Conference Papers

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

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Author: search.filters.author.Natesha, B.V.Subject: search.filters.subject.Cloud Computing

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    Heuristic-based iot application modules placement in the fog-cloud computing environment
    (Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Guddeti, R.M.
    Nowadays many Smart City applications make use of Internet of Things (IoT) devices for monitoring the environment. The increase in use of IoT for smart city applications causes exponential increase in the volume of data. Using centralised cloud for time sensitive IoT applications is not feasible due to more delay because of the network congestion. Hence, fog computing is used for processing the data near to the edge of the network, where processing is done by distributed network nodes. But, there is a challenge to select the fog nodes which can host and process the application modules. The placement of application module on these fog devices is known as NP-hard problem. Hence, we need better placement strategies to decide placement of application modules in fog infrastructure to minimize the application latency. In this paper, we design a First-Fit Decreasing (FFD) heuristic based approach for placing IoT application modules on Fog-Cloud and carried out the experiment using iFogsim simulator. The simulation results demonstrate that the proposed method shows significant decrease in both the application latency and energy consumption of Fog-Cloud as compared to the benchmark method. © 2018 IEEE.
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    GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center
    (Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Sharma, N.; Domanal, S.; Guddeti, R.M.
    Task Scheduling is a key challenging issue of Infrastructure as a Service (IaaS) based cloud data center and it is well-known NP-complete problem. As the number of users' requests increases then the load on the cloud data center will also increase gradually. To manage the heavy load on the cloud data center, in this paper, we propose multiobjective Grey Wolf Optimization (GWO) technique for task scheduling. The main objective of our proposed GWO based scheduling algorithm is to achieve optimum utilization of cloud resources for reducing both the energy consumption of the data center and total makespan of the scheduler for the given list of tasks while providing the services as requested by the users. Our proposed scheduling algorithm is compared with non meta-heuristic algorithms (First-Come-First-Serve (FCFS) and Modified Throttle (MT)), and meta-heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO)). Experimental results demonstrate that the proposed GWO based scheduler outperforms all algorithms considered for performance evaluation in terms of makespan for the list of tasks, resource utilization and energy consumption. © 2018 IEEE.