IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Scalable analytics for iaa s cloud availability
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Scalable Analytics for IaaS Cloud Availability Abstract— In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead tooccasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. Theavailability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a definedSLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach toquantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machinesamong three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for largesystems, we use an interacting Markov chain based approach to demonstrate the eduction in the complexity of analysis and thesolution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-pointiteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the
- 2. monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approachcan handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of themethods are compared. EXISTING SCHEME The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a definedSLA, as well as to evaluate the capabilities of an existing one. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved..Many of the existing published models are hierarchical in nature. In our case, complexity and characteristics of large IaaS clouds (e.g., migration of PMs from one pool to another) lead to cyclic dependency among the submodels,needing fixed-point iteration. PROPOSED SCHEME The analytic-numeric solutions obtained from the proposed approach and from themonolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approachcan handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of themethods are compared. We state the availability assessment problem for anIaaScloud and propose a realistic
- 3. monolithicmodel representative of the state-of-the-arts. an interacting sub-models approach tosolve the largeness problem of the monolithic availability model . The overall model solutionis obtained by fixed-point iteration over individualsub-model solutions. To solve such problems, the hierarchical composition is introduced in (and many other papersand books), where a two-level hierarchical model is proposed.Each subsystem is modeled by a Markov chain andthe system. The specific case of cloud computing, some modeling approaches focusing on dependability aspects have beenproposed in recent years. The scalable stochasticapproach that we describe can be complementary to thiswork as the measured failure/repair rates of hardware componentscan be used to parameterize the model we propose. an anomaly prediction system (ALERT) forachieving robust hosting infrastructures is proposed. In special cases, closed form solutions canalso be derived to solve very large cloud models quickly.Cloud service providers can benefit from the proposedmodeling approach during design, development, testingand operation of IaaS cloud. CONCLUSIONS This paper describes a stochastic modeling approach foravailability analysis of large IaaS cloud systems. We showhow scalability issues for a monolithic model can beresolved by means of interacting sub-models or by means of simulation. The interacting sub-models approach quicklyprovides model solutions facilitating scalability without significantlycompromising the accuracy. Simulation providesresults that closely match with monolithic and interactingsub-models approaches and, for large systems, results areobtained faster. In special cases, closed form solutions canalso be derived to solve very large cloud models quickly.Cloud service providers can benefit from theproposedmodeling approach during design, development, testingand operation of IaaS cloud. During design and development,providers can use these
- 4. models to determine the poolsize required to offer a specific availability SLA. In the testingand operational stages, the providers can tune parametersfor the dynamic repair strategies (e.g., number ofparallel repairs, automated versus manual repairs) to maintainthe promised availability SLA. System Requirements: Hardware Requirements: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 44 Mb. Monitor : 15 VGA Colour. Ram : 512 Mb. Software Requirements: Operating system : Windows XP/7. Coding Language : net, C#.net Tool : Visual Studio 2010