Efficient Resource Allocation for Green Cloud Data Center

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1 Efficient Resource Allocation for Green Cloud Data Center Myint Myat Myo, and Thandar Thein Abstract As high popularity and growth demand of Cloud Computing, the backend cloud infrastructure providers have tremendous attention to effective management over their cloud data centers, not only to fulfill the provision of everything-as-a-service to cloud users but also to achieve maximize revenue and minimize environmental affects. Therefore, these factors become their objectives and are realized among cloud providers by various efforts. This paper presents energy efficient cloud infrastructure resource allocation framework for Green Cloud Data Center taking into account data center energy efficiency and resource utilization criteria and supports satisfied allocation decisions towards green cloud computing. The novelty of our work relies on Energy efficient Resource Allocation algorithm building upon the integral work of Reinforcement Learning (RL) and Fuzzy Logic for dynamic cloud environments. We have validated our approach by examining a set of performance evaluation study under dynamic workload scenarios for Cloud environments using CloudSim toolkit. Keywords Cloud Computing, CloudSim Toolkit, Green Data Center, Fuzzy Logic, Reinforcement Learning. C I. INTRODUCTION LOUD computing is rapidly gaining in popularity among computing paradigms for its unique property of offering Everything as a Service on-demand basis over the Internet in pay-per-use manner. As the ability of the fulfillment of cloud computing for many flexible, on-demand services is progressive spotlight, all IT industry, academic and business community have concentrated on this computing and many societies have joined the cloud computing. The cloud computing can be deployed as three-tier structure, namely Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) from low to high level. One important cloud computing deployment model is Infrastructure as a Service (IaaS) where massive heterogeneous data center infrastructures, and virtualized physical resources such as CPU, memory, storage and network, is provided to cloud users. The software development/deployment environment for enterprise systems as well as software applications can be provided as Platform as a Service (PaaS) and Software as a Service (SaaS) models respectively in cloud computing. The cloud service providers and cloud users negotiate a set of service levels representing performance and economical Myint Myat Myo is with University of Computer Studies, Mandalay, Myanmar (phone: ; mnt.myo@gmail.com). Thandar Thein is with University of Computer Studies, Yangon, Myanmar ( thandartheinn@gmail.com). constraints for service delivery in advance, referred as Service Level Agreement (SLA). Therefore cloud service providers emphasize the decisions of the amount of computing resources to allocate to the requested services to meet SLAs and these decisions may also have a critical impact on Total Cost of Ownership (TCO) of cloud service providers including investment, operating and energy consumption costs. Both resource under-provisioning and resource over-provisioning may consequently cause SLA violations and lead to the loss of revenue and comprise the various complex problems for cloud service providers. Therefore, the ultimate goal for an infrastructure provider is to maximize its profit by offering resource allocation decisions and degrading the number of SLA violations. Moreover, the tremendous pressure of the cloud service providers when designing optimal resource allocation is the growth of data center resource densities and energy consumption while minimizing SLA violations. Actually, large-scale data centers behind cloud computing are utilized to provide the capacity needed to meet the demands of the services in the cloud, and the core building block of cloud computing is cloud infrastructure resources in data centers, by which cloud service providers enable to provision the infrastructure to cloud service users. Obviously, the cloud data centers behave as cloud providers and it is a facility dedicated to a large group of networked servers and associated power distribution, networking, and cooling equipment. Large scale cloud computing providers like Amazon, Google, Facebook operate dozens of data centers spread across the world for the cloud service delivery, where hundreds of thousands of servers in each individual. Among the factors impacting on Total Cost of Ownership (TCO) for the cloud data centers, the energy consumption cost is the most influence factor and this will rapidly grow in recent due to the growth of cloud computing popularity. Therefore, the energy efficiency for the sake of economical and environmental footprint for cloud data centers is now one of the top concerns for all institutions and cloud resource providers. According to 2007 report on computing datacenters by US Environmental Protection Agency (EPA), the datacenters in US consumed about 1.5% of total energy, which costs about $4.5 billon. This high usage also translates to very high carbon emissions which was estimated to be about Metric Megatons each year [12]. 67

2 In this paper, the problem of dynamically delivering physical resources of cloud infrastructure is tackled as a form of energy efficient resource allocation manner, able to reduce the energy consumption spent by the infrastructure for delivering services and to achieve the SLA guarantee of infrastructure services at the same time. Due to the dynamic nature of resource demands and the complexity of the cloud environment, Reinforcement Learning (RL) approach is utilized to recommend the optimal allocation policy. Moreover, we consider more than one criterion to allocate resources with RL approach; we transform all criteria results as Fuzzy Sets and make allocation decision with the aid of Fuzzy System. II. LITERATURE REVIEW Recently, the win-win strategy (maximize resource utilization, minimize energy efficiency) for cloud resource management is the critical role for cloud IaaS providers because of the growth rate of cloud computing. Therefore, many research works investigates various scheme including resource management, resource allocation, resource scheduling, resource consolidation and others for green cloud computing community. L. Liu et al. [8] presented GreenCloud architecture which provided not only power efficiency and effectiveness for online gaming applications hosted in data center environment but also guaranteed the performance for the cloud users. This solution was achieved by live migration of virtual machine technology, and heuristic algorithm in Migration Scheduling Engine to provide optimal VM placement by minimize possible migration cost and execution cost and comprehensive online-monitoring for many performance-sensitive applications was also performed. They evaluated their framework with an online real-time game, Tremulous, and their verification showed the efficiency and effectiveness of their proposed architecture can save up to 27% of the energy. A. Beloglazov et al. [3]-[9] defined an architectural framework and principles for Green Cloud computing solutions that cannot only minimize operational costs but also reduce the environmental impact. Based on this architecture, they presented resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. They proposed energy-efficient allocation heuristics provision data center resources to client applications in Modified Best Fit Decreasing (MBFD) way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). They conducted performance evaluation of their approach using the CloudSim toolkit and their results offered significant cost savings and demonstrated high potential for the improvement of energy efficiency. Among numerous green cloud computing efforts, we focus on the key point of cloud resource management, cloud resource allocation, for the cloud computing environment. The combination of the strengths of both RL and queuing models in a hybrid approach was developed for autonomic resource allocation in [5]. They trained RL in offline on data collected to avoid suffering potentially poor performance in live online training while controlled their system with queuing model policy. They used RL to train multi-layer perceptrons to enable scaling to substantially larger state spaces. They showed significant performance improvements over a variety of initial model-based policies in both open-loop and closed-loop traffic. Also, X. Dutreilh et al. [10] used Reinforcement Learning for Autonomic Resource Allocation in Clouds and they solved the problem of having good policies in the early phases of RL using appropriate initialization as well as convergence speedups applied throughout the learning phases for high convergence time problem. The performance model change detection was introduced to complete the learning process management. In this paper, we grant the optimal energy efficient resource allocation at data center level by conducting the trial-and-error search of RL approach, in contrast to papers [3]-[9] where the allocation decision is relied only on the search of resource with minimum power. Moreover, we design model free resource allocation based on RL approach differently from [5]-[10] for efficient energy consumption and better resource utilization with the aid of Fuzzy Logic in generating resource allocation decision. III. RESOURCE ALLOCATION IN CLOUD COMPUTING In general, the infrastructure resources (i.e. host, storage, and network capacity) in cloud data centers are virtualized and delivered to cloud users as according to the key enabling virtualization technology. This technology is the heart of the cloud computing and the high-level scalability, agility and availability of the cloud computing features is accomplished with sophisticated virtualization techniques. In addition, the resource allocation in this environment is thereby associated which infrastructure resources (Hosts) should be assigned and allocated for incoming VMs. In this paper, the energy efficient resource allocation framework is designed to enable automatically resource allocation in such a way to drop the TCO of the infrastructure providers by minimizing SLA violations while, at the same time, reducing the energy consumed by the physical infrastructure. This paper takes into account numerous aspects when designing resource allocation for this environment. They are (1) multiple infrastructure resources have to be dynamically virtualized and shared among cloud users for several independent demands, (2) virtualized infrastructure resources have to be dynamically allocate among cloud physical resources, (3) data center energy efficiency have to maintain through the consideration of the amount of energy absorbed by the physical resources of the infrastructure, (4) cloud users' SLA have to guarantee in the presence of timevarying demands (5) VM allocation have to monitor and reconfigure to avoid the performance degradation (6) data 68

3 center resource utilization have to improve between the underutilization and over-utilization criteria. The framework covering all these aspects is proposed in Fig. 1. This work relies on mechanisms provided by Reinforcement Learning for enabling the cloud infrastructure providers to allocate resource optimally under variable and unpredictable workloads. Due to the dynamic nature of resource demands and the complexity of the cloud environment, it is hard to set up a mathematical model for the energy efficient resource allocation strategy. This issue of the lack of accurate resource allocation model is addressed with the aid of model-free Reinforcement Learning (RL) approach. The unique property of RL is that it can automatically sense the environmental situations and perform the best suitable management policies without specific domain knowledge. Therefore it grantees the best effort to resource allocation problem in cloud environment where the user requests are dynamic and the providers are very complex to handle these requests in real time. A. Energy-efficient Resource Allocation Framework The energy-efficient resource allocation framework for cloud resource providers to distribute any cloud services satisfying cloud users SLA in energy saving manner is depicted in Fig. 1. The Cloud IaaS providers also attempt to meet specific IaaS SLA metrics including CPU capacity, Memory size, Storage, Availability, and Response Time. In this work, the infrastructure resources demands are also transparent as CPU utilization as the foremost SLA metric for all IaaS providers and the allocation is performed tradeoff resource utilization and cloud data center energy adjustments. The task performed by each component is the following. Cloud Users Energy-efficient Cloud Infrastructure Resources Allocation Framework Energy efficient Resource Allocator Service Level Agreement (SLA) Manager Date Center Energy Analyzer Cloud Infrastructure Resource Monitor Cloud Infrastructure Resources in Data Center Fig. 1 Energy-efficient Resource Allocation Framework Energy efficient Resource Allocator performs as the most essential component of the proposed framework. It selects the optimized host to allocate VMs assigned for the user request by considering the utilization and energy information of the current data center and facilitate the low energy, high utilization resource allocation decision. Service Level Agreement (SLA) Manager keeps track requirements of cloud users as SLA metric (CPU request) and inspects whether the current allocation decision violates SLA between the cloud resource providers and user. Data center Energy Analyzer monitors not only the power usage of cloud infrastructure resources (host, VMs) but also the energy consumption of the data center and supports the core information for the optimized resource allocation decisions. Cloud Infrastructure Resource Monitor collects the history of the CPU utilization pattern of the cloud infrastructure resources to provide energy efficient VM assignment across physical machines. It also monitors the actual usage of resources by VMs and accounts for the resource usage costs. B. Efficient Resource Allocation based RL approach Reinforcement Learning (RL) is a process of learning by interactions with dynamic environment, which generates the optimal control policy for a given set of states without requiring domain knowledge of the environment. Reinforcement learning is learning what to do, how to map situations to actions so as to maximize a numerical reward signal. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation and, through that, all subsequent rewards. These two characteristics: trial-and-error search and delayed reward are the two most important distinguishing features of reinforcement learning. Reinforcement learning is defined not by characterizing learning algorithms, but by characterizing a learning problem. One of the challenges that arise in reinforcement learning and not in other kinds of learning is the tradeoff between exploration and exploitation. Another key feature of reinforcement learning is that it explicitly considers the whole problem of a goal-directed agent interacting with an uncertain environment. Among many model-free reinforcement learning algorithms developed, this work stands on Q-learning, Off-Policy temporal-difference (TD) method (Watkins, 1989; Watkins and Dayan, 1992) [2]. The problem description of the resource allocation in cloud data center environment which intends to be addressed with reinforcement learning can be formulated as a sort of optimization problem in terms of Markov decision processes such that the aspects of the resource allocation problem while interacting the cloud data center environment to achieve the optimal decision. Energy efficient Resource Allocator, performed as the controller residing cloud data center environment, senses the state of the cloud data center environment to some extent and take actions to explore the level of implication of actions over the current state. 69

4 3rd International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2014) Feb , 2014 Singapore 1. State Space accomplish the optimal resource allocation according to the PUE standard defined by The Green Grid Consortium [11]. Another metric for data center is Data Center Infrastructure Efficiency (DCiE) which is the inverse of PUE, as described in In cloud data center environment, there are N physical machines (hosts) to serve as infrastructure resources for the cloud users and each host is formulated as a component of a state (s) and the state set of the environment is S={s1,s2,s3,,sN} accounting all hosts in general. Each state (si) for time t has four dimensions: current energy consumption of data center (et), candidate host in data center (ht), utilization of candidate host in million instruction per seconds (ut), and CPU demand of virtual machine in million instructions per seconds (vt). DCiE The possible actions for any candidate host can be either ALLOCATE or NOT-ALLOCATE according to the outcome of the composite consideration of the data center energy and current utilization of this host. Energy efficient Resource Allocator follows the trail-and-error search over the state space exhaustively. Algorithm: Energy efficient Resource Allocation Algorithm Input: host_list, total number of host created in data center, vm_list, total number of VMs to be created in host of the data center, workload, CPU demand in million instruction per seconds, γ, discount factor, L, specified episode for learning process. Output: optimize allocation of VMs. Method: 1 init(workload) //read user workload and initialize DC components 2 createhost(host_list) //create hosts in data center 3 createvm(vm_list) //create VMs in data center 4 createenvironment() //set up state set S, action set A and initialize Q values and R values 5 for each vm ϵ vm_list{ 6 for each host ϵ host_list{ 7 s0={et, ht, ut, vt} //assign initial state with energy of data center, host, VM demand 8 for each time step k = 0,1,2,...,L { 9 ak = a maxa Qk(sk,a ) //execute policy for current energy, utilization and choose action (host) with maximum value 10 apply ak, measure sk+1 //determine the value of the outcome action 11 reward rk+1 //calculate composite reward value for allocation with Fuzzy Logic Qk+1(sk,ak) r +[ γ maxa Qk(sk+1,a)] //update value of the policy 13 sk = sk+1 //allocate next host 14 } 15 } 16 return host; //select best reward host 17 allocate(host,vm); 18 } 19 return allocation. 3. Reward Signal At each time step, Energy efficient Resource Allocator selects the optimal decision according the outcome of the reward function, which is specified as a combination of the energy model and the utilization function by applying Fuzzy Logic Rules. The larger the value of the utilization function, the better the resource allocation for VM in this candidate host. The optimization criteria described in below are used in order to determine the host to allocate VM. Avability: The available resources (CPU, Memory, and Storage) in candidate host are evaluated in order to assign VM demand. Host Utilization: This criterion aims at the maximization of the candidate host CPU utilization so that the data center can be beneficial in both saving energy of the inactive host and gaining the maximum utilization. Data Center Engery Efficiency: The famous metric for measuring data center energy efficiency defined by The Green Grid [11] is mostly beneficial for the cloud providers in order to manage their data center power consumption: PUE (Power Usage Effectiveness), in [1]-[11] which is the ratio of total amount of power used by a data center facility to the power delivered to computing equipment, as in Total Facility Power IT Equipment Power 12 (2) C. Energy efficient Resource Allocation Algorithm The core component of the proposed framework is the Energy efficient Resource Allocator and the implementation of Allocator embedded with other components in the proposed framework are described as Energy efficient Resource Allocation Algorithm, presenting in Fig Action Space PUE IT Equipment Power Total Facility Power (1) The Total Facility Power is the power delivered entirely to the data center, while IT Equipments Power is referred to the energy facilities consumed by the equipment that is used to manage, process, store, or route data within the data. Our work emphasizes on the energy consumption of the cloud data center IT equipment and it can be observed that energy consumption of CPU (40%) are the most influenced factor in IT equipment cost of the data center [13]. Moreover, we Fig. 2 Proposed Energy efficient Resource Allocation Algorithm 70

5 IV. EXPERIMENTAL EVALUATION The proposed resource allocation algorithm is implemented as a new resource allocation optimization heuristic in CloudSim toolkit [7]. The components in the proposed framework are encapsulated as the form of extended heuristic in CloudSim for the purpose of evaluation. CloudSim is a scalable simulation framework that enables novel support for modeling, simulation, and experimentation of virtualized Cloud based data center environments and Cloud management services for VMs, memory, storage, and bandwidth with different capabilities, configurations, and domains. The features supported by CloudSim are the modeling and simulation of large scale Cloud computing environments including data centers, service brokers, resource provisioning and allocation policies, virtualization engine, network connections, federated Cloud environments from both private and public providers. Due to these collective features, CloudSim can be leveraged to easily construct new heuristics to evaluate the performance bottlenecks relating to service delivery and provisioning policies in resource management strategies. This section presents our experiments and evaluation using CloudSim toolkit. In addition, we simulate the nature of cloud data center environment in 7 days for the purpose of examining the how many SLAs are violated from cloud user perspectives, and how much data center energy efficiency maintained and how much overall resource utilization of hosts in data center are effective from cloud resource providers perspectives. A. Power Consumption We have created a data center with 200 heterogeneous host machines with two types of HP ProLiant ML110 G4 (Intel Xeon 3040, 2 cores-1860 MHz, 4 GB), and HP ProLiant ML110 G5 (Intel Xeon 3075, (2 cores-2660 MHz, 4 GB) with the power consumption characteristics shown in Fig. 3. The power consumption of the two machines in the idle state and at different CPU utilization levels, ranging from 0% to 100%, is published by SPECpower [14]-[15]. HpProLiantMl110G4Xeon3040 HpProLiantMl110G5Xeon3075 B. Evaluation Results The real workload traces provided from the PlanetLab Project in [4]-[6] is used in this evaluation where CPU utilization of VMs is extracted from 1175 nodes located at 564 sites around the world based on the data provided as a part of the CoMon project [6]. Among these traces, the CPU utilization of 200 VMs for 7 days is selected to perform experiments. The CPU utilization is a function of time and is represented as u(t) and the energy consumption of a host, referred as E in a period can be defined for host i ranging from 1 to total number of hosts N in data center as in, t1 E P( u( t)) dt, i 1,2,..., N. (3) i t00 Therefore, the total energy consumption by the data center is the summation of the energy consumption of all hosts in the data center and can be described as in, E N E i i 1 Fig. 4 reflects how the data center consumes energy for serving incoming requests from virtual machines (VMs) in all 7 days as four dimension of the number of VMs. Energy Consumption (kwh) Virtual Machines (VM) Fig. 4 Energy Consumption by Energy efficient Resource Allocation Algorithm (4) Power Consumption (Watts) CPU Utilization (%) Fig. 3 Power consumption variations for Hosts It can be seen in Fig. 4 that the increasing number of VMs, the energy consumption level of data center is also increased. Conversely, our emphasis metric PUE is gradually reducing to reflect the goal of better data center energy efficiency when more energy use for infrastructure resources depicted in Fig. 5. According to standard PUE value [11], the PUE of 1 is very efficient and range from 1 to 2 is efficient, range from 2 to 3 is inefficient, above 3 is not good indicator. Therefore, our algorithm promotes the data center energy efficiency level through the investigation of Fig

6 Power Usage Effectiveness (PUE) Fig. 5 PUE metric by Energy efficient Resource Allocation Algorithm The DCiE metric of our proposed approach evaluation is depicted in Fig. 6. We can seen that as PUE of our approach is within the range from efficient to average, the effective DCiE is also produced by our approach from 48% to 58% level. VIrtual Machines (VM) % 49% 50% 51% 52% 53% 54% 55% 56% 57% Data Center Infrastructure Efficiency (DCiE) Fig. 6 DCiE metric by Energy efficient Resource Allocation Algorithm The overall host CPU utilization for a data center is based upon the active running hosts for any service in data center and these utilization results are depicted in Fig. 7. Host CPU Utilization (%) Fig. 7 Overall Host Utilization by Energy efficient Resource Allocation Algorithm The threshold value for CPU utilization of a host in our approach is determined from lower utilization threshold-30% and higher utilization threshold-80%. From Fig. 7, for any number of VMs in 7 days we can conclude that our algorithm utilizes data center infrastructure sources efficiently, having the above utilization of 50% in general. The SLA violation in Fig. 8 of our approach is performed by using the SLA measure metric published by [3] implemented in CloudSim. When the number of VM increases, it is more SLA violations as the VM size increasing may lead to more processing time in Fig. 9 and the average response time of service also decreases. SLA Violations (%) Fig. 8 Overall SLA Violation by Energy efficient Resource Allocation Algorithm Because of the curse of the dimensionality of Reinforcement Learning nature, the more VMs to serve, the more exploration on cloud data center environment to allocate and the high processing time to give decision. This nature is highlighted in Fig. 9 which is the execution time of our approach. Execution Time (seconds) Fig. 9 Processing Time by Energy efficient Resource Allocation Algorithm 72

7 V. CONCLUSION This paper set up energy efficient resource allocation framework which can encourage for both cloud providers and cloud users for allocating cloud infrastructure resources while achieving the high energy efficiency and preventing SLA violation respectively with the aim of green cloud resources deployment. Through the thorough analysis of the evaluation results, although the our resource allocation algorithm augments the energy efficiency of the cloud data center and their infrastructure resources utilization and attain our target objectives, it introduces more complexities as much as the number of infrastructure resources in data center, precisely in Processing time, SLA violations. Therefore, the direction of the future research is to tackle these issues by the intelligent scheme for any size of cloud infrastructure demands. [15] REFERENCES [1] L. A. Barroso and U. Hölzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machine, M.D. Hill, Morgan & Claypool Publishers Ed., 2009, ch. 5. [2] R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning, 1st ed., MIT Press Ed., Cambridge, MA, USA, 1998, ch. 1, 6. [3] A. Beloglazov, J. Abawajy and R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing, Journal of Future Generation Computer Systems, Vol. 28, Issue 5, pp , [4] A. Beloglazov, R. Buyya, "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers," Journal of Concurrency and Computation: Practice & Experience archive, Vol. 24 Issue 13, pp , September [5] G. Tesauro, N. K. Jong, R. Das and M. N. Bennani, On the use of hybrid reinforcement learning for autonomic resource allocation, Journal of Cluster Computing, Vol. 10, Issue 3, pp , [6] K. S. Park, and V. S. Pai, CoMon: a mostly-scalable monitoring system for PlanetLab, in ACM SIGOPS Operating Systems Review, pp , [7] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. D. Rose and R. Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning algorithms, Journal of Software: Practice and Experience, Vol. 41, Issue 1, pp , [8] L. Liu, H. Wang, X. Liu, X. Jin, W. B. He, Q. B. Wang, and Y. Chen, GreenCloud: A New Architecture for Green Data Center, in Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, ICAC- INDST '09, ACM New York, USA, pp , [9] 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 Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2010, Las Vegas, USA, [10] X. Dutreilh, S. Kirgizov, O. Melekhova, J. Malenfant, N. Rivierre and I. Truck, Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow, ICAS 2011, in The Seventh International Conference on Autonomic and Autonomous Systems, pp. 67, [11] H. Barrass, C. Belady, S. Berard, M. Bramfitt, T. Cader, H. Coles, J. Cooley, L. Coors, T. Darby, J. Froedge, N. Gruendler and J. Haas, PUE : A Comprehensive Examination Of The Metric, The Green Grid Association, [12] U.S. Environmental Protection Agency, Report to Congress on Server and Data Center Energy Efficiency Public Law , [13] G. Koutitas, Green Datacenters: Green ICT of the MSc in ICT Systems, International Hellenic University, Turin, [14] 73