Optimizing of Resource Allocation in Cloud Computing for High Utilization

Transcription

1 Optimizing of Resource Allocation in Cloud Computing for High Utilization J.Rajani, C.Vasu Murthy, S.G.Nawaz Abstract Cloud computing has been around for some years as a new programming model that enables commodity computing. Without capital investment people and organizations can access huge amount of computing resources in pay per use fashion. Cloud provides various services including storage. While cloud users are being served, the resources of cloud are utilized as per the requirements. However, the resource utilization may experience skewness which needs to be avoided. Many algorithms came into existence in order to overcome this problem. In this paper a new algorithm is proposed for optimal resource allocation in cloud computing for high utilization of resources. Our algorithm considers two cases to act up on. In the first case when VM has scarcity of resources, its capacity gets second case makes use of a technique in which a VM is migrated from one physical machine to another available. We built a prototype web application that demonstrates the proof of concept. The application simulates the two cases proposed in the algorithm with visual clues. The empirical results reveal that the proposed algorithm is useful and can be used in real cloud computing applications. Index Terms Cloud computing, virtualization, load balancing, optimal resource allocation I. INTRODUCTION Cloud computing has been around for number of years. However, recently it became popular and made affordable. Its popularity can be understood in terms of the technology virtualization. The VM is a machine inside the machine which is software component that mimics as another machine. This way one machine can have multiple VMs to cater to the needs of thousands of cloud users. This will reduce the investment from the cloud service provider point of view. With fewer resources physically available, more users can be served. Thus the cloud computing became an affordable solution. However, dynamic resource allocation is an open problem in cloud computing to be addressed. It is very challenging job since it needs optimizing resource allocation and thus achieving high resource utilization. When more resources are allocation, it results in resource wastage. When fewer resources are allocated, it results in scarcity. To strike balance between these two, it is challenging to allocation resources dynamically. Many solutions came into existence to solve this issues in cloud computing. For instance MUSE [1] where replicas are maintained for optimal performance; load balancing strategies in [2]; integrated load dispatching approach in [3]; delay scheduling [4], HARMONY where multiple resource layers are used [5]; and skewness algorithm for dynamic resource allocation [6]. These researchers followed different strategies to solve the same problem. However, we follow different solution as proposed in this paper. Our solution has two cases. In the first case when VM has scarcity of resources, its capacity gets increased by assigning idle resources to VM or assigning free memory of physical machine. The second case makes use of a technique in which a VM is migrated from one physical machine to another physical machine where abundant resources are available. Our contributions in the paper include: Architecture is proposed to accommodate dynamic and optimal resource allocation. Al algorithm is proposed to facilitate dynamic resource allocation. A prototype application is built to demonstrate the proof of concept. The remainder of the paper is structured as follows. Section II provides review of literature that exists about the prior works on dynamic resource allocation problem. Section III presented the proposed architecture. Section IV provides the algorithm proposed. Section V presents experimental results while section VI concludes the paper. II. RELATED WORKS Data centers play crucial role in the successful implementation of cloud computing technology. At all levels resources are taken from the cloud data All Rights Reserved 2014 IJARCSEE 433

2 centers. There was focus on this aspect for [2] and [1] for dynamic resource allocation. The load on the resources is to be studied and the resource allocation needs to be done optimally. In [3] there was a study on server provisioning and also load dispatching without using virtual machines concept. They were also not done in multi-tier or distributed environment. Distributed computing allows multiple servers to involve in solving problems and cloud computing works in distributed environment. In [6] a solution is provided for resource allocation and its optimization using Amazon EC2-style cloud environment and experiments were made to this effect to demonstrate the optimization of resource allocation. The researcher used the concept of VIRTUAL MACHINES and the resource allocation to them. MapReduce, a new programming paradigm, was used in [7] in a distributed programming environment. Job scheduling and cost minimization was explored in [8]. In [4] a new approach is scheduling known as delay scheduling wasintroduced for local data processing effectively. The concept of dynamic priorities was introduced in [9] which targets dynamic resource allocation. The concept of VM migration was explored by many researchers as explored in [10], [11] and [5]. Their approach makes use of a VM that can simulate like a physical machine. It is a machine within another machine that mimics a physical machine and supports OS installation and users online. Load balancing was studied in [12] which have been around for number of years. However, it is used with cloud computing in the presence of virtual machines. In this paper we proposed an algorithm that demonstrates the concept of VM capacity extension and VM live migration towards optimal utilization of cloud resources. III. NEW APPROACH FOR DYNAMIC RESOURCE ALLOCATION Virtualization is the technology that provides affordable cloud computing solutions. Virtual Machines concept plays an important role as one physical machine can act as thousands of virtual machines and they can have their own OS and other applications, users and so on. This wonderful technology enables cloud computing at all levels to have affordable solution to resources that can be shared across the globe in pay per use fashion. Figure 1 shows an overview of the proposed architecture for optimizing resource allocation from a data center point of view. Figure 1 Architectural overview of the proposed system As the architectural diagram shows, there are virtual machines allocated to various physical machines. The physical machines have real resources required by computing needs of users while the VMs utilize the resources of PM so as to enable users to have affordable cloud solutions. As one physical resource is expanded by using multiple virtual machines, the virtualization is great technology on top of which cloud computing works. In this context, the proposed algorithm works. The architecture reveals that there are two possible solutions to resource optimization. They are VM capacity increment or VM live migration. These two are considered as two cases in the solution proposed in this paper. In the first case available. All Rights Reserved 2014 IJARCSEE 434

3 Figure 2 Live VM migration As shown in Figure 2, the VM3 of physical machine 1 is migrated to physical machine 2 as per the runtime dynamics according to the proposed algorithm which is described in ensuing section. IV. PROPOSED ALGORITHM The proposed algorithm is meant for maximizing resource utilization so as to optimize the dynamic resource allocation problem in cloud computing. The algorithm is based on the architecture proposed earlier in this paper. The goal of the algorithm is to ensure high utilization of resources while the resources are not wasted. The algorithm takes resources available as input and allocate them optimally. Thus a scalable solution is achieved. Figure 1 Algorithm for optimal resource allocation All Rights Reserved 2014 IJARCSEE 435

4 As shown in Figure 1, it is evident that the algorithm has two distinct cases to be considered. The first case is related to scarcity of resources with a VM and the solution is VM capacity extension while the second case is also VM having less resources but the solution here is VM live migration i.e., migrating VM from one PM to another PM. In the first case when VM has scarcity of resources, its capacity gets increased by assigning idle resources to VM or assigning free memory of physical machine. The second case makes use of a technique in which a VM is migrated from one physical machine to another physical machine where abundant resources are available. V. EXPERIMENTAL RESULTS In order to demonstrate the dynamic resource allocation, we built a custom simulator which is a prototype to demonstrate the proof of concept. The environment used for simulation study include JDK 1.7, Tomcat 7.0, MY SQL, Chrome running in Windows 7 machine with 4 GB RAM, core 2 dual processor. The application demonstrates the two cases of the proposed algorithm. In the first case available. Figure 3 Dynamic resource allocation As seen in Figure 3, it is evident that resource allocation could be possible with physical machine 1 as indicated in green color VM. The resource allocation is done for a VM on physical machine 1. Figure 4 Dynamic resource allocation Figure 2 Dynamic resource allocation As seen in Figure 2, it is evident that resource allocation could not be possible with physical machine as indicated in red color VMs. The resource allocation is done for a VM on physical machine 2. As seen in Figure 4, it is evident that VM migration took place. Initially there were 3 VMs with first physical machine. As one of the VMs experienced over-commitment and there was no possibility of extending resources for VM, it is migrated to other physical machine where resources are available. The migrated VM is shown in green color. VI. CONCLUSION AND FUTURE WORK In this paper we studied the dynamic resource allocation problem in cloud computing. Though cloud is supported by virtualization for affordable All Rights Reserved 2014 IJARCSEE 436

5 resource utilization, it needs to be optimized. The optimal resource allocation is a challenging job. Many techniques came into existence to solve this problem. In this paper a new algorithm is proposed for optimal resource allocation in cloud computing for high utilization of resources. Our algorithm considers two cases to act up on. In the first case available. We built a prototype web application that demonstrates the proof of concept. The application simulates the two cases proposed in the algorithm with visual clues. The empirical results reveal that the proposed algorithm is useful and can be used in real cloud computing applications. REFERENCES 1. J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle, Managing energy and server resources in hosting centers, in Proc. Of the ACM Symposium on Operating System Principles (SOSP 01), Oct C. Tang, M. Steinder, M. Spreitzer, and G. Pacifici, A scalable application placement controller for enterprise data centers, in Proc. Of the International World Wide Web Conference (WWW 07), May G. Chen, H. Wenbo, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, Energy-aware server provisioning and load dispatching for connection-intensive internet services, in Proc. of the USENIX Symposium on Networked Systems Design and Implementation (NSDI 08), Apr M. Zaharia, D. Borthakur, J. Sen Sarma, K. Elmeleegy, S. Shenker, and I. Stoica, Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling, in Proc. of the European conference on Computer systems (EuroSys 10), heterogeneous environments, in Proc. of the Symposium on Operating Systems Design and Implementation (OSDI 08), M. Isard, V. Prabhakaran, J. Currey, U. Wieder, K. Talwar, and A. Goldberg, Quincy: Fair scheduling for distributed computing clusters, in Proc. of the ACM Symposium on Operating System Principles (SOSP 09), Oct T. Sandholm and K. Lai, Mapreduce optimization using regulated dynamic prioritization, in Proc. of the international joint conference on Measurement and modeling of computer systems (SIGMETRICS 09), T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif, Black-box and gray-box strategies for virtual machine migration, in Proc. Of the Symposium on Networked Systems Design and Implementation (NSDI 07), Apr N. Bobroff, A. Kochut, and K. Beaty, Dynamic placement of virtual machines for managing sla violations, in Proc. of the IFIP/IEEE International Symposium on Integrated Network Management (IM 07), Y. Toyoda, A simplified algorithm for obtaining approximate solutions to zero-one programming problems, Management Science, vol. 21, pp , august A.Singh, M. Korupolu, and D. Mohapatra, Serverstorage virtualization: integration and load balancing in data centers, in Proc. of the ACM/IEEE conference on Supercomputing, M. Zaharia, A. Konwinski, A. D. Joseph, R. H. Katz, and I. Stoica, Improving MapReduce performance in All Rights Reserved 2014 IJARCSEE 437