With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21 in terms of energy consumption and in terms of maintaining the SLAs, it performs 60 better than Single Threshold algorithm.
In @cite_16 , the authors propose Single Threshold algorithm which sorts all the VMs in decreasing order of their current utilization and allocates each VM to a physical machine that provides the least increase of power consumption due to this allocation. The algorithm does optimization of the current allocation of VMs by choosing the VMs to migrate based on CPU utilization threshold of a particular physical machine called Single Threshold'. The idea is to place VMs while keeping the total utilization of CPU of the physical machine below this threshold. The reason for limiting CPU usage below the threshold is to avoid SLA violation under a circumstance where there is a sudden increase in CPU utilization of a VM, which could be compensated with the reserve. Single Threshold algorithm works better in terms of energy conservation when compared to Dynamic Round Robin Algorithm discussed in . This algorithm is fairly improved one which takes into consideration of power consumption and CPU usage of physical machines.
@cite_16
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Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd. Many power management strategies have been proposed for enterprise servers based on dynamic voltage and frequency scaling (DVFS), but those solutions cannot further reduce the energy consumption of a server when the server processor is already at the lowest DVFS level and the server utilization is still low (e.g., 5 or lower). To achieve improved energy efficiency, request batching can be conducted to group received requests into batches and put the processor into sleep between the batches. However, it is challenging to perform request batching on a virtualized server because different virtual machines on the same server may have different workload intensities. Hence, putting the shared processor into sleep may severely impact the performance of all the virtual machines. This paper proposes Virtual Batching, a novel request batching solution for virtualized servers with primarily light workloads. Our solution dynamically allocates CPU resources such that all the virtual machines can have approximately the same performance level relative to their allowed peak values. Based on this uniform level, our solution determines the time length for periodically batching incoming requests and putting the processor into sleep. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase processor frequency for performance guarantees. Empirical results based on a hardware testbed and real trace files show that Virtual Batching can achieve the desired performance with more energy conservation than several well-designed baselines, e.g., 63 more, on average, than a solution based on DVFS only. Nowadays, virtualization is present in almost all computing infrastructures. Thanks to server consolidation and VM migration, virtualization helps in power reduction. However, modern powerful computers with higher processor frequency, multiple cores and multiple CPUs constitute the main factor contributing to the continuously increase of energy consumption in numerous computing infrastructures. In this context, energy management takes a critical importance. A hardware technology, called Dynamic Voltage and Frequency Scaling (DVFS), serves to dynamically modify the processor frequency (according to the CPU needs) in order to achieve less energy consumption. However, lowering frequency also generates poor virtual machine (VM) performance. In this paper, we propose a solution consisting of an extended VM scheduler and DVFS, and report some experiments based on this proposal. This enhanced scheduler, according to VM CPU load, dynamically scales processor frequency in order to save energy. The idea is to adapt the current VM scheduler to analyze CPU load, and modify the current processor frequency to the lowest possible, but still support the guaranteed VM performance. The algorithm is designed and simulated on a web server as the sample application and Xen as the virtualization platform. Test results and performance evaluations prove our design and implementation. In the area of system architecture, there are two most significant trends: multi-core and system virtualization technology. Both of them have quite close relationship with energy efficient computing. Industry turns to integrate more cores on a single chip instead of increasing frequency to solve the heat problem. Meanwhile system virtualization could decrease the total power consumption by sharing the same platform among different operating systems. So the necessity of power management on the multi-core virtualization platform has become increasingly evident. However, traditional virtual machine monitor schedulers could not make efficient use of DVFS, and thus could not take it into considering that the guest OSes may run at different frequency. In order to address this problem, this paper designs a power efficient scheduler which uses the load and the power level of the guest OSes as feedback, and implements a prototype based on Xen virtual machine monitor. This scheduler allocates power credit to VCPU of each guest OS, accounts the power consumption of VCPU sat different speed levels and makes scheduler decision by integrating it to the credit oriented to CPU time slice sharing. It also uses utilization of processors as feedback and sets frequency according to the load change trends instead of the simple static relationship between load and frequency policies, so as to decrease the speed steps required by response to burst load change. Experiment results show that the scheduling fairness of guest OS improved when using DVFS as main power saving method, and the power consumption of the whole system can be reduced by 5 percent - 30 percent. Therefore, this framework for feedback scheduling could make efficient use of varieties of power saving methods and maintain ideal balance between overall system power saving and single core over heat. The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) and switching idle nodes off allow Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off. We propose a novel technique for dynamic consolidation of VMs based on adaptive utilization thresholds, which ensures a high level of meeting the Service Level Agreements (SLA). We validate the high efficiency of the proposed technique across different kinds of workloads using workload traces from more than a thousand PlanetLab servers. Virtual machines (VMs) have recently emerged as the basis for allocating resources in enterprise settings and hosting centers. One benefit of VMs in these environments is the ability to multiplex several operating systems on hardware based on dynamically changing system characteristics. However, such multiplexing must often be done while observing per-VM performance guarantees or service level agreements. Thus, one important requirement in this environment is effective performance isolation among VMs. In this paper, we address performance isolation across virtual machines in Xen [1]. For instance, while Xen can allocate fixed shares of CPU among competing VMs, it does not currently account for work done on behalf of individual VMs in device drivers. Thus, the behavior of one VM can negatively impact resources available to other VMs even if appropriate per-VM resource limits are in place. In this paper, we present the design and evaluation of a set of primitives implemented in Xen to address this issue. First, XenMon accurately measures per-VM resource consumption, including work done on behalf of a particular VM in Xen's driver domains. Next, our SEDF-DC scheduler accounts for aggregate VM resource consumption in allocating CPU. Finally, ShareGuard limits the total amount of resources consumed in privileged and driver domains based on administrator-specified limits. Our performance evaluation indicates that our mechanisms effectively enforce performance isolation for a variety of workloads and configurations. Current trends in virtualization, green computing, and cloud computing require ever increasing efficiency in consolidating virtual machines without degrading quality of service. In this work, we consider consolidating virtual machines on the minimum number of physical containers (e.g., hosts or racks) in a cloud where the physical network (e.g., network interface or top of the rack switch link) may become a bottleneck. Since virtual machines do not simultaneously use maximum of their nominal bandwidth, the capacity of the physical container can be multiplexed. We assume that each virtual machine has a probabilistic guarantee on realizing its bandwidth Requirements-as derived from its Service Level Agreement with the cloud provider. Therefore, the problem of consolidating virtual machines on the minimum number of physical containers, while preserving these bandwidth allocation guarantees, can be modeled as a Stochastic Bin Packing (SBP) problem, where each virtual machine's bandwidth demand is treated as a random variable. We consider both offline and online versions of SBP. Under the assumption that the virtual machines' bandwidth consumption obeys normal distribution, we show a 2-approximation algorithm for the offline version and improve the previously reported results by presenting a (2 +∈)-competitive algorithm for the online version. We also observe that a dual polynomial-time approximation scheme (PTAS) for SBP can be obtained via reduction to the two-dimensional vector bin packing problem. Finally, we perform a thorough performance evaluation study using both synthetic and real data to evaluate the behavior of our proposed algorithms, showing their practical applicability. When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption due to leakage current is expected to increase significantly. Then it will be more effective to limit the number of processors employed (i.e., turn some of them off), or to use a combination of DVS and processor shutdown. In this paper, leakage-aware scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results obtained using a public benchmark set of task graphs and real parallel applications show that our approach reduces the total energy consumption by up to 46 for tight deadlines (1.5× the critical path length) and by up to 73 for loose deadlines (8× the critical path length) compared to an approach that only employs DVS. We also compare the energy consumed by our scheduling algorithms to two absolute lower bounds, one for the case where all processors continuously run at the same frequency, and one for the case where the processors can run at different frequencies and these frequencies may change over time. The results show that the energy reduction achieved by our best approach is close to these theoretical limits. Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed). In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed Power consumption is one of the most critical problems in data centers. One effective way to reduce power consumption is to consolidate the hosting workloads and shut down physical machines which become idle after consolidation. Server consolidation is a NP-hard problem. In this paper, a new algorithms Dynamic Round-Robin (DRR), is proposed for energy-aware virtual machine scheduling and consolidation. We compare this strategy with the GREEDY, ROUNDROBIN and POWERSAVE scheduling strategies implemented in the Eucalyptus Cloud system. Our experiment results show that the Dynamic Round-Robin algorithm reduce a significant amount of power consumption compared with the three strategies in Eucalyptus. Energy management for servers is now necessary for technical, financial, and environmental reasons. This paper describes three policies designed to reduce energy consumption in Web servers. The policies employ two power management mechanisms: dynamic voltage scaling (DVS), an existing mechanism, and request batching, a new mechanism introduced in this paper. The first policy uses DVS in isolation, except that we extend recently introduced task-based DVS policies for use in server environments with many concurrent tasks. The second policy uses request batching to conserve energy during periods of low workload intensity. The third policy uses both DVS and request batching mechanisms to reduce processor energy usage over a wide range of workload intensities. All the policies trade off system responsiveness to save energy. However, the policies employ the mechanisms in a feedback-driven control framework in order to conserve energy while maintaining a given quality of service level, as defined by a percentile-level response time. We evaluate the policies using Salsa, a web server simulator that has been extensively validated for both energy and response time against measurements from a commodity web server. Three daylong static web workloads from real web server systems are used to quantify the energy savings: the Nagano Olympics98 web server, a financial services company web site, and a disk intensive web workload. Our results show that when required to maintain a 90th-percentile response time of 50ms, the DVS and request batching policies save from 8.7 to 38 and from 3.1 to 27 respectively of the CPU energy used by the base system. The two polices provide these savings for complementary workload intensities. The combined policy is effective for all three workloads across a broad range of intensities, saving from 17 to 42 of the CPU energy. The ability to cap peak power consumption is a desirable feature in modern data centers for energy budgeting, cost management, and efficient power delivery. Dynamic voltage and frequency scaling (DVFS) is a traditional control knob in the tradeoff between server power and performance. Multi-core processors and the parallel applications that take advantage of them introduce new possibilities for control, wherein workload threads are packed onto a variable number of cores and idle cores enter low-power sleep states. This paper proposes Pack & Cap, a control technique designed to make optimal DVFS and thread packing control decisions in order to maximize performance within a power budget. In order to capture the workload dependence of the performance-power Pareto frontier, a multinomial logistic regression (MLR) classifier is built using a large volume of performance counter, temperature, and power characterization data. When queried during runtime, the classifier is capable of accurately selecting the optimal operating point. We implement and validate this method on a real quad-core system running the PARSEC parallel benchmark suite. When varying the power budget during runtime, Pack & Cap meets power constraints 82 of the time even in the absence of a power measuring device. The addition of thread packing to DVFS as a control knob increases the range of feasible power constraints by an average of 21 when compared to DVFS alone and reduces workload energy consumption by an average of 51.6 compared to existing control techniques that achieve the same power range. Along with the prevailing of mobile devices, the demand for efficient power consumption has become one of the major issues in designing embedded system. Dynamic Voltage Scaling (DVS) is a technique that can reduce energy consumption by changing the processor voltage levels dynamically. Fixed Priority with Preemption Threshold (FPPT) scheduling is a scheduling policy that includes preemptive and non-preemptive aspect of scheduling policy. In this paper, an efficient universal fixed priority DVS algorithm (FPPT-DVS) will be presented. This algorithm has the advantage of both Fixed Priority Preemptive (FPP) DVS scheduling and Fixed Priority Non-Preemptive (FPNP) DVS scheduling. FPPT-DVS algorithm also combines the on-line DVS and off-line DVS together. Experimental results show that the proposed FPPT-DVS algorithm can save up to 20 energy over the existing DVS algorithms. The emergence of Cloud Computing raises the question of dynamically allocating resources of physical (PM) and virtual machines (VM) in an on-demand and autonomic way. Yet, using Cloud Computing infrastructures efficiently requires fulfilling three partially contradicting goals: first, achieving low violation rates of Service Level Agreements (SLA) that define non-functional goals between the Cloud provider and the customer; second, achieving high resource utilization; and third achieving the first two issues by as few time- and energy consuming reallocation actions as possible. To achieve these goals we propose a novel approach with escalation levels to divide all possible actions into five levels. These levels range from changing the configuration of VMs over migrating them to other PMs to outsourcing applications to other Cloud providers. In this paper we focus on changing the resource configuration of VMs in terms of storage, memory, CPU power and bandwidth, and propose a knowledge management approach using rules with threat thresholds to tackle this problem. Simulation reveals major improvements as compared to recent related work considering SLA violations, resource utilization and action efficiency, as well as time performance. In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve them using our algorithm. The selection is done among a set of experts, which refers to a set of DPM policies and voltage-frequency settings, leveraging the fact that different experts outperform each other under different workloads and device leakage characteristics. The online-learning algorithm adapts to changes in the characteristics and guarantees fast convergence to the best-performing expert. In our evaluation, we perform experiments on a hard disk drive (HDD) and Intel PXA27x core (CPU) with real-life workloads. Our results show that our algorithm adapts really well and achieves an overall performance comparable to the best-performing expert at any point in time, with energy savings as high as 61 and 49 for HDD and CPU, respectively. Moreover, it is extremely lightweight and has negligible overhead. In this paper, we study the reliable resource allocation (RRA) problem of allocating virtual machines (VMs) from multiple optically interconnected data centers (DCs) with the objective of minimizing the total failure probability based on the information obtained from the optical network virtulization. We first describe the framework of resource allocation, formulate the RRA problem, and prove that RRA is NP-complete. We provide an algorithm, named Minimum Failure Cover (MFC), to obtain optimal solutions for small scale problems. We then provide a greedy algorithm, named VM-over-Reliability (VOR), to solve large scale problems. Numerical results show that VOR achieves results close to optimal solutions gained by MFC for small scale problems. Numerical results also show that VOR outperforms the resource allocation through random DC selection (RDS). Online services hosted in data centers show significant diurnal variation in load levels. Thus, there is significant potential for saving power by powering down excess servers during the troughs. However, while techniques like VM migration can consolidate computational load, storage state has always been the elephant in the room preventing this powering down. Migrating storage is not a practical way to consolidate I O load. This paper presents Sierra, a power-proportional distributed storage subsystem for data centers. Sierra allows powering down of a large fraction of servers during troughs without migrating data and without imposing extra capacity requirements. It addresses the challenges of maintaining read and write availability, no performance degradation, consistency, and fault tolerance for general I O workloads through a set of techniques including power-aware layout, a distributed virtual log, recovery and migration techniques, and predictive gear scheduling. Replaying live traces from a large, real service (Hotmail) on a cluster shows power savings of 23 . Savings of 40--50 are possible with more complex optimizations. This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on off and VM relocations. We show the effectiveness of the approach applied to a server cluster test bed. Our experiments show that our approach conserves about 50 of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved. Power-aware scheduling problem has been a recent issue in cluster systems not only for operational cost due to electricity cost, but also for system reliability. As recent commodity processors support multiple operating points under various supply voltage levels, Dynamic Voltage Scaling (DVS) scheduling algorithms can reduce power consumption by controlling appropriate voltage levels. In this paper, we provide power-aware scheduling algorithms for bag-of-tasks applications with deadline constraints on DVS-enabled cluster systems in order to minimize power consumption as well as to meet the deadlines specified by application users. A bag-of-tasks application should finish all the sub-tasks before the deadline, so that the DVS scheduling scheme should consider the deadline as well. We provide the DVS scheduling algorithms for both time-shared and space-shared resource sharing policies. The simulation results show that the proposed algorithms reduce much power consumption compared to static voltage schemes.