Psdot 15 performance analysis of cloud computing
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FINAL YEAR IEEE PROJECTS, ...

FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai

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Psdot 15 performance analysis of cloud computing Document Transcript

  • 1. PERFORMANCE ANALYSIS OF CLOUD COMPUTING AND COST ESTIMATION USING COCOMO II TECHNIQUEOBJECTIVE: The main objective of this project is to evaluate the performance analysis ofcloud computing centers using queuing systems. To obtain accurate estimation ofthe complete probability distribution of the request response time and otherimportant performance indicators such as mean number of tasks in the system,blocking probability, and probability.PROBLEM DIFINITION:  A cloud center can have a large number of facility (server) nodes, typically of the order of hundreds or thousands, traditional queuing analysis rarely considers systems of this size.  The coefficient of variation of task service time may be high.  Due to the dynamic nature of cloud environments, diversity of user’s requests and time dependency of load, cloud centers must provide expected quality of service at widely varying loads.ABSTRACT: Cloud Computing is a novel paradigm for the provision of computinginfrastructure, which aims to shift the location of the computing infrastructure to Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765
  • 2. the network in order to reduce the costs of management and maintenance ofhardware and software resources. Cloud computing has a service-orientedarchitecture in which services are broadly divided into three categories:Infrastructure-as-a- Service (IaaS), which includes equipment such as hardware,Storage, servers, and networking components are made accessible over theInternet; Platform-as-a-Service (PaaS), which includes hardware and softwarecomputing platforms such as virtualized servers, operating systems, and the like;and Software-as-a-Service (SaaS), which includes software applications and otherhosted services. To obtain accurate estimation of the complete probability distribution of therequest response time and other important performance indicators. The modelallows cloud operators to determine the relationship between the number of serversand input buffer size, on one side, and the performance indicators such as meannumber of tasks in the system, blocking probability, and probability that a task willobtain immediate service, on the other.EXISTING SYSTEM:  The number of servers is comparatively small, typically below 10, which makes them unsuitable for performance analysis of cloud computing data centers.  Approximations are very sensitive to the probability distribution of task service times. Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765
  • 3.  User may submit many tasks at a time because of this bags-of-task will appear.DISADVANTAGES:  Due to dynamic nature of cloud environments, diversity of user’s requests and time dependency of load is high.  Traffic intensity is high.  The coefficient of variation of task service time is high.  Modeling errors.PROPOSED SYSTEM: In Proposed system, the task is sent to the cloud center is serviced within asuitable facility node; upon finishing the service, the task leaves the center. Afacility node may contain different computing resources such as web servers,database servers, directory servers, and others. A service level agreement, SLA,outlines all aspects of cloud service usage and the obligations of both serviceproviders and clients, including various descriptors collectively referred to asQuality of Service (QoS). QoS includes availability, throughput, reliability,security, and many other parameters, but also performance indicators such asresponse time, task blocking probability, probability of immediate service, andmean number of tasks in the system, all of which may be determined using thetools of queuing theory. Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765
  • 4. We model a cloud server farm as a COCOMO II system which indicates thatthe inter arrival time of requestsis exponentially distributed, while task servicetimes are independent and identically distributed random variables that follow ageneral distribution with mean value of u. The system under consideration containsm servers which render service in order of task request arrivals (FCFS).Thecapacity of system is m þ r which means the buffer size for incoming request isequal to r. As the population size of a typical cloud center is relatively high whilethe probability that a given user will request service is relatively small, the arrivalprocess can be modeled as a Markovian process.ADVANTAGES:  Less Traffic Intensity.  Analytical technique based on an approximate Markov chain model for best performance evaluation.  General Service time for requests and large number of servers makes our model flexible in terms of scalability and diversity of service time.  High degree of accuracy for the mean number of tasks in the system, blocking probability, probability, response time.ALGORITHM USED: 1. COCOMO-II 2. A-Priori Algorithm Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765
  • 5. 3. AES (Advanced Encryption Standard)ARCHITECTURE DIAGRAM: Cloud Server User H Coordinator Internet CS1 CS2 CSn Back-end Database Shared File systemSYSTEM REQUIREMENTS: Hardware Requirements: • Intel Pentium IV • 256/512 MB RAM • 1 GB Free disk space or greater Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765
  • 6. • 1 GB on Boot Drive • 17” XVGA display monitor • 1 Network Interface Card (NIC)Software Requirements: • MS Windows XP/ windows 7 • MS IE Browser 6.0/later • MS Dot Net Framework 4.0 • MS Visual Studio.Net 2010 • Internet Information Server (IIS) • MS SQL Server 2005 • Windows Installer 3.1APPLICATIONS: 1. Organizations 2. Cloud Providers Clients 3. Government Sectores Z Technologies www.ztech.ininfo@ztech.incall : 91760 91765