ISSN: 2250-3005 - ijcer

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International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Study of Genetic Algorithm for Process Scheduling inDistributed SystemsUsha Barad1,PG Student of Computer Engineering, Merchant Engineering College, Gujarat, IndiaAbstract : This paper presents and evaluates a new method for process scheduling in distributed systems. Theproblem of process scheduling in distributed system is one of the important and challenging area of research incomputer engineering. Scheduling in distributed operating system has an important role in overall system performance.Process scheduling in distributed system can be defined as allocating processes to processor so that total execution timewill be minimized, utilization of processors will be maximized and load balancing will be maximized. Geneticalgorithm is one of the widely used techniques for constrain optimization. The scheduling in distributed systems isknown as an NP-complete problem even in the best conditions, and methods based on heuristic search have beenproposed to obtain optimal and suboptimal solutions. In this, paper using the power of genetic algorithms. We solvethis problem considering load balancing efficiently. We evaluate the performance and efficiency of the proposedalgorithm using simulation result.Keyword: Distributed system, scheduling, Genetic algorithm, load balancingI. IntroductionScheduling in distributed operating systems is a critical factor in overall system efficiency. A DistributedComputing system (DCS) is comprised of a set of Computers (Processors) connected to each other by communicationnetworks. Process scheduling in a distributed operating system can be stated as allocating processes to processors sothat total execution time will be minimized, utilization of processors will be maximized, and load balancing will bemaximized. The computational complicated process cannot be executed on the computing machine in an acceptedinterval time. Therefore, they must be divided into small sub-process. Process scheduling in distributed system is donein two phases: in first phase processes are distributed on computers and in second processes execution order on eachprocessor must be determined [1]. Several methods have been proposed to solve scheduling problem in DCS. Themethods used to solve scheduling problem in distributed computing system can be classified into three categories graphtheory based approaches , mathematical models based methods and heuristic techniques [1]. Heuristic algorithmcan be classified into three categories iterative improvement algorithms [2], the probabilistic optimization algorithmsand constructive heuristics. Heuristic can obtain sub optimal solution in ordinary situations and optimal solution inparticulars. The first phase of process scheduling in a distributed system is process distribution on computer. Thecritical aspects of this phase are load balancing. Recently created processes may be overloaded heavily while the othersare under loaded or idle. The main objectives of load balancing are to speared load on processors equally, maximizingprocessors utilization and minimizing total execution time [4]. The second phase of process scheduling in distributedcomputing system is process execution ordering on each processor. Genetic algorithm used for this phase. Geneticalgorithm is guided random search method which mimics the principles of evolution and natural genetics . Geneticalgorithms search optimal solution from entire solution space. In dynamic load balancing, processes must bedynamically allocated to processors in arrival time and obtain a near optimal schedule, therefore the execution of thedynamic load balancing algorithm should not take long to arrive at a decision to make rapid task assignments.A GA starts with a generation of individuals, which are encoded as strings known as chromosomes. Achromosome corresponds to a solution to the problem. A certain fitness function is used to evaluate the fitness of eachindividual. Good individuals survive after selection according to the fitness of individuals. Then the survivedindividuals reproduce offspring through crossover and mutation operators. GA-based algorithms have emerged aspowerful tools to solve NP-complete constrained optimization problems, such as traveling salesman problem, job -shopscheduling and flow-shop scheduling, machine learning, VLSI technology, genetic synthesis and etc.In this paper usingthe power of genetic algorithms we solve this problem considering load balancing efficiently. The proposed algorithmmaps each schedule with a chromosome that shows the execution order of all existing processes on processors. Thefittest chromosomes are selected to reproduce offspring; chromosomes which their corresponding schedules have lesstotal execution time, better load-balance and processor utilization. We assume that the distributed system is nonuniformand non-preemptive, that is, the processors may be different, and a processor completes current process beforeexecuting a new one. The load-balancing mechanis m used in this paper only schedule processes without processmigration and is centralized.||Issn 2250-3005(online)|| ||December|| 2012 Page 269
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 82. Model and Problem Definition2.1. System ModelThe system used for simulation is loosely coupled non-uniform system, all task are non-pre-emptive and noprocess migration are assumed. The process scheduling problem considered in this paper is based on the deterministicmodel. The most important purposes of distributed system are assigned as providing an appropriate and efficientenvironment for sharing resource, having an acceptable speed and high reliability and availability.Netwo rk topology,processors speed, communication channels speed and so on. Since we study a deterministic model, a distributed systemwith m processors, m > 1 should be modeled as follows:P= {p1, p2, p3,..., pm} is the set of processors in the distributedsystem. Each processor can only execute one process at each moment, a processor completes current process beforeexecuting a new one, and a process cannot be moved to another processor during execution. G is an m × m matrix,where the element guv 1≤ u,v ≤ m of G, is the communication delay rate between Pu and Pv. H is an m × m matrix,where the element huv 1≤ u, v ≤m of H, is the time required to transmit a unit of data from Pu and Pv. It is obvious thathuu=0 and ruu=0.T = {t1, t2, t3,...,tn} is the set of processes to execution. E is an n × m matrix, where the element eij 1≤ i ≤ n, 1 ≤ j ≤ m of E, is the execution time of process ti on processor pj. In distributed systems the execution time ofan individual process ti on all processors is equal.F is a linear matrix, where the element fi 1≤ i ≤ n of F, is the targetprocessor that is selected for process ti to be executed on.C is a linear matrix, where the element ci i ≤ i ≤ n of C, is theprocessor that the process ti is presented on just now.The problem of process scheduling is to assign for each process ti Є T a processor fi Є P so that total execution timewill be minimized, utilization of processors will be maximized, and load balancing will be maximized.The processor load for each processor is the sum of process execution times allocated to that process [1].The length or max span of schedule T is the maximal finishing time of all the processes or maximum load. Also,communication cost (CC) to spread recently created processes on processors must be computed:Maxspan (T) = max (Load (p i )) for each 1≤i≤ Number of processors… (2)The processor utilization for each processor is obtained by dividing the sum of processing times by scheduling length.The average of process utilization is obtained by dividing the sum of all utilizations by number of processors [2].2.2. Genetic algorithmGenetic algorithm is guided random search algorithm based on the principles of evolution and natural genetics.It combines the exploitation of the past results with the exploration of new areas of the search space. Geneticalgorithms, as powerful and broadly applicable stochastic search and optimization techniques, are the most widelyknown types of evolutionary computation methods today. In general, a genetic algorithm has five basic components asfollows [3]:1. An encoding method that is a genetic representation (genotype) of solutions to the program.2. A way to create an initial population of individuals (chromosomes).3. An evaluation function, rating solutions in terms of their fitness, and a selection mechanis m.4. The genetic operators (crossover and mutation) that alter the genetic composition of offspring during reproduction.5. Values for the parameters of genetic algorithm.||Issn 2250-3005(online)|| ||December|| 2012 Page 270
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