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In recent years, energy efficiency has emerged as one of the most important design requirements for modern computing systems, ranging from single servers to data centers and Clouds, as they continue to consume enormous amounts of electrical power. Apart from high operating costs incurred by computing resources, this leads to significant emissions of CO2  into the environment. For example, currently, IT infra- structures contribute about 2% of the total CO2  footprints. Unless energy-efficient techniques and algorithms to manage computing resources are developed, IT’s contri- bution in the world’s energy consumption and CO2  emissions is expected to rapidly grow. This is obviously unacceptable in the age of climate change and global warming. To facilitate further African Mango developments in the area, it is essential to survey and review the existing body of knowledge. Therefore, in this chapter plus size wedding dresses
, we have studied and classified various ways to achieve the power and energy efficiency in computing systems. Recent research advancements have been discussed and categorized across the hard- ware, OS, virtualization, and SEO Services data center levels.

It has been shown that intelligent management  of trade show booths computing resources can lead to a significant reduction of the energy consumption by a system, while still meeting the performance requirements. A relaxation of the performance constraints usually results in a further decrease of the energy consumption.  One of the significant advancements that have facilitated the progress in managing single computing servers is the implementation of the ability to penny stocks to watch adjust the voltage and frequency of the CPU (DVFS), followed by the subsequent introduction and imple- mentation of ACPI. These technologies have enabled the run-time software control over the power consumption by the CPU traded for the performance. In electric cigarettes this work, we have surveyed and classified various approaches to control the power consump- tion  by a  system from the  OS  level  applying DVFS and  other power saving techniques and algorithms. A number of research efforts aimed at the development of leather furniture efficient algorithms for managing the CPU power consumption have resulted in the mainstream adoption of DVFS in a form of the implementation in a kernel module of the Linux OS. The main idea of the technique is to monitor the CPU utilization, and continuously adjust its clock frequency and supply voltage to match the current performance requirements.

The virtualization technology has further advanced the area by introducing the ability to encapsulate the workload in VMs and consolidate them to a single physical server, while providing fault and performance  isolation between individual VMs. The consolidation  has become especially effective after the adoption of multi-core CPUs in computing environments, as numerous VMs can be allocated to a single physical node leading to the improved utilization of resources and reduced energy consumption compared to a multi-node setup. Besides the consolidation, leading  virtualization vendors (i.e., Xen, VMware) similarly to the Linux OS implement continuous DVFS. The power management problem becomes more complicated when considered from  the data center level. In this case the system is represented by a set of interconnected computing nodes that need to be managed as a single resource in order to optimize the energy consumption. The efficient resource  management is extremely important for data pokies centers and Cloud computing systems comprising multiple computing nodes, as due to a low average pokies
utilization of resources, the cost of energy consumed by pokies computing nodes and a supporting infrastructure (e.g., cooling systems, power supplies, PDU) leads to an inappropriately high TCO. We have classified and discussed a number of recent research works that deal with the problem of the energy-efficient resource management in non-virtualized and
email lists virtua- lized data centers. Due to a narrow dynamic power range of servers, the most effective power saving technique is to allocate the workload to the minimum number of physical servers and switch idle servers off. This technique improves the utiliza- tion of resources and eliminates the sole f80 power consumed by idle servers, which accounts for up to 70% of the power consumption by fully utilized servers. In virtualized environments and Clouds, live and offline VM migration offered by the sole f63 virtualiza- tion technology have enabled the technique of dynamic consolidation of VMs

according to their current performance
leather furniture requirements. However, applying VM migration leads to energy and performance overheads, requiring a careful analysis and intelligent techniques to eliminate non-productive migrations that can occur due to workload variation and violations of theSLAnegotiated between Cloud providers and their customers. Common
total gym xls limitations of most of the surveyed research works are that no other system resource except for the CPU are considered in the optimization; the transition overhead caused by switching power states of resources and the uggs VM
pokies migration  overhead  are  not  handled  leading  to  performance degradation;  VM migration is not applied to optimize the allocation in run-time. In summary, a more generic solution suitable for modern Cloud computing  environments should comply with the following requirements:

l Virtualization of the infrastructure to support baby headbands hardware and software  hetero- geneity and simplify the resource provisioning.

l The application of VM migration to gym mats continuously adapt the allocation  and quickly respond to changes in the workload.

l The  ability  to  handle multiple  applications with diabetic diet differentSLAowned  by multiple users.

l Guaranteed meeting of the QoS requirements for buy nuratrim each application.

l The support for different kinds of applications creating, mixed workloads.

l The decentralization and high performance
bread maker of the optimization algorithm  to ensure scalability and fault tolerance.

l The  optimization  of  resource  provisioning  considering  multiple   system resources, such as the CPU, memory, disk storage, and network interface.

Apart from satisfying the listed requirements, for work from home future research in the area, we propose the investigation of the following directions. First of all, due to the wide adoption of multi-core CPUs, it weight loss pills is important to develop energy-efficient resource management approaches that will leverage such architectures. Apart from the CPU and memory, another significant energy consumer in data centers
WP Robot is the network interconnect infrastructure. Therefore, it is crucial to develop intelligent techniques to manage network resources efficiently. One of the ways to achieve this for virtualized data centers is to continuously optimize network topologies established between VMs, and thus reduce the network communication overhead and Tinnitus Miracle the load of network devices. Regarding the low-level system design, it is important to improve the efficiency of power supplies and develop hardware components supporting the performance scaling proportionally to the power consumption. A reduction of the transition overhead caused by switching between different power states and the VM migration overhead can also greatly advance the energy-efficient resource management and should be addressed by

future research.

Another future research direction is the investigation of Cloud federations com-

prising geographically seanol distributed data centers. For example, efficient distribution of the workload across geographically distributed data centers can reduce the costs by dynamically reallocating the workload to a place where the computing resources, energy and/or cooling are cheaper (e.g., solar energy during daytime across different time zones, Spray Tan efficient cooling due to climate conditions).
harman kardon soundsticks ii Other important directions for future research are the investigation of a fine-grained user’s control over the power consumption/CO2 emissions in Cloud environments, and support for flexibleSLAnegotiated between resource providers and users. Building on the  strong foundation of prior works, new research projects are starting to investigate advanced resource management and power-saving techniques. Nevertheless ,Bose Companion 3 there are still many open research challenges that are becoming even more prominent in the age of Cloud computing.

Buyya have proposed the GreenCloud seo firms project aimed at the development of energy-efficient provisioning of Cloud resources, while meeting QoS requirements defined by theSLAestablished through a negotiation between providers and consu- mers. The project has explored the problem of power-aware allocation of VMs in Cloud data centers for application services based on user QoS requirements such as deadline and budget constraints [65]. The authors have introduced a real-time virtual machine model. Under this model, a Cloud provider provisions VMs for requested real-time applications and ensures meeting the facial hair removal specified deadline constraints.

The problem is addressed at several levels. At the first level, a user submits a

request to a resource broker for provisioning resources for an application consisting

of a set of subtasks with specified CPU and deadline requirements. The broker

translates the specified resource requirements into a Cosmetic Surgery Thailand request for provisioning VMs and submits the request to a number of Cloud data centers. The data centers return the price of provisioning VMs for the broker’s request if the deadline requirement can be fulfilled. The broker chooses the data center that provides the lowest price of resource provisioning. The selected data center’s VM provisioner allocates the requested replica bags VMs to the physical resources, followed by launching the user’s applica- tions. The authors have proposed three policies for scheduling real-time VMs in a data center using DVFS to reduce the energy consumption, while meeting the deadline constraints and maximizing the request acceptance rate. The Lowest- DVS policy adjusts the CPU’s P-state to the lowest level, ensuring that all the real-time VMs meet their deadlines. The d-Advanced-DVS policy over-scales the CPU speed up to d% to increase the acceptance rate. The Adaptive-DVS policy uses an M/M/1 queueing model to calculate the optimal CPU speed if the arrival rate and service time of real-time VMs can be estimated in advance.

The proposed approach has been evaluated via simulations using the CloudSim toolkit [70]. The simulations results have shown that the d-Advanced-DVS shows the best performance in terms of profit per unit of the consumed power, as the CPU performance is automatically adjusted according to the system load. The perfor- mance of the Adaptive-DVS is limited by the simplified queueing model.

Gmach have studied the problem of the energy-efficient dynamic consolidation of VMs in enterprise environments. The authors have proposed a combination of a trace-based workload placement controller and a reactive migra- tion controller. The trace-based workload placement controller collects data on resource usage by VMs instantiated in the data center and uses this historical information to optimize the allocation, while meeting the specified QoS require- ments. This controller performs multiobjective optimization by trying to find a new placement of VMs that will minimize the number of servers needed to serve the

workload, while limiting the number of VM migrations required to achieve the new

placement. The bound on the number of migrations is supposed to be set by the system administrator depending on the acceptable VM migration overhead. The controller places VMs according to their peak resource usage over the period since the previous reallocation, which is set to 4 hours in the experimental study.

The reactive migration controller continuously monitors the resource utilization of physical nodes and detects when the servers are overloaded or underloaded. In contrast to the trace-based workload placement controller, it acts based on the real- time data on the resource usage and adapts the allocation in a small scale (every minute). The objective of this controller is to rapidly respond to fluctuations in the workload. The controller is parameterized by two utilization thresholds that deter- mine overload and underload conditions. An overloading occurs when the utilization of CPU or memory of a server exceeds a given threshold. An underloading occurs when the CPU or memory usage averaged over all the physical nodes falls below a  specified threshold. The  threshold values are  statically  set  according  to  the workload analysis and QoS requirements.

The authors have proposed several policies based on different combinations of the described optimization controllers with different utilization thresholds. The simula- tion-driven evaluation using 3 months of real-world workload traces for 138 SAP applications has shown that the best results can be achieved by applying both optimi- zation controllers simultaneously. The best policy invokes the workload placement controller every 4 hours, and when the servers are detected to be lightly utilized. The migration controller is executed in parallel to tackle the overloading and underloading of servers when they occur. This policy provides minimal CPU violation penalties and requires 10–20% more CPU capacity than the ideal case.

Cardosa have investigated the problem of the power-efficient VM 

allocation in virtualized enterprise computing environments. They leverage min, max, and shares parameters, which are supported by the most modern VM man- agers. Min and max allow the user to specify minimum and maximum of CPU time that can be allocated to a VM. Shares parameter determines proportions, in which CPU time will be allocated to VMs sharing the same resource. Such approach suits only enterprise environments, as it does not support strictSLAand requires the knowledge of the application priorities.

The  authors  have  provided  a  mathematical  formulation of  the  optimization problem. The objective function to be optimized includes the power consumption and utility gained from the execution of a VM, which is assumed to be known a priori. The authors provide several heuristics for the defined model and experi- mental results. A basic strategy is to place all the VMs at their maximum resource requirements in a first-fit manner and leave 10% of the spare capacity to handle the future growth of the resource usage. The algorithm leverages the heterogeneity of the infrastructure by sorting physical machines in the increasing order of the power cost per unit of capacity. The limitations of the basic strategy are that it does not leverage relative priorities of different VMs, it always allocates a VM at its maxi- mum resource requirements, and uses only 90% of a server’s capacity. This algo- rithm has been used as the benchmark policy and improved throughout the paper eventually  culminating  in  the  recommended  PowerExpandMinMax  algorithm. In comparison to the basic policy, this algorithm uses the value of profit that can be gained by allocating an amount of resource to a particular VM. It leverages the ability to shrink a VM to minimum resource requirements when necessary and expand it when it is allowed by the spare capacity and can bring additional profit. The power consumption cost incurred by each physical server is deducted from the profit to limit the number of servers in use.

The authors have evaluated the proposed algorithms on a range of large-scale

simulations and a small real data center testbed. The experimental results show that the PowerExpandMinMax algorithm consistently outperforms the other policies across a broad spectrum of inputs—varying VM sizes and utilities, varying server capacities, and varying power costs. One of the experiments on a real testbed showed  that  the  overall  utility  of  the  data  center  can  be  improved  by  47%. A  limitation of this work is that migration of VMs is not applied in order to adapt the allocation of VMs at run-time—the allocation is static. Another problem is that no other system resources except for CPU are handled by the model. Moreover, the approach requires static definition of the application priorities that limits the

applicability in real-world environments.

pMapper:  Power and Migration Cost

Aware Application Placement

Verm have investigated the problem of dynamic placement of applications in virtualized systems, while minimizing the power consumption and main- taining theSLA. To address the problem, the authors have proposed the pMapper application placement framework. It consists of three managers and an arbitrator, which coordinates their actions and makes allocation decisions. Performance Man- ager monitors the applications’ behavior and resizes VMs according to current resource requirements and theSLA. Power Manager is in charge of adjusting hardware power states and applying DVFS. Migration Manager issues instructions for live migration of VMs in order to consolidate the workload. Arbitrator has a global view of the system and makes decisions about new placements of VMs and determines which VMs and which nodes should be migrated to achieve this place- ment. The authors claim that the proposed framework is general enough to be able to incorporate  different  power  and  performance  management  strategies  underSLAconstraints.

The authors have formulated the problem as a continuous optimization: at each time frame, the VM placement should be optimized to minimize the power con- sumption and maximize the performance. They make several assumptions to solve the problem, which are justified by experimental studies. The first of them is the performance isolation, which means that a VM can be seen by an application running on that VM as a dedicated physical server with the characteristics equal to the VM parameters. The second assumption is that the duration of a VM live migration does not depend on the background load, and the cost of migration can be estimated based on the VM size and profit decrease caused by anSLAviolation. The moreover, the solution does not focus on specific applications and can be

applied to any kind of the workload. Another assumption is that the power minimization algorithm can minimize the power consumption without knowing the actual amount of power consumed by the application.

The authors have presented several algorithms to solve the defined problem. They have defined it as a bin packing problem with variable bin sizes and costs. The bins, items to pack, and bin costs represent servers, VMs, and power consumption of servers, respectively. To solve the bin packing problem, first-fit decreasing algorithm (FFD) has been adapted to work for differently sized bins with item-dependent cost functions. The problem has been divided into two subproblems: in the first part, new utilization values are determined for each server based on the cost functions and required performance; in the second part, the applications are placed onto servers to fill the target utilization. This algorithm is called min Power Packing (mPP). The first phase of mPP solves the cost minimization problem, whereas the second phase solves the application placement problem. mPP has been adapted to reduce the migration cost by keeping track of the previous placement while solving the second phase. This variant is termed mPPH. Finally, the placement algorithm has been designed that optimizes the power and migration cost trade-off (pMaP). A VM is chosen to be migrated only if the revenue due to the new placement exceeds the migration cost. pMap searches the space between the old and new placements and finds a placement that minimizes the overall cost (sum of the power and migration costs). The authors have implemented the pMapper architecture with the proposed algorithms and per- formed extensive experiments to validate the efficiency of the approach. The experi- mental results show that the approach allows saving about 25% of power relatively to the Static and Load Balanced Placement algorithms. The researchers have suggested several directions for the future work such as the consideration of memory bandwidth, a  more advanced application of idle states, and an extension of the theoretical

formulation of the problem.

Song have studied the problem of the efficient resource allocation in multiapplication virtualized data centers. The objective is to improve the utilization of resources leading to the reduced energy consumption. To ensure the QoS, the resources are allocated to applications proportionally according to the application priorities. Each application can be deployed using several VMs instantiated on different physical nodes. In resource management decisions, only the CPU and RAM utilizations are taken into account. In cases of limited resources, the perfor- mance of a low-priority application is intentionally degraded and the resources are allocated to critical applications. The authors have proposed scheduling at three levels: the application-level scheduler dispatches requests among application’s VMs; the local level scheduler allocates resources to VMs running on a physical node according to their priorities; and the global-level scheduler controls the resource “flow” among the applications. Rather than apply VM migration to implement the global resource “flow,” the authors preinstantiate VMs on a group of physical nodes and allocate fractions of the total amount of resources assigned to an application to different VMs.

Chase et al. [52] have studied the problem of managing resources in Internet hosting centers. Resources are shared among multiple service applications with specifiedSLA—the throughput and latency. The authors have developed an OS for an Internet hosting center (Muse) that is a supplement for theOSsof the individual servers and supposed to manage and coordinate interactions between the data center’s components. The main distinction from previous research on resource management in hosting centers is that the objective is not just to schedule resources efficiently but also to minimize the consumption of electrical power by the system components. In this study, this approach is applied to data centers in order to reduce: operating costs (power consumption by computing resources and cooling system); CO emissions, and thus the impact on the environment; thermal vulnerability of the system due to cooling failures or high service load; and over-provisioning in capacity planning. Muse addresses these problems by automatically scaling back the power demand (and therefore waste heat)

when appropriate. Such a control over the resource usage optimizes the trade-off between the service quality and price, allowing the support of flexibleSLAnegotiated between consumers and the resource provider.

The main challenge is to determine resource demand of each application at its current request load level, and to allocate resources in the most efficient way. To deal with this problem, the authors apply an economic framework: the system allocates resources in a way that maximizes the “profit” by balancing the cost of each resource unit against the estimated utility, or the “revenue” that is gained from allocating that resource unit to a service. Services “bid” for the resources in terms of the volume and quality. This enables negotiation of the SLA according to the available budget and current QoS requirements, that is, balancing cost of resource usage (energy cost) and benefit gained due to the usage of this resource. This enables a  data  center  to  improve the  energy efficiency  under  a  fluctuating  workload, dynamically match the load and power consumption, and respond gracefully to resource shortages.

The system maintains an active set of servers selected to serve requests for each service. Network switches are dynamically reconfigured to change the active set when necessary. Energy consumption is reduced by switching idle servers to power- saving states (e.g., sleep, hibernation). The system is targeted at the web workload, which leads to a “noise” in the load data. The authors address this problem by applying the statistical “flip-flop” filter, which reduces the number of unproductive realloca- tions and leads to a more stable and efficient control.

This work has created a foundation for numerous studies in the area of power-

efficient resource allocation at the data center level; however, the proposed approach has several weaknesses. The system deals only with the CPU management, but does not take into account other system resources such as memory, disk storage, and network interface. It utilizes APM, which is an outdated standard for Intel-based systems, while currently adopted by industry standard is ACPI. The thermal factor is not considered as well as the latency due to switching physical nodes on/off. The authors have pointed out that the management algorithm is stable, but it turns out to be relatively  expensive during significant changes in the  workload. Moreover, heterogeneity of the software configuration requirements is not handled, which can be addressed by applying the virtualization technology.

8.2.3   Energy-Efficient Server Clusters

Elnozahy et al.  [20] have explored the  problem of power-efficient resource management in a single-service environment for web applications with fixedSLA(response time) and automatic load-balancing running in a homogeneous cluster. The motivation for the work is the reduction of operating costs and improvement of

the error-proneness due to overheating. Two power management mechanisms  that

are applied switching physical nodes on and off (vary-on vary-off, VOVO) and

DVFS of the CPU.

The authors have proposed five policies for the resource management: indepen- dent voltage scaling (IVS), coordinated voltage scaling (CVS), VOVO, combined policy (VOVO-IVS), and coordinated combined policy (VOVO-CVS). The last mentioned policy is stated to be the most advanced and is provided with a detailed description and mathematical model for determining CPU frequency thresholds. The thresholds define when it is appropriate to turn on an additional physical node or turn off an idle node. The main idea of the policy is to estimate total CPU frequency required to provide the expected response time, determine the optimal number of physical nodes, and proportionally set the frequency for all the nodes.

The experimental results show that the proposed IVS policy can provide up to

29% energy savings and is competitive with more complex schemes for some workloads. VOVO policy can produce saving up to 42%, whereas CVS policy in conjunction with VOVO (VOVO-CVS) results in 18% higher savings that are obtained using VOVO separately. However, the proposed approach is limited in the following factors. The transition time for starting up an additional node is not considered. Only a single application is assumed to be run in the cluster and the load- balancing is supposed to be done by an external system. Moreover, the algorithm is centralized that creates a single point of failure and reduces the system scalability. The workload data are not approximated, which can lead to inefficient decisions due to fluctuations in the demand. No other system resources except for CPU are

considered in resource management decisions.

Cardosa et al. [61] have investigated the problem of the power-efficient VM allocation in virtualized enterprise computing environments. They leverage min, max, and shares parameters, which are supported by the most modern VM man- agers. Min and max allow the user to specify minimum and maximum of CPU time that can be allocated to a VM. Shares parameter determines proportions, in which CPU time will be allocated to VMs sharing the same resource. Such approach suits only enterprise environments, as it does not support strictSLAand requires the knowledge of the application priorities.

The authors have provided a mathematical formulation of the optimization problem. The objective function to be optimized includes the power consumption and utility gained from the execution of a VM, which is assumed to be known

a priori

. The authors provide several heuristics for the defined model and experi- mental results. A basic strategy is to place all the VMs at their maximum resource requirements in a first-fit manner and leave 10% of the spare capacity to handle the future growth of the resource usage. The algorithm leverages the heterogeneity of the infrastructure by sorting physical machines in the increasing order of the power cost per unit of capacity. The limitations of the basic strategy are that it does not leverage relative priorities of different VMs, it always allocates a VM at its maxi- mum resource requirements, and uses only 90% of a server’s capacity. This algo- rithm has been used as the benchmark policy and improved throughout the paper eventually culminating in the recommended PowerExpandMinMax algorithm. In comparison to the basic policy, this algorithm uses the value of profit that can be gained by allocating an amount of resource to a particular VM. It leverages the ability to shrink a VM to minimum resource requirements when necessary and expand it when it is allowed by the spare capacity and can bring additional profit. The power consumption cost incurred by each physical server is deducted from the

profit to limit the number of servers in use. Utilities-based Power Consolidation

Resource Allocation Using Virtual Clusters

Stillwell et al. [59] have studied the problem of the resource allocation for HPC

applications in virtualized homogeneous clusters. The objective is to maximize the resource utilization, while optimizing user-centric metric that encompasses both performance and fairness, which is referred to as the yield. The idea is to design a scheduler focusing on a user-centric metric. The yield of a job is “a fraction of its maximum achievable compute rate that is achieved.” A yield of 1 means that the job consumes computational resources at its peak rate.

To formally define the basic resource allocation problem, the authors have assumed that an application requires only one VM instance; the application’s computational

Kernel-based Virtual Machine (KVM)

KVM is a virtualization platform, which is implemented as a module of the Linux

kernel [51]. Under this model, Linux works as a hypervisor and all the VMs are regular processes scheduled by the Linux scheduler. This approach reduces the complexity of the hypervisor implementation, as scheduling and memory manage- ment are handled by the Linux kernel.

KVM supports the S4 (hibernate) and S3 (sleep/stand by) power states.

9

S4 does

not require any specific support from KVM: on hibernation, the guest OS dumps the memory state to a hard disk and initiates powering off the computer. The hypervisor translates this signal into termination of the appropriate process. On the next boot, the OS reads the saved memory state from the disk, resumes from the hibernation, and reinitializes all the devices. During the S3 state, memory is kept powered, and thus the content does not need to be saved to a disk. However, the guest OS must save the states of the devices, as they should be restored on a resume. During the next boot, the BIOS should recognize the S3 state and instead of initializing the devices, but jump directly to the restoration of the saved device states. Therefore, the BIOS has to be modified in order to support such behavior.

7.2 Energy Management for Hypervisor-based VMs

Stoess et al. [45] have proposed a framework for energy management in virtua- lized servers. Typically, energy-aware OSs assume the full knowledge and full control over an underlying hardware, implying device- or application-level account- ing for the energy usage. However, in virtualized systems, a hardware resource is shared among multiple VMs. In such an environment, device control and accounting information are distributed across the system, making it infeasible for an OS to take the full control over the hardware. This results in the inability of energy-aware

OSs to invoke their policies in the system. The authors have proposed mechanisms

for fine-grained guest OS-level energy accounting and allocation. To encompass the diverse demands on energy management, the authors have proposes to use the notion of energy as the base abstraction in the system, an approach similar to the currentcy model in ECOsystem described in Section 6.2.

The prototypical implementation comprises two subsystems: a host-level resource manager and an energy-aware OS. The host-level manager enforces system-wide power limits across VM instances. The power limits can be dictated by a battery or a power generator, or by thermal constraints imposed by reliability requirements and the cooling system capacity. The manager determines power limits for each VM and device type, which cannot be exceeded to meet the defined power constraints. The complementary energy-aware OS is capable of fine-grained application-specific energy management. To enable application-specific energy management, the frame- work supports accounting and control not only for physical but also for virtual devices. This enables guest resource management subsystems to leverage their application-specific knowledge.

Experimental results presented by the authors show that the prototype is capable of enforcing power limits for energy-aware and energy-unaware guest OSs. Three areas are considered to be prevalent for future work: devices with multiple power

states, processors with