Browsing by Subject "Cloud computing"
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Item Automated Live Migration of Virtual Machines in Cloud Data Center(Department of Computer Science & Information Technology, 2019) Tamang, RameshThe thesis entitled “Automated Live Migration of Virtual Machines in Cloud Data Center” involves the study and implementation of load balancing algorithm for Virtual Machine Management in the cloud infrastructure. The study is focused on dynamic resource scaling and live migration algorithm of virtual machines to achieve the load balancing in the cloud infrastructure. As data centers grow sharply, they find themselves accommodating an increasing number of Physical Machines and Virtual Machines. This development requires an effective resource management in data centers. As a result, the load is evenly distributed and service level agreements (SLAs) are met. From this point of view, load balancing and live migration are becoming essential processes for data center management. In cloud, computing resources are provided as a service to its clients across the globe based on demand. Huge demand for cloud resources results in overutilization by degrading the performance of the servers whenever there is a heavy load. It is necessary to distribute the load across the servers in cloud by taking into consideration of allocating the right amount of resources dynamically based on the load to improve the performance of applications running inside virtual machines. Experiment is conducted on Xen servers in the real physical hardware and in the virtualized environment and implemented dynamic resource scaling and live migration technique to manage load in the virtual machines running on those Xen servers. The response time of virtual machine is used as a metric to measure the performance of algorithms. After the implementation of dynamic resource scaling and live migration technique, the improved performance of virtual machine in terms of response time and efficient utilization of Xen server resources is observed.Item Comparison of back propagation algorithm and SVM on SLA based masquerader detection in cloud(Department of Computer Science and Information Technology, 2013) Ghimire, Dadhi RamCloud computing is a prospering technology that most organizations are considering for adoption as a cost effective strategy for managing IT. However, organizations still consider the technology to be associated with many business risks that are yet to be resolved. Such issues include security, privacy as well as legal and regulatory risks. As an initiative to address such risks, organizations can develop and implement Service Level Agreement (SLA) to establish common expectations and goals between the cloud provider and customer. Organizations can base on the SLA to address the security concern. However, many SLAs tend to focus on cloud computing performance whilst neglecting information security issues. This study is oriented to build a masquerade detection system in cloud computing, based on the proposed SLA. The new SLA contains additional security constraints than that found in traditional SLA such as length of temporal sequence, weight of each activities and the threshold weight of the temporal sequence. The performance analysis includes comparison of BackPropagation algorithm with SVM. The detection rate and false alarm rate is observed and found that it can detect masqueraders well from the small set of training data with small false alarm rate. Keywords: Cloud Computing, Service Level Agreement, Masquerader, Backpropagation Algorithm, Support Vector Machine, Temporal SequenceItem Task Scheduling in Cloud Computing Environment using Evolutionary and Swarm Based Algorithm(Department of Computer Science and Information Technology, 2017) Rimal, Nawa RajCloud computing is a popular computing concept that performs processing of huge volume of data using highly accessible geographically distributed resources that can be access by user on the basis of Pay per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. Complexity class of the task scheduling problem belongs to NP-complete involving extremely large search space with correspondingly large number of potential solutions and takes much longer time to find the optimal solution. There is no readymade and well-defined methodology to solve the problems under such circumstances. However, in cloud, it is sufficient to find near optimal solution, preferably in a short period of time. The Evolutionary and Swarm based Algorithm is a scheduling algorithm capable of locating good solutions efficiently. The algorithm could be considered as belonging to the category of “Intelligent Optimization”. This dissertation work analyzes Genetic Algorithm, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) task scheduling algorithms in cloud computing that address the above-mentioned problem. The simulation result shows that the PSO algorithm produces the significant result and provides the better solution. General GA, ACO and PSO are implemented and tested. Evaluation results have shown that PSO seemed to be 38% better than GA and 26.5% better than ACO.