Conference Paper

# Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing.

DOI: 10.1109/IPDPS.2005.184 Conference: 19th International Parallel and Distributed Processing Symposium (IPDPS 2005), CD-ROM / Abstracts Proceedings, 4-8 April 2005, Denver, CO, USA

Source: DBLP

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**ABSTRACT:**Genetic Algorithms (GAs) are stochastic search techniques based on principles of natural selection and recombination that attempt to find optimal solutions in polynomial time by manipulating a population of candidate solutions. GAs have been widely used for job scheduling optimisation in both homogeneous and heterogeneous computing environments. When compared with list scheduling heuristics, GAs can potentially provide better solutions but require much longer processing time and significant experimentation to determine GA parameters. This paper presents a GA for scheduling dependent jobs in grid computing environments. A number of selection and pre-selection criteria for the GA are evaluated with an aim to improve GA performance in job scheduling optimization. A Task Matching with Data scheme is proposed as a GA mutation operator. Furthermore, the effect of the choice of heuristics for seeding the GA is investigated.The Journal of Supercomputing 10/2012; 62(1). · 0.92 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an np-hard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balancing which is a major issue in task scheduling is also considered. The nature of the tasks are independent and non pre-emptive. The HPSO yields a better result than the Normal PSO when applied to the task assignment problem. The results Of PSO and HPSO is also compared with another popular heuristic optimization technique namely Genetic Algorithm ( GA). The results infer that the PSO performs better than the GA. -
##### Conference Paper: Configuring Cloud Admission Policies under Dynamic Demand

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**ABSTRACT:**We consider the problem of admitting sets of possibly heterogenous, virtual machines (VMs) with stochastic resource demands onto physical machines (PMs) in a Cloud environment. The objective is to achieve a specified quality-ofservice related to the probability of resource over-utilization in an uncertain loading condition, while minimizing the rejection probability of VM requests. We introduce a method which relies on approximating the probability distribution of the total resource demand on PMs and estimating the probability of overutilization. We compare our method to two simple admission policies: admission based on maximum demand and admission based on average demand. We investigate the efficiency of the results of using our method on a simulated Cloud environment where we analyze the effects of various parameters (commitment factor, coefficient of variation etc.) on the solution for highly variate demands.MASCOTS Conference, 2013 IEEE 21th International Symposium on, San Francisco; 08/2013

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