Papers by Satheesh Kavuri
Infrastructure as a Service (IaaS) clouds have emerged as a promising new platform for massively ... more Infrastructure as a Service (IaaS) clouds have emerged as a promising new platform for massively parallel data processing. By eliminating the need for large upfront capital expenses, operators of IaaS clouds offer their customers the unprecedented possibility to acquire access to a highly scalable pool of computing resources on a short-term basis and enable them to execute data analysis applications at a scale which has been traditionally reserved to large Internet companies and research facilities. However, despite the growing popularity of these kinds of distributed applications, the current parallel data processing frameworks, which support the creation and execution of large-scale data analysis jobs, still stem from the era of dedicated, static compute clusters and have disregarded the particular characteristics of IaaS platforms so far. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Particular tasks of processing a job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. However, the current algorithms does not consider the resource overload or underutilization during the job execution. In this paper, we have focused on increasing the efficacy of the scheduling algorithm for the real time Cloud Computing services. Our Algorithm utilizes the Turnaround time Utility efficiently by differentiating it into a gain function and a loss function for a single task. The algorithm also assigns high priority for task of early completion and less priority for abortions issues of real time tasks. The algorithm has been implemented on RR method. The out performs existing utility based scheduling algorithms and also compare its performance.
Cloud computing is recently a booming area and has been emerging as a commercial reality in the i... more Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.
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Papers by Satheesh Kavuri