Cost Effective Selection of Data Center in Cloud ... - IRD India

Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)migration of virtual machines. A load balancing modelis designed and implemented to reduce virtualmachines’ migration time by shared storage, to balanceload amongst servers according to their processor or IOusage, etc. and to keep virtual machines’ zero-downtimein the process. A distributed load balancing algorithmCOMPARE AND BAL-ANCE is also proposed that isbased on sampling and reaches equilibrium very fast.This algorithm assures that the migration of VMs isalways from high-cost physical hosts to low-cost hostbut assumes that each physical host has enough memorywhich is a weak assumption.E. Event-drivenV. Nae et al. presented an event-driven loadbalancing algorithm for real-time Massively MultiplayerOnline Games (MMOG). This algorithm after receivingcapacity events as input, analyzes its components incontext of the resources and the global state of the gamesession, thereby generating the game session loadbalancing actions. It is capable of scaling up and down agame session on multiple resources according to thevariable user load but has occasional QoS breaches.Metrics for Load Balancing In CloudsVarious metrics considered in existing loadbalancing techniques in cloud computing are discussedbelowThroughput is used to calculate the no. of tasks whoseexecution has been completed. It should be high toimprove the performance of the system.Overhead Associated determines the amount ofoverhead involved while implementing a load-balancingalgorithm. It is composed of overhead due to movementof tasks, inter-processor and inter-processcommunication. This should be minimized so that a loadbalancing technique can work efficiently.Fault Tolerance is the ability of an algorithm to performuniform load balancing in spite of arbitrary node or linkfailure. The load balancing should be a good faulttoleranttechnique.Migration time is the time to migrate the jobs orresources from one node to other. It should beminimized in order to enhance the performance of thesystem.Response Time is the amount of time taken to respondby a particular load balancing algorithm in a distributedsystem. This parameter should be minimized.Resource Utilization is used to check the utilization ofre-sources. It should be optimized for an efficient loadbalancing.Performance is used to check the efficiency of thesystem. This has to be improved at a reasonable cost,e.g., reduce task response time while keeping acceptabledelays.IV. INTRODUCTION TO CLOUD ANALYSTGUI Package - It is responsible for the graphical userinterface, and acts as the front end controller for theapplication, managing screen transitions and other UIactivities.Simulation - This component is responsible for holdingthe simulation parameters, creating and executing thesimulation.UserBase - This component models a user base andgenerates traffic representing the users.DataCenterController - This component controls thedata center activities.Internet - This component models the Internet andimplements the traffic routing behavior.InternetCharacteristics - This component maintains thecharacteristics of the Internet during the simulation,including the latencies and available bandwidthsbetween regions, the current traffic levels, and currentperformance level information for the data centers.VmLoadBalancer - This component models the loadbalance policy used by data centers when servingallocation requests. Default load balancing policy uses around robin algorithm, which allocates all incomingrequests to the available virtual machines in round robinfashion without considering the current load on eachvirtual machine. Additionally, it is possible applicationof a throttled load balancing policy that limits thenumber of requests being processed in each virtualmachine to a throttling threshold. If requests arereceived causing this threshold to be exceeded in allavailable virtual machines, then the requests are queueduntil a virtual machine becomes available.CloudAppServiceBroker This component models theservice brokers that handle traffic routing between userbases and data centers. The default traffic routing policyis routing traffic to the closest data center in terms ofnetwork latency from the source user base.V. PERFORMANCE ANALYSISWe had used the cloud analyst tool to evaluate thealgorithms round robin, and throttled algorithm for thecase closest data center by using g user base (1-3) withdifferent regions & data centers (1-4) with differentvirtual machine monitors.3ISSN (Print) : 2319 – 2526, Volume-2, Issue-1, 2013

Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)A. User BaseThe design model use the user base to represent thesingle user but ideally a user base should be used torepresent a large numbers of users for efficiency ofsimulation.VI. RESULTB. DatacenterFig. 3 : Simulation ConfigurationDatacenter manages the data management activitiesvirtual machines creation and destruction and does therouting of user requests received from user base via theinternet to virtual machines.After performing the simulation the result computedby cloud analyst is shown in following below figures.We have used the above defined configuration for eachload balancing policy one by one and depending(Response Time and Processing Cost Using RoundRobin)4ISSN (Print) : 2319 – 2526, Volume-2, Issue-1, 2013

Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)VIII. REFERENCES(Response Time and Processing Cost Using Throttled )Fig. 4 : Comparison of Load Balancing Algorithm)Comparing with the table and graph, overallresponse time and data centre processing time isimproved. It is also seen that the virtual machine costand data transfer time in the round robin algorithm ismuch better when compared to throttled algorithms.VII. CONCLUSIONSThe response time and data transfer cost is achallenge of every cloud engineer to build up theproducts that can increase the business performance inthe cloud industry adopted sector. Several strategies lackefficient scheduling and load balancing resourceallocation techniques leading to increased operationalcost and give customer dissatisfaction. The paper tries togive a bird’s eye view on enhanced strategies throughimproved job and load balancing resource allocationtechniques.[1] Anthony T.Velte, Toby J.Velte, Robert Elsenpeter,Cloud Computing A Practical Approach, TATAMcGRAW-HILL Edition 2010.[2] Bhathiya, Wickremasinghe. Cloud Analyst: A CloudSim-based Visual Modeller for Analysing CloudComputing Environments and Applications[3] Chhabra, G. Singh, Qualitative Parametric Comparisonof Load Balancing Algorithms in DistributedComputing Environment,14th International Conferenceon Advanced Computing and Communication, July2006 IEEE, pp 58 – 61.[4] Fang Y., Wang F. and Ge J. (2010) Lecture Notes inComput-er Science, 6318, 271-277.[5] Hu J., Gu J., Sun G. and Zhao T. (2010) 3rdInternational Symposium on Parallel Architectures,Algorithms and Pro-gramming, 89-96.[6] Mehta H., Kanungo P. and Chandwani M. (2011)International Conference Workshop on EmergingTrends in Technology, 370-375[7] Nae V., Prodan R. and Fahringer T. (2010) 11thIEEE/ACM International Conference on GridComputing (Grid), 9-17.[8] Ram Prasad Padhy (107CS046), P Goutam Prasad Rao(107CS039).Load balancing in cloud computingsystem Department of Computer Science andEngineering National Institute of Technology,Rourkela Rourkela-769 008, Orissa, India May, 2011.[9] Randles M., Lamb D. and Taleb-Bendiab A. (2010)24th Inter-national Conference on AdvancedInformation Networking and Applications Workshops,551-556[10] T.R.Gopalakrishnan Nair Vaidehi .M2 Suma. VImproved Strategies for Enhanced BusinessPerformance in Cloud based IT Industries, Research &Industry Incubation Centre, DSI, Bangalore, India.[11] Wenhong Tian, Yong Zhao, Yuanliang Zhong,Minxian Xu, Chen Jing(2011) , A Dynamic AndIntegrated Load balancing Scheduling Algorithm ForCloud Datacenters, University of Electronic Scienceand Technology[12] Zhao Y. and Huang W. (2009) 5th International JointConfer-ence on INC, IMS and IDC, 170-175.5ISSN (Print) : 2319 – 2526, Volume-2, Issue-1, 2013