Faculty Publications

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Author: search.filters.author.Domanal, S.G.Subject: search.filters.subject.Cloud computing

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    Load balancing in cloud computing using modified throttled algorithm
    (IEEE Computer Society help@computer.org, 2013) Domanal, S.G.; Guddeti, G.
    Load balancing is one of the critical components for efficient operations in the cloud computing environment. In recent years many clients from all over the world are demanding the various services at rapid rate. Many algorithms have been designed to carry out the client's request towards the cloud nodes. Accordingly the cloud computing platform will dynamically configure its servers and these servers may be present physically or virtually in the computing environment. Hence, selecting the virtual machines or servers has to be scheduled properly by using an appropriate load balancing approach. In the present work, a local optimized load balancing approach is proposed for distributing of incoming jobs uniformly among the servers or virtual machines. Further, the performance is analyzed using CloudAnalyst simulator and compared with existing Round Robin and Throttled algorithms. Simulation results have demonstrated that the proposed algorithm has distributed the load uniformly among virtual machines. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.
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    A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment
    (Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.
    In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.