Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Achar, R.Subject: search.filters.subject.Virtual Machine

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    Optimal scheduling of computational task in cloud using Virtual Machine Tree
    (2012) Achar, R.; Santhi Thilagam, P.; Shwetha, D.; Pooja, H.; Roshn; I Andrea
    The increasing demand in computing resources and widespread adaptation of Service Oriented Architecture (SOA) has made cloud as a new IT delivery mechanism. In cloud, computing resources are provided to the requester as a service, which include Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Cloud Computing is still in developing stage and faces many challenges. Out of the various issues, scheduling plays a very important role in determining the efficient execution of tasks in cloud environment. In this paper we present a scheduling algorithm which uses tree based data structure called Virtual Machine Tree (VMT) for efficient execution of tasks. The proposed algorithm is tested using CloudSim simulator and the results shows that algorithm gives better performance compared to other traditional scheduling algorithms. © 2012 IEEE.
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    Load balancing in cloud based on live migration of virtual machines
    (2013) Achar, R.; Santhi Thilagam, P.S.; Soans, N.; Vikyath, P.V.; Rao, S.; Vijeth, A.M.
    Cloud computing is an upcoming trend in the field of computer science in recent years. In cloud, computing resources are provided as service in the form of virtual machine to its clients across the globe based on demand. Huge demand for cloud resources results in overutilization of 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 in virtual machines. In this paper we present an algorithm which dynamically allocate resources based on the need and distribute the load across the servers. We conducted the experiment on Xen Cloud Platform. We use response time as a metric. The experiments conducted shows that the proposed algorithm improves the performance of applications running in virtual machines by using the feature scaling and migration. © 2013 IEEE.