Conference Papers

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

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    Study and analysis of various task scheduling algorithms in the cloud computing environment
    (Institute of Electrical and Electronics Engineers Inc., 2014) Mathew, T.; Chandra Sekaran, K.C.; Jose, J.
    Cloud computing is a novel perspective for large scale distributed computing and parallel processing. It provides computing as a utility service on a pay per use basis. The performance and efficiency of cloud computing services always depends upon the performance of the user tasks submitted to the cloud system. Scheduling of the user tasks plays significant role in improving performance of the cloud services. Task scheduling is one of the main types of scheduling performed. This paper presents a detailed study of various task scheduling methods existing for the cloud environment. A brief analysis of various scheduling parameters considered in these methods is also discussed in this paper. © 2014 IEEE.
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    Determination of task scheduling mechanism using computational intelligence in Cloud Computing
    (Institute of Electrical and Electronics Engineers Inc., 2016) George, N.; Chandrasekaran, K.; Binu, A.
    Cloud Computing delivers computational services through the internet. The services availed can vary according to the user requirements. The services are basically provided using virtualization technique. One of the services that are provided is the computational services for the client tasks. The client provides the Service Provider with various sized tasks that need to be executed. The execution of tasks is done using the resources present within the Provider of the services. The service provider checks for available resources, and allocates the jobs to these resources in such a manner as to minimize execution time, and various other factors that affect the performance of the Cloud. The process of allocating resources to the tasks is known as scheduling, and various scheduling mechanisms are present. A single scheduling strategy may not be always optimal in performing scheduling. In this paper, an improved mechanism for choosing the scheduling strategy is explained, which aims at addressing the problems associated with choosing the right scheduling mechanism according to the previously exhibited performances. Experimental results demonstrate the importance of using such a mechanism in selecting the right scheduling strategy. © 2015 IEEE.
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    Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization
    (Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.
    Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.
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    GA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2019) Yadav, V.; Natesha, B.V.; Guddeti, R.M.R.
    Internet of Thing (IoT) applications require an efficient platform for processing big data. Different computing techniques such as Cloud, Edge, and Fog are used for processing big data. The main challenge in the fog computing environment is to minimize both energy consumption and makespan for services. The service allocation techniques on a set of virtual machines (VMs) is the decidable factor for energy consumption and latency in fog servers. Hence, the service allocation in fog environment is referred to as NP-hard problem. In this work, we developed a hybrid algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve this NP-hard problem. The proposed GA-PSO is used for optimal allocation of services while minimizing the total makespan, energy consumption for IoT applications in the fog computing environment. We implemented the proposed GA-PSO using customized C simulator, and the results demonstrate that the proposed GA-PSO outperforms both GA and PSO techniques when applied individually. © 2019 IEEE.