International Journal of Computer Networks and Applications

Number V-7-I-3, 2020

62 - 81
Performance Analysis of Various Mobility Management Protocols for IPv6 based Networks

Authors: Kushal Pokhrel, Nitul Dutta, Mrinal K. Ghose, Hiren Kumar Deva Sarma

Abstract

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Amount of Internet traffic has increased significantly in recent times. Penetration of mobile and handheld devices in the society is also remarkable. In order to address the user demands and seamless mobility in IP networks like the Internet, there is a need to have efficient mobility management protocols and architectures. This is required to address various issues that arise due to users’ mobility. Such protocols and architectures should intend to provide better service quality to the end users. Protocols like Mobile IPv6 (MIPv6), Hierarchical MIPv6 (HMIPv6) and Proxy Mobile IPv6 (PMIPv6) have been established as widely accepted mobility solutions for IP based wireless networks which has also been standardized by the Internet Engineering Task Force (IETF). Three Layer MIPv6 (TLMIPv6) strives to provide seamless mobility management under mobile environment. In this paper, MIPv6, HMIPv6, PMIPv6 and TLMIPv6 are thoroughly surveyed. These protocols are also examined under different mobility models to evaluate respective performances. Three mobility models: (1) Random Walk Mobility Model, (2) Probabilistic Random Walk Mobility Model and (3) Gauss-Markov Mobility Model are exploited to model the mobility of users, in order to analyze the performance of the protocols. Future scope of the work has also been outlined.

82 - 92
Energy Efficient Load Balancing Aware Task Scheduling in Cloud Computing using Multi-Objective Chaotic Darwinian Chicken Swarm Optimization

Authors: G. Kiruthiga, S. Mary Vennila

Abstract

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Number of views: 144

Scheduling of tasks in a cloud environment has larger influence on time and energy depletion. Different heuristic models were developed to solve the NP-hard task scheduling problem based on time. However, ideal task scheduling algorithms must also maximize energy efficiency with good load balancing and ensure better Quality-of-Service (QoS). An innovative multi-objective Chaotic Darwinian Chicken Swarm Optimization (CDCSO) system is suggested in this article to provide energy efficient QoS and load balancing aware task scheduling. The multi-objective CDCSO algorithm incorporates the chaotic and Darwinian Theory to the standard Chicken Swarm Optimization to increase its global exploration and maximize the convergence rate. This performance enhanced CDCSO algorithm models the cloud task scheduling problem as NP-hard and utilizes the optimization principles to solve them based on multiple objective parameters. The multi-objective fitness function used in CDCSO is modelled based on the objective parameters namely energy, cost, task completion time, response time, throughput and load balancing index. Based on this multi-objective function, the CDCSO effectively allocates the tasks to the suitable energy efficient, cost and time minimized Virtual machines (VMs) which are also optimally load balanced. CloudSim simulations were conducted and the obtained results illustrated that the proposed multi-objective CDCSO has provided better task scheduling with minimized energy, cost, time and optimal load balancing.