Enhancing the Scalability of Virtual Machines in Cloud

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1 Enhancing the Scalability of Virtual Machines in Cloud Chippy.A #1, Ashok Kumar.P #2, Deepak.S #3, Ananthi.S #4 # Department of Computer Science and Engineering, SNS College of Technology Coimbatore, Tamil Nadu, India Abstract- In cloud computing, the overloaded hosts are managed by allocating the Virtual Machines from that host to another. This increases the resource utilization. The proposed model introduces a load balanced model for the cloud based on the dynamic resource allocation concept with a switching technique to select different methods for different scenarios. The algorithm applies the game theory for implementing resource allocation strategy to improve the efficiency of the cloud environment. Index Terms- Cloud computing, load balancing, green computing, overload detection. I. INTRODUCTION Cloud computing has become an increasingly popular model in which computing resources are made available on-demand to the user as required. The unique value of cloud computing creates new opportunities to align IT and business motives. Cloud computing works only with the help of internet for delivering IT- Enabled capabilities as a service to any needed users i.e. through cloud computing we can access anything that we want from anywhere to any computer without worrying about anything like about their storage, cost, management and so on. Clouds are large pools of easily usable and accessible virtualized resources. These resources can be dynamically scaled up or down to adjust to a different load, allowing maximum utilization of resource. It s a pay-per-use model in which the service Provider with the help of Service Level Agreements (SLAs) provides a pool of computing resources. Any organizations and individuals can be benefited from this mass computing and storage centers, provided by large companies with stable and strong cloud facilities. The concept behind cloud computing is virtualization. On-demand deployment, Internet delivery of services, and open source software is the important characteristics of cloud computing. From one point of view, cloud computing is nothing new because the concepts used in it is existing. From other view, cloud computing is new because of its flexibility, update-ability, deployment techniques. The applications and its information in cloud computing are maintained and updated with the help of internet and remote servers. For using any application from cloud computing the users need not install it in their physical device. They can access their files from any device with access to internet. Cloud computing provides more efficient computing techniques by increased bandwidth, memory, storage and security for files [1]. The cloud has a great impact on businesses of all sizes-from small and midsized businesses to large enterprisesand it s showing no signs of slowing down. There are three cloud service models. IaaS provides the entire infrastructure for computing such that the users need not worry about hardware, power and cooling system to protect this hardware. Computer resources can be provisioned on demand as a utility. PaaS takes us to the next level in the stack it provides the operating system, database, ISSN: Page 208

2 application server, and programming language for developing a software or application. SaaS is the next level in the stack. SaaS provides the application or service through internet connection. In this service model, the consumer only needs to focus on administering users to the system. Load balancing is an efficient method for distributing workloads across various computing resources. Load balancing focuses on increasing response time, throughput and to overcome overloading of any resources [2]. More work can be executed in minimum amount of time when deploying load balancing. Load balancing is the process of dividing the loads between n numbers of computers which helps in performing some operation efficiently. Because of this all users get served faster. Load balancing can be implemented with hardware, software, or a combination of both. Typically, load balancing is the main reason for computer server clustering. In this paper we propose a model for avoiding the overloading of the servers by load balancing. The idle servers that is which do not have any virtual machines running on it can be turned off or made to go to sleep mode thus saving energy. data locality while keeping fairness among different jobs [5]. Dynamic priorities to jobs and users were assigned to achieve resource allocation [6]. Live migration of VM is used for dynamic resource allocation. Sandpiper sorts the list of PMs based on their volumes and the VMs in each PM in their volume to size ratio [7]. It abstracts away critical information needed when making the migration decision and considers the PMs and the VMs in the presorted order. Another method in which it uses VM and data migration to mitigate hot spots not just on the servers, but also on network devices and the storage nodes as well [8]. A method using skewness was also used for resource allocation dynamically [9]. This measures the uneven utilization of resources on a server. Load prediction algorithm is used to identify the hotspots and the cold spots. Hotspots occur when servers are overloaded. Cold spot is when the servers are in idle state without performing any operation. The algorithm then migrates the VMs from the servers coming under the category of hotspot to server in idle state. It helps in dynamic resource allocation of resources. II. RELATED WORKS Dynamic resource allocation of web based applications was already carried out. The web applications were scaled automatically. Each server has the copies of all the web applications in the system in MUSE [3]. Some resource allocation methods were based on network flow algorithms to allocate the load of an application [4]. Quincy adopts min-cost flow model in task scheduling to maximize III. PROPOSED MODEL In the system architecture each physical machine runs the Virtual Machine Monitor such as Xen hypervisor. The virtual machine contains more number of applications running in it. There is backend storage for these physical machines. The interoperability of virtual machines to physical machines are being managed. Every physical machine has a local node manager. This local node manager is used for collecting the resource ISSN: Page 209

3 utilization levels of all the virtual machines running in that physical machine. The memory, storage and bandwidth usage can be analyzed using the scheduling techniques used in the Virtual Machine Monitor. The utilization of memory is not being identified by the hypervisor. This can be managed by identifying the storage shortage in virtual machine. The information gathered at each physical machine is send to the controller which is responsible for the scheduling in the virtual machines. The local node manager invokes the scheduler in virtual machines regarding the history of demand, load of physical machines. A predictor is used for foretelling the resource demands of virtual machines and in identifying the loads in the physical machines based on previous analysis. The physical machines load is calculated by monitoring the resource utilization of the virtual machines. The local node manager tries to meet all the demands by allocating the virtual machines which has mutual sharing of same Virtual Machine Monitor. The hypervisor can replace the CPU allocation between virtual machines by altering the weights in the scheduler. The virtual machine scheduler has hot spot predictor. It monitors whether the resource utilization of physical machine has gone above the threshold. If any occurs, then any virtual machines running in the physical machine is migrated for reducing the load of the physical machine and increasing its performance. The scheduler also has a cold spot identifier. This is used for checking the average or normal utilization of active physical machines is below the threshold or not. If any occurs, then these physical machines can be moved to shut down mode by moving all its virtual machines. This migration list is then forwarded to the controller by the local node manager. In order to identify the future resource requirements of virtual machines, it is necessary to view the application level usage of the virtual machines. For performing this needs modification of the virtual machine. This is a tedious process. Another approach is to identify the previous activities of the virtual machines. The CPU loads on the physical machines are determined as discussed previously. A. MITIGATION SPOTS Fig.1 Handling hot spot and cold spot The algorithm calculates the resource utilization in all the physical machines. It also evaluates the resource allocation depending on the calculated future resource demands of the virtual machines. A server or a physical machine is being described as a hot spot only when the resource utilization is above the threshold as described above. This means that the server or physical machine is in overload state and number of virtual machines running in it more. This leads to result that these virtual machines should be migrated to any other physical machine having same hypervisor. Similarly a server or physical machine is being described as a cold spot only when the resource utilization is below the threshold, as the name implies. This means that the server or physical machine ISSN: Page 210

4 is not performing any operation; else to say in simple terms the server or the physical machine is in idle state. Such nodes can be made to move to sleep mode. A node is said to be in active state if it has one virtual machine running (minimum). Finally a server or the physical machine is said to have a warm spot when the level of resource utilization is high to have the server performing its application and not too high to change into hot spot, which will in turn affect the resource demands. These thresholds vary according to various types of resources. Consider the instance, the threshold for CPU usage be defined as 85%. Then it becomes a hot spot if it goes beyond 85%. When a hot spot is identified in the system it must be migrated. These hot spots are listed according to the temperature i.e., the hottest is first handled. Trying to remove all the hot spots are not possible, atleast their temperature must be brought down. While migrating the virtual machines, initially which virtual machine is to be migrated must be decided. If any ties occur then the virtual machine which can minimize the uneven resource utilization is being selected. All the virtual machines to be migrated are stored in a list. For the stored virtual machines, availability of destination is checked. It is also to be noted that on migrating the virtual machine from a hot spot server, the receiving server must not change to hot spot. Such servers are identified and updated. Destinations are found for all such virtual machines in the list. This overcomes the overloaded state of the server or the physical machine. B. ENERGY CONSERVATION When a server or physical machines is in cold spot as mentioned earlier, the virtual machines in that node is migrated to some other active servers. Then that server or the physical machine is switched off. This can help a lot in energy conservation. This is achieving green computing. The main goal in green computing is to minimize the count of servers in the active state which is not having a load or not performing any operation currently or in future. Similar to the list of virtual machines in hot spot server, a list is maintained here also which is the exact opposite of the previous list. Here the list is sorted with minimum temperature. The virtual machines in cold spot servers are assigned a new destination. These destinations are selected in such a way that these must be in warm spot state. Thus the energy is saved even to a accepting level. C. GAME THEORY As mentioned earlier, the virtual machines are to be migrated if the server is in hot spot or in cold spot. The virtual machines are migrated from hot spot server in order to avoid or overcome overloading. Similarly the virtual machines are migrated from cold spot server in order to save energy and achieve green computing by turning off the idle servers. These migrations are carried out based on the game theory strategies. Each server is considered as the players and all have given equal priorities [10]. One server takes an action and all other available servers react accordingly. If a server goes to hot spot state then it tries to manage the overload or checks for the nearby server availability. Various servers are selected for virtual machine migration. The most nearer server and the server with warm threshold is selected. The server for migrating the virtual machine does not wait for monitoring any other server s ISSN: Page 211

5 activities and just migrate its virtual machine to the server with warm threshold. IV. CONCLUSION Overloading is the one of the important issue. It must be managed efficiently to achieve better performance. All the resource demands must be meet at the required time. If overloading occurs then this is not possible. Load balancing is an important factor in meeting resource demands. Various other techniques can be used for load balancing. Here the activities of all the servers are monitored continuously. Identifying hot spot, cold spot and warm spot is an important process. Analyzing these spots helps a lot in migrating virtual machines and in deciding which virtual machines to be migrated to which destination. [8] A. Singh, M. Korupolu, and D. Mohapatra, Server-storage virtualization: integration and load balancing in data centers, in Proc. of the ACM/IEEE conference on Supercomputing, [9] Zhen Xiao, Weijia Song and Qi Chen, Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment IEEE Transaction on Parallel and Distributed Systems [10] Nageswara S.V. Rao, Chris Y. T. Ma, Cloud Computing Infrastructure Robustness: A Game Theory Approach International Conference on Computing, Networking and Communications(ICNC) REFERENCES [1] Pankaj Arora, Rubal Chaudhry Wadhawan, Er. Satinder Pal Ahuja Cloud Computing Security Issues in Infrastructure as a Service International Journal of Advanced Research in Computer Science and Software Engineering Volume 2, Issue 1, January [2] Anton Beloglazov and Rajkumar Buyya, Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints IEEE Transactions on Parallel and Distributed Systems, Vol.24, No.7, July [3] 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 [4] 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 [5] 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 [6] 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), [7] 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 ISSN: Page 212

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