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). · 0.84 Impact Factor -
##### 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 -
##### Conference Paper: Real-time task scheduling in heterogeneous multiprocessor systems using artificial bee colony

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**ABSTRACT:**Scheduling of real-time tasks in multiprocessor systems is a NP-hard problem. Recently, swarm intelligence algorithms have been efficiently applied for this problem. Real-time tasks can be classified into hard real-time tasks and soft real-time tasks. The aim of hard real-time task scheduling algorithms is to meet all tasks deadline constraints. However, slight violation is not critical, in the case of soft real-time tasks. In this paper, a new algorithm based on artificial bee colony (ABC) is proposed for scheduling of soft real-time tasks. In this method, a hybrid neighborhood search mechanism is introduced to improve the convergence of ABC. Experimental results demonstrate the effectiveness of proposed algorithm for scheduling of soft real-time tasks in heterogeneous multiprocessor systems.22nd Iranian Conference on Electrical Engineering (ICEE2014), Tehran, Iran; 05/2014

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