine is beyond a fixed threshold, the machine is deemed overloaded. A virtual machine is then selected within the overloaded physical machine for migration to a lightly loaded physical machine. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. We present in this paper a learning framework that autonomously finds and adjusts thresholds at runtime for different computing requirements. Central to our approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Workload placement on servers has been traditionally driven by mainly performance objectives. In this work, we investigate the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters. The placement component of the application management middleware takes into account the power and migration costs in addition to the performance benefit while placing the application containers on the physical servers. The contribution of this work is two-fold: first, we present multiple ways to capture the cost-aware application placement problem that may be applied to various settings. For each formulation, we provide details on the kind of information required to solve the problems, the model assumptions, and the practicality of the assumptions on real servers. In the second part of our study, we present the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement. We present comprehensive theoretical and experimental evidence to establish the efficacy of pMapper. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> EUCALYPTUS, an open source cloud-computing framework, is still lack of load balancing. In the paper, we provide a kind of implementation by adaptive live migration of virtual machines. We design and implement a simple model which decreases the migration time of virtual machines by shared storage and fulfills the zero-downtime relocation of virtual machines by transforming them as Red Hat cluster services. During the migration process, we also keep the inclusion relationship between VLAN and virtual machines. We propose a distributed load balancing algorithm COMPARE_AND_BALANCE based on sampling to reach an equilibrium solution. The experimental results show that it converges quickly. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware. <s> BIB004 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Clusters provide powerful computing environments, but in practice much of this power goes to waste, due to the static allocation of tasks to nodes, regardless of their changing computational requirements. Dynamic consolidation is an approach that migrates tasks within a cluster as their computational requirements change, both to reduce the number of nodes that need to be active and to eliminate temporary overload situations. Previous dynamic consolidation strategies have relied on task placement heuristics that use only local optimization and typically do not take migration overhead into account. However, heuristics based on only local optimization may miss the globally optimal solution, resulting in unnecessary resource usage, and the overhead for migration may nullify the benefits of consolidation. In this paper, we propose the Entropy resource manager for homogeneous clusters, which performs dynamic consolidation based on constraint programming and takes migration overhead into account. The use of constraint programming allows Entropy to find mappings of tasks to nodes that are better than those found by heuristics based on local optimizations, and that are frequently globally optimal in the number of nodes. Because migration overhead is taken into account, Entropy chooses migrations that can be implemented efficiently, incurring a low performance overhead. <s> BIB005 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered. In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide variety of techniques. We evaluate these techniques with a framework that covers the whole control cycle of a real scenario, using a simulation with representative heterogeneous workloads, and we measure the quality of the results according to a set of metrics focused toward our goals, besides traditional policies. The results obtained indicate that our approach is close to the optimal placement and behaves better when the level of uncertainty increases. <s> BIB006 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Intelligent workload consolidation and dynamic cluster adaptation offer a great opportunity for energy savings in current large-scale clusters. Because of the heterogeneous nature of these environments, scalable, fault-tolerant and distributed consolidation managers are necessary in order to efficiently manage their workload and thus conserve energy and reduce the operating costs. However, most of the consolidation managers available nowadays do not fulfill these requirements. Hence, they are mostly centralized and solely designed to be operated in virtualized environments. In this work, we present the architecture of a novel scalable, fault-tolerant and distributed consolidation manager called Snooze that is able to dynamically consolidate the workload of a software and hardware heterogeneous large-scale cluster composed out of resources using the virtualization and Single System Image (SSI)technologies. Therefore, a common cluster monitoring and management API is introduced, which provides a uniform and transparent access to the features of the underlying platforms. Our architecture is open to support any future technologies and can be easily extended with monitoring metrics and algorithms. Finally, a comprehensive use case study demonstrates the feasibility of our approach to manage the energy consumption of a large-scale cluster. <s> BIB007 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct two application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%. <s> BIB008 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy metric for evaluating the service center greenness level. If the entropy value is above a predefined threshold, the service center is not in a green state. As a consequence, consolidation or dynamic power management actions are selected by means of reinforcement learning and executed to bring back the service center in an energy efficient state. The results are promising showing that the proposed energy aware consolidation algorithm decreases the energy consumption with about 26% from the total energy consumption of a service center. <s> BIB009 </s> Cloud Computing: Survey on Energy Efficiency <s> VM Migration and Consolidation. <s> Cloud computing data centres are emerging as new candidates for replacing traditional data centres. Cloud data centres are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Making the data centre more energy efficient is a necessary task. In this paper, we focus on the organisation’s internal Infrastructure as a Service (IaaS) data centre type. An internal IaaS cloud data centre has many distinguished features with heterogeneous hardware, single application, stable load distribution, lived load migration and highly automated administration. This paper will propose a way of saving energy for IaaS cloud data centre considering all stated constraints. The basic idea is rearranging the allocation in a way that saving energy. The simulation results show the efficiency of the method. <s> BIB010
The third possibility is investigating VM (live) migration combined with physical machine consolidation. BIB008 have studied live migration of VMs in order to model the performance and energy use of migration. They show that migration is an I/O intensive application and that it consumes energy on both ends. The architectural framework proposed in for green clouds also achieves VM reconfiguration, allocation, and reallocation. The authors use a CPU power model to monitor the energy consumption of the cloud. The algorithm they propose to dynamically consolidate VMs significantly reduces the global power consumption of their infrastructure. BIB003 have implemented a distributed load balancing algorithm using live migration for Eucalyptus BIB004 ], an open-source cloud computing platform offering Infrastructure as a Service (IaaS). They do not consider the memory capacity of the servers at all. In OpenNebula, BIB001 propose a machine learning framework that learns from experience when and where to migrate a VM in case of overload. In this approach, all possible migrations must be evaluated, leading to scalability problems for big infrastructures. Designed in the course of the GAMES project, the Green Cloud Scheduler is integrated with OpenNebula. It proactively detects overprovisioned computing resources and identifies the most appropriate adaptation decisions to dynamically adjust them to the incoming workload. It generates adaptation action plans consisting of consolidation actions and hibernating or waking up servers and also uses a learning phase BIB009 . BIB006 make dynamic resource allocation decisions using machine learning. They favor the allocation of new jobs to already powered nodes, using migration if necessary. Entropy BIB005 ] uses constraint programming for the dynamic consolidation of resources in homogeneous clusters. It uses migration and accounts for migration overhead. In the context of the Fit4Green project, BIB010 propose a framework for VM placement over a federation of data centers built using constraint programming and an Entropy system. have developed and evaluated vManage, which places workloads under consideration of power, thermal, and performance aspects using stabilized first-fit and best-fit heuristics. pMapper BIB002 and Snooze BIB007 are other examples for cluster infrastructures. Snooze is based on a hierarchical agent structure that manages the placement and migration of VMs under the control of a centralized decision point. Snooze is extensible and can easily integrate different algorithms.