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  1. 1. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Exploiting Dynamic reserve provision for Proficient analogous Data dispensation in the Cloud S. Shabana1, R. Priyadarshini2, R. Swathi3, A. Dhasaradhi4, 1, 2 M.Tech Student, 3, 4Asst. Prof, 1, 2, 3, 4 Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, Andhrapradesh, India, 1 sshabana.mtech@gmail.com.Abstract— In recent years ad-hoc analogous data dispensation processing structure then takes care of distributing thehas emerged to be one of the killer applications for program in the middle of the presented nodes and executesInfrastructure-as-a- Service (IaaS) clouds. Foremost Cloud each case in point of the program on the proper section ofcomputing organizations have ongoing to amalgamate data.frameworks for analogous data dispensation in their product For companies that only have to procedure large amountsassortment, manufacture it easy for clients to admission these of data infrequently running their own data middle isservices and to deploy their programs. Conversely, thedispensation frameworks which are presently worn have been noticeably not an selection. Instead, Cloud computing hasdesigned for inert, standardized gather setups and disrespect emerged as a talented come within reach of to rent a large ITthe scrupulous scenery of a cloud. Consequently, the owed infrastructure on a short-term pay-per-usage basis. Operatorscompute possessions may be derisory for big parts of the of so-called Infrastructure-as-a-Service (IaaS) clouds, likesubmitted job and gratuitously increase dispensation time and Amazon EC2 [1], let their clientele allocate, right to use, andcost. be in charge of a set of virtual machines (VMs) which run In this paper we converse the opportunities and challenges surrounded by their data centers and only charge them for thefor proficient analogous data dispensation in clouds and current period of time the machines are billed. The VMs are naturallyour research project Nephele. Nephele is the first data offered in dissimilar types, each type with its owndispensation framework to unambiguously utilize the dynamicresource allowance existing by today’s IaaS clouds for both, individuality (number of CPU cores, quantity of maintask preparation and implementation. Scrupulous errands of a remembrance, etc.) and charge.dispensation job can be assigned to dissimilar types of effective Since the VM concept of IaaS clouds fits the architecturalequipment which are automatically instantiated and concluded pattern unspecified by the data processing frameworksduring the job finishing. Based on this new scaffold, we achieve described above, projects like Hadoop [3], a popular openextensive evaluations of MapReduce-inspired dispensation jobs source completion of Google’s Map Reduce framework,on an IaaS cloud system and evaluate the results to the popular previously have begun to sponsor using their frameworks indata dispensation framework Hadoop. the obscure [4]. Only in recent times, Amazon hasKeywords:-cloud computing, Nephele incorporated Hadoop as one of its interior infrastructure services [2]. However, as a substitute of acceptance its I. INTRODUCTION energetic resource allowance, current data dispensation Now a day’s number of companies is growing and has to frameworks rather anticipate the obscure to emulate the staticprocess huge amounts of data in a cost-efficient manner. nature of the bunch environments they were formerlyCommon legislature for these companies is operators of designed for. E.g., at the instant the types and numeral ofInternet search engines, like Google, Yahoo, or Microsoft. VMs allocated at the commencement of a work out jobThe huge amount of data they have to contract with every day cannot be tainted in the course of giving out, although thehas complete conventional database solutions prohibitively tasks the job consists of might have totally different demandsexpensive [1]. As an alternative, these companies have on the atmosphere. As a result, rented possessions may bepopularized an architectural standard based on a large insufficient for big parts of the handing out job, which maynumber of articles of trade servers. Problems like allowance lower the overall giving out presentation and raise the cost.crawled documents or regenerating a web directory are In this paper we want to talk about the exacting challengesopening into several autonomous subtasks, distributed and opportunities for efficient parallel data processing inamong the available nodes, and computed in similar. clouds and current Nephele, a new dispensation framework In order to shorten the advance of dispersed applications explicitly designed for cloud environments. Moston top of such architectures, many of these companies have remarkably, Nephele is the first data dispensationalso built personalized data giving out frameworks. They can frame-work to include the opportunity of energeticallybe confidential by terms like high throughput computing allocating/ deallocating different compute resources from a(HTC) or many-task computing (MTC), de-pending on the cloud in its preparation and during job implementation.quantity of data and the numeral of tasks involved in the This paper is an comprehensive version of [13]. It includesworking out [12]. Although these systems change in devise, additional details on preparation strategies and absolutetheir indoctrination models share similar objectives, namely investigational results. The paper is prearranged as follows:hiding the bother of parallel programming, fault tolerance, Section 2 starts with analyzing the above mentionedand execution optimizations from the developer. Developers opportunities and challenges and derives some importantcan classically continue to write chronological programs. The design main beliefs for our new framework. In Section 3 we 335 All Rights Reserved © 2012 IJARCET
  2. 2. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012present Nephele’s basic structural design and summarize aware of which task’s output is required as one more task’show jobs can be described and executed in the cloud. Section input. Otherwise the scheduler of the processing structure4 provides a number of first statistics on Nephele’s cannot decide at what point in time a particular VM is nopresentation and the collision of the optimizations we longer needed and deallocate it. The MapReduce prototype ispropose. Finally, our work is completed by related work a good paradigm of an incompatible paradigm here: even if at(Section 5) and ideas for potential work (Section 6). the end of a job only few reducer household tasks may still be management, it is not probable to shut down the idle VMs, II. CHALLENGES AND OPPORTUNITIES since it is unclear if they contain in-between results which are Presented information processing frameworks like still necessary.Google’s MapReduce or Microsoft’s goblin tank engine have Finally, the scheduler of such a dispensation arrangementbeen planned for group surroundings. This is reflected in a must be able to come to a decision which task of a job shouldnumeral of assumptions they make which are not necessarily be executed on which type of VM and, perhaps, how a lot ofvalid in cloud environments. In this section we discuss how of those. This in order might be either provided on theabandoning these assumption raises new opportunities but external, e.g. as an explanation to the job description, oralso challenges for efficient similar data dispensation in deduced internally, e.g. from collected statistics, similarly toexhaust. the way record systems try to optimize their effecting plan over time [15]. A. Opportunities B. Challenges Today’s handing out frameworks typically assume there-sources they direct consist of a standing deposit of The cloud’s virtualized natural world helps to facilitatestandardized calculate nodes. even though planned to deal talented new use cases for resourceful parallel datawith individual nodes failures, they think the numeral of dispensation. How-ever, it also imposes new challengespresented machinery to be steady, especially when compared to classic come together setups. The majorscheduling the processing job’s completing. although IaaS confront we see is the cloud’s opaqueness with panorama toexhaust can definitely be used to make such cluster-like exploiting data region:setups, much of their flexibility leftovers idle. In a cluster the compute nodes are naturally One of an IaaS cloud’s key facial appearance is the interconnected through a corporal high-performanceprovisioning of compute resources on demand. New VMs network. The topology of the network, i.e. the way thecan be billed at any time from side to side a distinct interface compute nodes are actually wired to each other, is more oftenand become available in a matter of seconds. equipment than not well-known and, what is more important, does notwhich are no longer used can be concluded instantaneously alter over time. Current data dispensation frameworks offerand the cloud consumer will be charged for them no more. to control this information about the network hierarchy andFurthermore, cloud operators like Amazon let their endeavor to schedule tasks on compute nodes so that dataconsumers rent VMs of dissimilar types, i.e. with different sent from one node to the other has to pass through as fewcomputational power, different sizes of main memory, and network switches as promising [9]. That way networkstorage. Hence, the compute possessions accessible in a bottlenecks can be avoided and the in general throughput ofcloud are highly dynamic and possibly assorted. the come together can be better. With value to similar data dispensation, this suppleness This negative aspect is chiefly severe; parts of the networkleads to a diversity of new potential, particularly for may become crowded while others are essentially unutilized.scheduling data dispensation jobs. The inquiry a scheduler Although there has been research on inferring possiblehas to answer is no longer “Given a set of compute resources, network topologies solely from end-to-end capacity (e.g.how to share out the exacting tasks of a job among them?”, [7]), it is unclear if these techniques are appropriate to IaaSbut rather “Given a job, what compute possessions match the clouds. For security reasons clouds often integrate networktasks the job consists of best”. This new pattern allows virtualization techniques (e.g. [8]) which can hamper theallocating work out resources energetically and just for the conjecture process, in particular when based on latencytime they are required in the processing workflow. E.g., a dimensions.construction exploiting the possible of a cloud can start with Even if it was possible to establish the original networka solitary VM which analyzes an inward job and then advises chain of command in a cloud and use it for topology awarethe cloud to directly start the required VMs according to the development, the obtained information would notjob’s handing out phases. After each phase, the machines fundamentally remain valid for the entire meting out time.could be unlimited and no longer contribute to the generally VMs may be migrated for managerial purposes betweencost for the dealing out job. dissimilar locations inside the data center without any Facilitate such use cases imposes some necessities on the announcement, rendering any previous knowledge of theplan of a handing out framework and the way its jobs are relevant network communications outmoded.described. First, the scheduler of such a frame must turn into As a result, the only way to make certain district betweenaware of the cloud environment a job must be executed in. It tasks of a dispensation job is at this time to effect these tasksmust know about the different types of available VMs as well on the same VM in the cloud. This may engage allocatingas their cost and be able to assign or obliterate them on behalf smaller amount, but more influential VMs with multiple CPUof the darken client. cores. E.g., consider an aggregation task getting data from Second, the pattern used to describe jobs must be seven producer tasks. Data locality can be ensured bypowerful enough to express dependency amongst the preparation these tasks to run on a VM with eight cores as andissimilar tasks the jobs consists of. The scheme must be alternative of eight dissimilar single-core machines. However, currently no data dispensation framework includes All Rights Reserved © 2012 IJARCET 336
  3. 3. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012such strategies in its preparation algorithms. previously compiled VM image. The image is configured to robotically start a Task Manager and schedule it with the Job III. DESIGN Manager. Once all the essential Task Managers have Based on the challenges and opportunities outlined in the successfully contacted the Job administrator it triggers theprevious section we have calculated Nephele, a new data carrying out of the scheduled job.dispensation framework for cloud environments. Nephele Initially, the VM metaphors used to boot up the Tasktakes up many ideas of previous processing frameworks but Managers are blank and do not surround any of the data therefines them to better match the dynamic and obscure nature Nephele job is supposed to activate on. As a result, we expectof a cloud. the cloud to offer determined storage (like e.g. Amazon S3 [3]). This importunate storage is supposed to store the job’s A. Architecture input data and sooner or later receive its output data. It must Nephele’s structural design follows a classic master be easily reached for both the Job Manager as well as for themember of staff prototype as illustrated in Fig. 1. position of Task Managers, even if they are associated by a private or virtual system. Client B. Job description Public Network (Internet) Similar to Microsoft’s Dryad [14], jobs in Nephele are Cloud articulated as a heading for acyclic graph (DAG). Each highest point in the graph represents a task of the overall Cloud Controller JM Persisten dispensation job, the graph’s edges define the announcement Private / Virtualized Network flow between these tasks. We also decided to use DAGs to describe dispensation jobs for two major reasons: t The first reason is that DAGs allow tasks to have multiple Stora input and multiple output edges. This extremely simplifies TM TM TM TM ge the accomplishment of classic data Combining functions like, e.g., join operations [6]. Fig. 1. Structural overview of Nephele running in an Second and more prominently, though, the DAG’s edges Infrastructure-as-a-Service (IaaS) cloud unambiguously model the announcement paths of the Before submitting a Nephele calculates work, a client has processing job. As long as the particular tasks only exchangegot to found a VM in the cloud which runs the so called Job data through these nominated announcement boundaries,Administrator (JA). The Job Manager receives the user’s Nephele can always keep pathway of what case in pointjobs, is accountable for development them, and coordinates capacity still have need of data from what other case in pointtheir implementation. It is accomplished of communicating and which example can potentially be shut bringing up thewith the crossing point the obscure operative provides to rear and deallocated.organize the instantiation of VMs. We call this interface the Defining a Nephele job comprises three obligatory steps:obscure Controller. By means of the obscure Controller the First, the user must write the agenda code for each task of hisJob Manager can allocate or deallocate VMs according to the dispensation job or select it from an external documentation.current job execution phase. We will comply with common Second, the task program must be assigned to a vertex.Cloud computing language and refer to these VMs as Finally, the vertices must be connected by edges to define theinstances for the remnants of this paper. The term instance announcement paths of the job.type will be used to distinguish between VMs with different Tasks are predictable to contain chronological code andhardware distinctiveness. E.g., the illustration type process so-called records, the most important data unit in“m1.small” could denote VMs with one CPU core, one GB of Nephele. Programmers can define subjective types ofRAM, and a 128 GB disk while the occasion type “c1.xlarge” proceedings. From a programmer’s standpoint proceedingscould refer to apparatus with 8 CPU cores, 18 GB RAM, and go into and leave the task agenda from side to side input ora 512 GB disk. output gates. Those input and output gates can be purposeful The actual implementation of responsibilities which a end points of the DAG’s edges which are distinct in theNephele job consists of is accepted out by a position of following step. Regular tasks (i.e. tasks which are laterinstances. Each illustration runs a so-called Task Manager assigned to inner vertices of the DAG) must have at least one(TM). A Task Manager receives one or more tasks from the or more input and output gates. Opposing to that, tasks whichJob Manager at a time, executes them, and after that informs either stand for the source or the sink of the data flow mustthe Job Administrator about their achievement or possible not have input or output gates, in that order.errors. Unless a job is submitted to the Job Manager, we be After having particular the code for the scrupulous tasks ofexpecting the set of instances (and hence the set of Task the job, the user must define the DAG to attach these tasks.Managers) to be empty. Upon job response the Job Manager We call this DAG the Job Graph. The Job Graph maps eachthen decides, depending on the job’s scrupulous tasks, how task to a highest point and determines the communicationmany and what type of instances the job be supposed to be paths between them. The number of a vertex’s incoming andexecuted on, and when the individual instance must be sociable edges must thereby conform with the amount ofallocated/deallocated to ensure a continuous but input and output gates distinct inside the tasks. In addition tocost-efficient dispensation. Our current strategies for these the task to carry out, input and output vertices (i.e. verticesdecisions are decorated at the end of this section. with either no incoming or outgoing edge) can be connected The newly to be paid instances thigh boot up with a with a URL pointing to peripheral storage services to read or 337 All Rights Reserved © 2012 IJARCET
  4. 4. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012write input or output data, in that order. Figure 2 illustrates overwrite the developer’s suggestion andthe simplest potential Job Graph. It only consists of one unmistakably specify the occurrence type for ainput, one task, and one invention high point. commission in the Job Graph. If the user omits to supplement the Job Graph with this stipulation, Nephele’s scheduler applies default strategies which are discussed later on in this section. Once the Job Graph is individual, the user submits it to the Job Manager, mutually with the recommendation he has obtained from his cloud operator. The recommendation is required since the Job Manager must allocate/deallocate instances during the job implementation on behalf of the user. C. Job Scheduling and Execution After having received a valid Job Graph from the user, Nephele’s Job Manager transforms it into a so-called Execution Graph. An Execution Graph is Nephele’s primary Fig. 2. An example of a Job Graph in Nephele data structure for scheduling and monitoring the implementation of a Nephele job. Unlike the nonfigurative One major design goal of Job Graphs has been simplicity: Users Job Graph, the completing Graph contains all the concrete in should be able to describe tasks and their relationships on sequence required to schedule and execute the conventional an abstract level. Therefore, the Job Graph does not job on the cloud. It unambiguously models task explicitly model task parallelization and the mapping of parallelization and the mapping of tasks to instances. tasks to instances. However, users who wish to influence Depending on the level of annotations the user has provided these aspects can provide annotations to their job with his Job Graph, Nephele may have different degrees of description. These annotations include: autonomy in constructing the completing Graph. Figure 3  Number of subtasks: A developer can declare his shows one possible completing Graph constructed from the task to be appropriate for parallelization. Users that previously depicted Job Graph (Figure 2). Task 1 is e.g. split include such tasks in their Job diagram can identify into two parallel subtasks which are both associated to the how many similar subtasks Nephele should split the task Output 1 via file channel and are all scheduled to run on own task into at runtime. subordinate everyday jobs the same instance. The exact structure of the carrying out perform the same job code, however, they naturally Graph is explained in the procedure dissimilar remains of the information.  Number of subtasks per instance: By default each subtask is dispense to a divide instance. In case more than a few subtasks are theoretical to share the same instance, the user can provide a matching an-notation with the personality job.  Sharing instances between tasks: Subtasks of different tasks are frequently assigned to dissimilar (sets of) instances unless prevented by one more preparation restraint. If a set of instance should be shared flanked by comparable tasks the user can attach a corresponding clarification to the Job Graph.  Channel types: For each edge linking two vertices the user can decide a waterway type. Before executing a job, Nephele requires all limits of the unique Job diagram to be replaced by at least one canal of a precise kind. The control type dictates how proceedings are elated from one subtask to Fig. 3. An Execution Graph created from the original Job Graph another at runtime. Currently, Nephele supports network, file, and in-memory channels. The choice On the abstract level, the Execution Graph equals the of the channel type can have several implications on user’s Job Graph. For every vertex of the original Job Graph the entire job program. A more detailed there exists a so-called Group Vertex in the Execution Graph. conversation on this is providing in the next section. As a result, Group Vertices also rep-resent distinct tasks of the overall job, however, they cannot be seen as executable  Instance type: A subtask can be executed on units. The edges between Group Vertices are only modeled different illustration types which may be more or implicitly as they do not represent any physical less apposite for the well thought-out curriculum. communication paths during the job processing. For the sake Therefore we have developed special explanation of presentation, they are also omitted in Figure 3. task developers can use to differentiate the hardware necessities of their code. However, a user who simply utilizes these annotated tasks can also All Rights Reserved © 2012 IJARCET 338
  5. 5. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 IV. EVALUATION instance utilization over time, i.e. the average utilization ofIn this fragment we want to current first recital outcome of all CPU cores in all instances allocated for the job at the givenNephele and evaluate them to the data dispensation point in time. The utilization of each instance has beenframework Hadoop. We have elected Hadoop as our entrant, monitored with the Unix command “top” and is broken downbecause it is an open resource software and presently enjoys into the amount of time the CPU cores spent running thehigh recognition in the data dispensation community. We are respective data processing framework (USR), the kernel andaware that Hadoop has been intended to run on a very great its processes (SYS), and the time waiting for I/O to completenumber of nodes (i.e. several thousand nodes). However, (WAIT). In order to illustrate the impact of networkaccording to our annotations, the software is classically used communication, the plots additionally show the averagewith extensively fewer instances in existing IaaS clouds. In amount of IP traffic flowing between the instances over timefact, Amazon itself restrictions the number of obtainableinstances for their MapReduce service to 20 unless the V. CONCLUSIONindividual customer passes an comprehensive registration The ad-hoc analogous data dispensation has emerged toprocess [2]. be one of the killer applications for Infrastructure-as-a- The defy for both frameworks consists of two intangible Service (IaaS) clouds. Foremost Cloud computingtasks: Given a set of haphazard integer numbers, the first task organizations have ongoing to amalgamate frameworks foris to conclude the k nominal of those numbers. The second analogous data dispensation in their product assortment,task afterward is to calculate the middling of these k nominal manufacture it easy for clients to admission these servicesnumbers. The job is a classic spokesperson for a mixture of and to deploy their programs. Conversely, the dispensationdata investigation jobs whose finicky tasks vary in their frameworks which are presently worn have been designed forconvolution and hardware demands. While the first task has inert, standardized gather setups and disrespect theto sort the intact data set and therefore can take advantage of scrupulous scenery of a cloud. Consequently, the owedlarge amounts of main reminiscence and comparable compute possessions may be derisory for big parts of theexecution, the second aggregation task requires almost no submitted job and gratuitously increase dispensation timemain memory and, at least ultimately, cannot be parallelized and cost. We implemented the described sort/cumulative task for In this paper we converse the opportunities and challengesthree unusual experiments. For the first experimentation, we for proficient analogous data dispensation in clouds andimplemented the task as a progression of MapReduce current our research project Nephele. Nephele is the firstprograms and executed it using Hadoop on a preset set of data dispensation framework to unambiguously utilize theinstance. For the second experimentation, we reused the same dynamic resource allowance existing by today’s IaaS cloudsMapReduce programs as in the first experiment but devised a for both, task preparation and implementation. Scrupulousunusual MapReduce requisite to make these programs run on errands of a dispensation job can be assigned to dissimilartop of Nephele. The goal of this testing was to exemplify the types of effective equipment which are automaticallybenefits of energetic resource allowance/deallocation while instantiated and concluded during the job finishing. Based onstill maintaining the MapReduce dispensation pattern. this new scaffold, we achieve extensive evaluations ofFinally, as the third testing, we discarded the MapReduce MapReduce-inspired dispensation jobs on an IaaS cloudpattern and implemented the task based on a DAG to also system and evaluate the results to the popular datahighlight the recompense of using various instances. For all dispensation framework Hadoop.three experiments, we chose the data set size to be 100 GB.Each integer number had the size of 100 bytesResults REFERENCES [1] Amazon Web Services LLC. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/, 2009. [2] Amazon Web Services LLC. Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce/, 2009. [3] AmazonWeb Services LLC. Amazon Simple Storage Service. http://aws.amazon.com/s3/, 2009. [4] D. Battr´e, S. Ewen, F. Hueske, O. Kao, V. Markl, and D. Warneke.Nephele/PACTs: A Programming Model and Execution Framework for Web-Scale Analytical Processing. In SoCC ’10: Proceedings of the ACM Symposium on Cloud Computing 2010, pages 119–130, New York, NY, USA, 2010. ACM. [5] R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib,S. Weaver, and J. Zhou. SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. Proc. VLDB Endow., 1(2):1265–1276, 2008. [6] H. chih Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parker. Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters. In SIGMOD ’07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pages 1029–1040, New York, NY, USA, 2007. ACM. [7] M. Coates, R. Castro, R. Nowak, M. Gadhiok, R. King, and Y. Tsang. Fig.4. 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  6. 6. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012[8] R. Davoli. VDE: Virtual Distributed Ethernet. Testbeds and Research Infrastructures for the Development of Networks & Communities, International Conference on, 0:213–220, 2005.[9] J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI’04: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, pages 10–10, Berkeley, CA, USA, 2004. USENIX Association.[10] E. Deelman, G. Singh, M.-H. Su, J. Blythe, Y. Gil, C. Kesselman,G. Mehta, K. Vahi, G. B. Berriman, J. Good, A. Laity, J. C. Jacob, and D. S. Katz. Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems. Sci. Program., 13(3):219–237, 2005.[11] T. Dornemann, E. Juhnke, and B. Freisleben. On-Demand Resource Provisioning for BPEL Workflows Using Amazon’s Elastic Compute th Cloud. In CCGRID ’09: Proceedings of the 2009 9 IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 140–147, Washington, DC, USA, 2009. IEEE Computer Society.[12] I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. Intl. Journal of Supercomputer Applications, 11(2):115– 128, 1997.[13] J. Frey, T. Tannenbaum, M. Livny, I. Foster, and S. Tuecke. Condor-G: A Computation Management Agent for Multi-Institutional Grids. Cluster Computing, 5(3):237–246, 2002.[14] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks.In EuroSys ’07: Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, pages 59–72, New York, NY, USA, 2007. ACM.[15] A. Kivity. kvm: the Linux Virtual Machine Monitor. In OLS ’07:The 2007 Ottawa Linux Symposium, pages 225–230, July 2007. All Rights Reserved © 2012 IJARCET 340

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