Energy-Awareness at Data Centers: An Overview of Management and Architecture Framework Techniques

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1 Energy-Awareness at Data Centers: An Overview of Management and Architecture Framework Techniques Abed A. Al-Sulami, Abdullatif Al-Hazmi, Iyad Katib Abstract All over the world, cloud computing provides utility-oriented IT services to users. It enables hosting of pervasive applications from consumer, scientific, and business domains. It is considered as an innovative and challenging technology that promises to modify the way IT and storage resources will be accessed in the future. However, data centers contribute to high operational costs and electrical energy will be consumed in enormous amounts. There are unique challenges within the domain of energy-awareness challenges, which have been addressed more so recently, than in other domains. Many different methods have been applied in order to reduce energy consumption. In this work, we review and classify management and architecture framework techniques as methods to provide a comprehensive review. Index Terms Architecture Framework, Data centers, Energy-awareness, Management. I. INTRODUCTION Cloud computing is a new paradigm for the dynamic storage of information technology (IT) services, which can use virtual machine (VM) technologies in data centers to support environment isolation and consolidation [1]. Cloud computing delivers applications, infrastructure, and platform services that have been made available to users in a pay as you go model. In a business setting, these IT services are referred to as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) respectively. Numerous IT service providers such as Google and IBM are rapidly integrating data centers in a variety of locations around the world to deliver cloud computing services. Armbrust [2] reported that "cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service." The cloud provides significant benefits to IT organizations by relieving them of the necessity of setting up basic hardware and software, therefore enabling emphasis on IT innovation. In addition, developers with new ideas for innovative Internet services no longer need large capital outlays in hardware to integrate their service or human expenses to operate it. In order to realize the potential of cloud computing fully, providers of cloud services have to make sure that they can be flexible in their service delivery to meet many users' requirements, while keeping users isolated from the underlying infrastructure. Over the years, high performance has been the only concern in data center integrations. This demand has been fulfilled without paying much attention to the consumption of energy. However, according to Kaplan [3]. An average data center consumes as much energy as 25,000 households. While energy costs are growing sharply, availability dwindle and there is a need to shift the emphasis from optimizing data center resource management for perspicuous performance to optimizing them for energy efficiency, while maintaining high service level performance. Thus, providers of cloud services need to adopt measures to make sure that their profit margin is not reduced due to the high cost of energy. The increasing cost of energy is a threat as it increases the total cost of ownership (TCO). It also reduces the return on investment (ROI) of cloud infrastructures. In addition, there is an increasing worldwide demand for decreasing carbon footprints, which significantly influences climate change. For instance, some governments (e.g., the Japanese government) have formed a data center council in order to address the soaring energy consumption of data centers [4]. Lately, IT service providers have established a global consortium [5] in order to promote energy efficiency for data centers and to minimize environmental effects. Hence, providers are required to minimize energy consumption while ensuring good service delivery. Reducing the energy consumption of data centers is a difficult task as IT applications and data are increasing rapidly. So much so that increasingly larger servers and disks are required to process them at a faster rate within the needed time period. Green cloud computing is envisioned to reduce energy usage [6]. 5

2 This is important to ensure that the future growth of cloud IT is sustainable. Otherwise, cloud IT, with increasingly pervasive frontend client devices connecting with back-end data centers, may result in an enormous escalation of energy consumption. With a view to handle this problem, data center resources ought to be administered in an energy-efficient manner. Specifically, cloud resources must be allocated to reduce energy consumption. The main contribution of this paper is to highlight some of the methods used in energy-aware data centers and to offer a comprehensive review of them. Specifically, we review and classify management and architecture framework techniques. This paper is organized as follows. Section II discusses energy-aware techniques, which we divided into two parts: management techniques (Section II.A) and architecture framework techniques (Section II.B). In Section III we address the benefits and challenges of both techniques. In Section IV, we talk about the future of energy-efficiency at data centers. Finally, concluding remarks and future directions are presented in Section V. II. ENERGY-AWARENESS TECHNIQUES Unfortunately, energy-efficient resource allocation is not supported by cloud infrastructure to save energy. Also, it does not support an advanced economic model. Therefore, the architecture framework techniques aim to design hardware, applications, databases, and user interfaces, which can help to lower energy usage and are eco-friendly so that the user can access or deploy any application. On the other hand, management techniques rely on building policies that aim to control energy consumption. A. Management Techniques Rodero et al. [7] present method relies on the following. Firstly, continuously monitoring changes in temperature and handling emergencies, while ensuring the thermal degradation of the performance of the application. Another component seeks to achieve energy efficiency with a commitment to the level of service by the subscriber identification compatible workloads in physical machines when choosing VM migration strategies as thermal management. It is determined by the compatibility between the workloads through profiling and wide applications of HPC. It is clear that the first component involves cross-layer interactions between virtualization, physical layers, and the environment. Meanwhile, the second element requires interactions between the application and virtualization layers. These components will help to achieve the unthinkable across the layer thermal management science autonomy for data centers through a careful study of the trade-offs between the requirements of energy, the quality of the service application, and views of the operating environment. Their approach is twofold on reactive thermal management and application-centric VM allocations. Berral et al. [8] present a methodology that uses a couple of techniques in order to decrease the power consumption of a data center, while respecting the service level agreements (SLAs). The first technique includes turning off the power of idle machines which saves more than 200W of power in a testbed machine. By consolidation, a complementary mechanism attempts to execute all the tasks, but with a lower amount of machines. Therefore, scheduling is the main point in order to achieve this reduction in power consumption. In case of the load increasing, they will turn on the machines as needed. For this reason, their strategy is to rely on promoting a range of tasks, distributed among a group of machines, in as small a number of machines as possible without overly degrading implementation of these functions. They can implement many scheduling policies here in order to appoint new jobs in the system to available machines. Jobs are being redistributed and implemented in order to make some machines inactive and then turning them off. Turning the machines on again might take approximately two minutes, as this is not a free and instantaneous process. They deem many of the conventional policies including random scheduling, which assigns the tasks randomly, and round robin scheduling, which assigns a task to each available node and implies a maximization of the amount of resources to a task but also a sparse usage of the resources. By backfilling, they solve the former problem by trying to fill the nodes as much as possible. Dynamic backfilling is able to move tasks between nodes in order to provide a higher consolidation level. The task entry or exit from the system checks whether it should transfer any tasks to other nodes according to different criteria, such as the system occupation, current job performance, or expected user SLA satisfaction. While dynamic backfilling performs well when there is precise information, other policies are needed when information is either incomplete or inaccurate. The second technique uses machine-learning techniques. There is a set of level of machines and a set of functions, which result in the customer satisfaction level of each function and energy consumption before placing tasks in the machines or the transfer of tasks across machines. Then, using these predictions by step selection algorithm to choose a destination, machines produced with good customer satisfaction and integration opportunities. Lee et al. [9] suggest dividing resource management into the long and short-term. In the case of short-term, they put machines on the safety mode, or even turn off machines that are not needed. The situation is different in the long-term. The providers' infrastructure service in the cloud-computing model has to provide better control in the provision of resources and this relieves the burden of operating costs because of an excessive waste in allocations. 6

3 Table 1. Percentages of saving energy in management techniques. Technique Saving (%) According to the experiments, the algorithm indicates a considerable improvement in performance and energy consumption compared to regimes with separate cyber and physical control systems. Berral et al. [8] 10 Rodero et al. [7] 12 Lee et al. [9] 12 Basmadjian et al. [10] 20 Chen et al. [11] 32 Buyya et al. [6] 40 Basmadjian et al. [10] examined the case of private cloud computing from an energy saving incentive perspective. They offered a generic conceptual description for computing resources of a data center and determined their corresponding energy attributes. In addition, this study gives power consumption models for network equipment, storage devices, and servers. This study shows that by the use of appropriate energy optimization policies, following the guidance of accurate power consumption prediction models, about 20% of energy usage can be saved when single-side private cloud data centers are taken into account. On one hand, minimizing the data center's energy usage acknowledges IT for saving energy. On the other hand, it assists the IT sector in showing the approach for the rest of the economy by decreasing its carbon footprint. This study showed that energy saving is possible, through the study of the case of single-side of private cloud data centers. In the research of Chen et al. [11], a hybrid method is combined that utilizes predictive and reactive control to provision computing resources. Predictive control works with coarse time scales (e.g., hours) and uses workload prediction to identify how many servers should be adopted for every tier of a multi-tier web application. Reactive control handles any excess demand by adjusting the resource allocation to virtual machines (VMs) at finer time scales (e.g., minutes). The application control of these two approaches improves in meeting SLAs (service level agreements), saves energy, and reduces provisioning cost. An electric current-aware VM placement approach considers inputs from the physical environment and regards power usage when making placement decisions, thus improving energy efficiency. Also, the balance of payments model that incorporates the physical and cyber control systems as an energy-aware cyber-physical system (EaCPS) to coordinate IT resource provisioning and workload placement management. Buyya et al. [6] suggests four steps to select the VMs which can be migrated from one host to another. The first step is to make a single threshold (ST) which relies on determining the maximum CPU ability in the host and placing the new VM if it is under the total maximum. The other three steps rely on putting a ceiling and minimal employment thresholds for hosts and all VMs, which have to be between these two thresholds. If the employment of a CPU breaks the ceiling threshold they transfer a few VMs from the host to reduce employment in order to prohibit potential SLA violation. Also, if the employment of a CPU breaks the minimal threshold, all VMs must be transferred from this host or the host that are needed to switch off with a view to reduce the idle power exhaustion. Therefore, to achieve this, they introduce three policies to select VMs, which should be transferred from the host: Minimization of Migrations (MM): transferring the minimal amount of VMs to decrease transfer consumption. Highest Potential Growth (HPG): transferring VMs which have the minimum CPU usage comparatively to demand because diminish gross prospective growing of the employment and SLA violation. Random Choice (RC): selecting the needful amount of VMs by selecting them according to a uniformly dole out with random parameters. Table 1 shows the percentages of saving energy in management techniques discussed. We observe that the method of Buyya et al. is the most efficient technique to save energy. B. Architecture Framework Techniques The study of Yao et al. [12] focuses on two important issues. The first issue is the present allocation algorithms, considering that the consumptions of network resources are designed for traditional datacenters whose network architectures are usually not distributed over a wide area. In a cloud datacenter, the distance between different sub-datacenters has a considerable influence on the performance of applications. The second issue is the fact that the virtual machines requested by users have many configurations, including different numbers of processors and amounts of memory. Therefore, the issues of heterogeneity should be examined. As it is widely agreed that the resource allocation is problematic, the heuristic algorithm, a network-aware VM allocation algorithm based on maximal cliques (MCNVMA), was developed. 7

4 The primary objective is to minimize the maximum latency in communication among sub-datacenters. MCNVMA considers constraints on local physical resources, including the CPU and memory. MCNVMA is permeated into two separated steps. The first step is the selection of datacenters to place the VMs. This obstacle was viewed as a sub-graph selection obstacle, which is finding a maximum sub-graph with a given diameter. The second step is, once a data center is chosen, separate VMs are assigned to it during the datacenter selection process. This obstacle was viewed as a variant of the two dimensional Knapsack obstacle. All datacenters are regarded as Knapsacks and every VM is regarded as an item. The capacity of a Knapsack comprises available memory and processors. Each item has two types of cost: memory and processors. The value of every VM is the sum of the bandwidth. The value is changing dynamically along with the algorithm implementation. The primary objective is finding the allocation of VMs with the maximum value under the conditions of minimum communication and limited capacity. The algorithm was able to meet this objective through finding a sub-clique by invoking the two dimensional Knapsack algorithm which was implemented by the method of dynamic programming. Ultimately, according to this study's experiments, they can conclude that the MCNVMA algorithm has a contribution to energy saving, load balancing, and other factors. In the research of Bruneo et al. [13], a methodology on the basis of stochastic reward nets (SRNs), an extension of generalized stochastic Petri nets, to examine the cost of different energy saving techniques were studied in order to manage the federation of several clouds within the context of IaaS (Infrastructure as a Service). The public and private clouds have both been taken into account. The method is based on the compositional approach: the primary components characterizing a typical private cloud infrastructure, represented by the use of SRN models, can capture both their functional and non-functional behavior. A basic SRN is also linked to every public cloud infrastructure. Such basic SRNs are composed, based on the structure of the actual cloud federated environment that must be analyzed, in order to obtain a complete model. This model can be solved and useful performance parameters can be acquired considering the main purpose to reduce the overall management costs. Some policies and aspects characterizing a cloud system, including federation and VM consolidation, are taken into account and their influence is evaluated, therefore contributing to a rational and efficient deployment of the cloud-computing paradigm. Table 2. Percentages of saving energy in architecture framework techniques. Technique Saving (%) Bruneo et al. [13] Quan et al. [15] Yao et al. [12] Unknown Unknown Unknown Dupont et al. [16] 20 Sharifi et al. [14] 24.9 Kliazovich et al. [17] Sharifi et al. [14] introduced an algorithm to schedule the workload of a set of physical machines (PMs) to a set of virtual machines (VMs) in a datacenter. The main purpose was to minimize total energy usage by taking into account the conflicts between disk utilization, processor, and the costs of migrating VMs. The study presented four kinds of models to correspond with the discrepancy: the model of the target system, the model of energy, the model of migration, and namely the model of application. Given that workload conflicts in virtualized environments rely on numerous variables, including the virtualization software, three experiments were conducted on the configured KVM (Kernel-based Virtual Machine) target infrastructure in order to measure the performance isolation of VMs in three kinds of workloads: disk intensive workloads, possessor intensive workloads, and consolidation of disk and processor intensive workloads. A consolidation fitness (CF) criterion has been introduced and the suggested scheduler was utilized to merit the consolidation of a set of VMs on a PM prior to making the decision on any specific scheduling of VMs. The study calculated fitness criterion by dividing the degraded performance of consolidation by the energy savings resulted from the consolidation. The study's experiment indicated that performance degradation of disk intensive VMs is attributed to the overheads of disk virtualization by the virtualization software and that larger CFs associate with cases of larger numbers of VMs that generate heavier disk workloads. A multi-objective optimization issue is formulated using the CF metric and the empirical models of the target infrastructure and it is solved with the simulated annealing approach. 50 8

5 The results of this optimization resulted in a distributed VM scheduling algorithm according to migrating VMs to different PMs and switching PMs ON and OFF. Simulative results of the algorithm indicated 24.9% power savings in comparison to the other two approaches. The results showed that the suggested scheduling algorithm only slightly degraded the system performance by 1.2%. Quan et al. [15] presented an energy efficient resource allocation method that utilizes the capability of virtual machines' live migration to relocate resources dynamically. This approach has contributed to decreasing the energy consumption of the internal IaaS data center (internal Infrastructure as a Service) data center by the use of rearranging the resources allocation by the workload consolidation and frequency adjustment. This study imposes the fact that new computer components have less energy consumption and have a higher performance than the old generation. Therefore, they utilize the heuristic that move the heavy load software to the new servers with the large number of cores while moving light load software to the old servers with a smaller number of cores, and therefore switching off the largest possible number of the old servers. After conducting the experiments, the algorithm indicates that it can enhance the performance, in particular when a data center has a large number of old servers, and when some modern servers are working with a light load rate and several old servers are working with a heavy load rate. Dupont et al. [16] developed a framework and implemented it within the FIT4Green project, which can reduce the direct energy consumption by 20% of ICT resources of a data center. To compute the energy-aware placement of VMs, the framework depends on the Entropy open source library and Constraint Programming (CP) paradigm which can achieve flexibility in the adaptation of new constraints without redesign of the algorithm. The CP paradigm provides a particular language framework to express restrictions. By using this language, they can achieve the important objective of separating two different domains: The specific knowledge of the domain of the data center. The enhancer is aimed at computing the configuration and the assignment of VMs to the nodes. This reduces the total power consumption of the federation of data centers while satisfying different levels of service. Kliazovich et al. [17] rely on a data center scheduling approach that integrates the awareness of the network and energy efficiency, known as Data Center Energy-efficient Network-aware Scheduling (DENS). Network awareness refers to the ability of DENS to take and analyze the feedback of runtime data center switches and links, as well as decisions and actions based on the reaction network. The objective of DENS is to achieve a balance between function, individual performance, quality of service requirements, and the specific function in the service level agreement (SLA), the request of traffic, and the energy consumed by data centers. Consequently, DENS reduces the total energy consumption in data centers by identifying that the most relevant computing resources for job execution rely on the potential load and communication components of data centers. The communicational ability is known as the amount of end-to-end bandwidth service providers, or a group of individual servers of the data center architecture. Incompatible with conventional scheduling solutions, model data centers as a homogeneous pool of computing servers. The DENS methodology develops a hierarchical model consistent with the state of the art data center topologies. Data intensive functions demand a less computational burden. The neighboring nodes produce heavy data influx directed out of the data center and prevalent video sharing or geographical information services generate data intensive jobs. While decreasing the amount of computing servers required for job execution, the scheduling process is designed to avoid hotspots in a data center. In the proposed methodology, network awareness is achieved with the introduction of reaction channels from the main network switches. Furthermore, the proposed methodology reduces computational and memory overhead compared to previous approaches, such as flow differentiation, which make the proposed methodology easy to implement and port to existing data center schedulers. Table 2 shows the percentages of saving energy in architecture framework techniques discussed. We observe that the method of Kliazovich et al. [17] is the most efficient technique to save energy. III. BENEFITS AND CHALLENGES Both management and architecture framework techniques share common benefits. They can reduce the overall cost, reduce the emissions of carbon dioxide, and support energy efficiency while using less number of physical servers. However, since they are two quite different approaches to achieve an ultimate common goal, they face different challenges as shown in Table 3. 9

6 Table 3. The challenges of management and architecture framework techniques. Technique Challenges Controlling a higher load. Management Self-management. Place with less heating. Insertion of new requests. Architecture Energy optimization. Developing energy hardware. IV. ENERGY-EFFICIENT AT DATA CENTERS IN THE FUTURE Numerous kinds of high-performance equipment are separated in data centers. Data centers need to offer more power to support these high performance needs for the equipment. There is a variety of techniques to improve energy usage in data centers. One technique is using renewable energy sources (solar, wind, etc.) to offer the needed power to the data centers. Another technique is avoiding multiple conversions between direct current (DC) and alternating current (AC). The primary concept in this issue is doing the conversion only once, instead of doing it numerous times on several servers. Using more power leads to increased heat generation by data center infrastructures. A metric including the power usage effectiveness (PUE) has been offered to quantify the data centers' power efficiency. PUE might be utilized to indicate the amount of energy that is being usefully adopted, versus what amount is wasted. The PUE can be defined as the total facilities' power divided by IT equipment power. Nowadays, data centers have a PUE of about 2.0 to 3.0. In order to achieve power efficiency, solutions must be evolved at a system level via power management solutions and the careful adoption of hardware solutions, which can reduce the power usage of central processing units. V. CONCLUSION Cloud computing is becoming more primordial in the IT sector due to the numerous benefits it gives to its end users. In order to cope with its high demands, data centers possess a myriad of computing resources. In a majority of cases, this over-provision of resources results in astronomical numbers with respect to energy saving. Recently, the energy consumption of data centers has become a major issue owing to ecological and economic reasons. Thus, different approaches have been used in order to reduce energy consumption. In this paper, we reviewed these recent methods and provided a comprehensive review of them. REFERENCES [1] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, Xen and the art of virtualization, in 19 th ACM Symposium on Operating Systems Principles, vol. 37, pp , December [2] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, A view of cloud computing, Communications of the ACM, vol. 53 (4), pp , April [3] J. Kaplan, W. Forrest, and N. Kindler, Revolutionizing data center energy efficiency, McKinsey & Company, Techical Report, July [4] A. Beloglazov, J. Abawajy, and R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Generation Computer Systems, May [5] The green grid consortium, URL: [6] R. Buyya, A. Beloglazov, and J. Abawajy, Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges, in 2010 International Conference on Parallel and Distributed Processing Techniques and Applications, p. 12, July [7] I. Rodero, H. Viswanathan, E. K. Lee, M. Gamell, D. Pompili, and M. Parashar, Energy-efficient thermal-aware autonomic management of virtualized hpc cloud infrastructure, Journal of Grid Computing, vol. 10 (3), pp , July [8] J. Berral, I. Goiri, R. Nou, F. Julia, J. Guitart, R. Gavalda, and J. Torres, Towards energy-aware scheduling in data centers using machine learning, in 1st International Conference on Energy-Efficient Computing and Networking, pp , April [9] Y. C. Lee, A. Y. Zomaya, M. Gamell, D. Pompili, and M. Parashar, Energy efficient utilization of resources in cloud computing systems, Journal of Supercomput, vol. 60, pp , March [10] R. Basmadjian, H. D. Meer, R. Lent, and G.Giuliani, Cloud computing and its interest in saving energy: the use case of a private cloud, Journal of Cloud Computing, vol. 1 (5), June [11] H. Chen, P. Lu, P. Xiong, C.-Z. Xu, and Z. Wang, Energy-aware application performance management in virtualized data centers, Frontiers of Computer Science, vol. 6 (4), pp , June [12] Y. Yao, J. Cao, and M. Li, A network-aware virtual machine allocation in cloud datacenter, in 10th IFIP International Conference NPC, vol. 8147, pp , September [13] D. Bruneo, F. Longo, and A. Puliafito, Modeling energy-aware cloud federations with srns, Springer Berlin Heidelberg, vol. 7400, pp , [14] M. Sharifi, H. Salimi, and M. Najafzadeh, Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques, Journal of Supercomputing, vol. 61(1), pp , July [15] D. M. Quan, R. Basmadjian, H. D. Meer, R. Lent, T. Mahmoodi, D. Sannelli, F. Mezza, and C. Dupont, Energy efficient resource allocation strategy for cloud data centers, in 26th International Symposium on Computer and information Sciences, vol. 2, pp , September [16] C. Dupont, G. Giuliani, and F. Hermenier, An energy aware framework for virtual machine placement in cloud federated data centers, in 3rd International Conference on Future Energy Systems (e-energy), pp. 1 10, May [17] D. Kliazovich, P. Bouvry, and S. U. Khan, Dens: data center energy efficient network-aware scheduling, Cluster Computing, vol. 16 (1), pp , September Abed A. Al-Sulami is currently a Master student at the Computer Science Department of King Abdulaziz University, Jeddah, Saudi Arabia. ( Abdullatif Al-Hazmi is currently a Master student at the Computer Science Department of King Abdulaziz University, Jeddah, Saudi Arabia. ( Iyad Katib is an assistant professor at the Computer Science Department of King Abdulaziz University. ( 10

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