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  1. 1. Gogila G. Nair, Karthikeyan P. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.717-720 A Hierarchical Chemical Reaction Optimization for Varying Length Task Scheduling in Grid Computing Gogila G. Nair*, Karthikeyan P.** *(Department of Computer Science and Engineering, Karunya University, Coimbatore) ** (Department of Computer Science and Engineering, Karunya University, Coimbatore)ABSTRACT A grid computing system shares the load The main goal of scheduling is to achieve the highestacross multiple computers to complete tasks more possible system throughput and to match theefficiently and quickly. Grid computing is applications requirements with the availablepresently a full of life analysis space. In this paper computing resources. A grid usually consists of fivea new method is proposed for scheduling the parts: clients, the Global and Local Grid Resourcevarying length jobs in the grid environment. It Brokers (GGRB and LGRB), Grid Informationpresents how the jobs can be scheduled effectively Server (GIS), and resource nodes. The clients registerby using Hierarchical scheduling. The concept of their requests of processing their computational tasksShortest Job First algorithm is used along with at GGRB. Resource nodes register their donatedChemical Reaction Optimization algorithm in resource at LGRB and process clients’ tasksorder to schedule varying length jobs efficiently. according to the instructions from LGRB. In practice,The results show that the proposed method client and resource node can be the same computer.provides better flowtime and also it performs GIS collects the information regarding resourcesbetter than the existing methods. from all LGRBs, and transfers it to GGRB. This GGRB is responsible for scheduling. It possesses allKeywords - Chemical Reaction Optimization, Grid necessary information about the tasks and resourcescomputing, scheduling algorithms, Task and acts like a database of the grid.scheduling. Each user may differ from other users with respect to different characteristics such as types of job created, execution time and scheduling optimization strategy. I. INTRODUCTION Each resource may differ from other resources with Grid computing is a computer network in respect to number of processors, cost, Speed andwhich each computer’s resources are shared with internal process scheduling policy. Grid computingevery other computer within the system. Processing solves high performance and high-throughputpower, memory and data storage are all community computing problems through sharing resourcesresources that the authorized users can tap into and ranging from personal computers to supercomputersleverage for specific tasks. A grid system may be as that are distributed throughout the world. A majoreasy as a collection of similar computers running on problem to be considered is task scheduling, i.e.,the same operating system or as complex as inter- allocating tasks to resources. There are severalnetworked systems. It can be thought of as a special algorithms for scheduling the jobs to the resources.kind of distributed computing. In distributed These algorithms are not suitable for all thecomputing, totally different computers within the situations.same network share one or more additional resources. The Chemical Reaction Optimization is an efficientIn the ideal grid computing system, each resource is algorithm for scheduling the jobs in the gridshared, turning a computer network into a powerful environment. This is efficient for scheduling equalsupercomputer. With a proper user interface, length jobs. But for varying length jobs this algorithmaccessing a grid computing system would look no is not much effective. Hence a hierarchicaldifferent than accessing a local machines resources. scheduling method is proposed in this paper. ThisEach of the authorized computers would have access method is effective for scheduling varying lengthto enormous processing power and storage capacity. jobs.A grid computing system [1] shares the load across The rest of the paper is organized as follows: relatedmultiple computers to complete tasks more work is described in Section II. Section III describesefficiently and quickly. Grid computing systems link the Chemical Reaction Optimization. In Section IV, acomputer resources together in a way that lets detailed view of Hierarchical scheduling method issomeone use one computer to access and leverage the given. Section V includes the result analysis. Finally,collected power of all the computers in the system. the conclusion is given in Section VI.To an individual user, its like the users computer hastransformed into a supercomputer. 717 | P a g e
  2. 2. Gogila G. Nair, Karthikeyan P. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.717-720II. RELATED WORK candidate solution is acceptable, it becomes the Scheduling refers to the method by which current solution and this completes an iteration of theprocesses or tasks or jobs are given access to system procedure. It has greater convergence time evenresources. Scheduling can also be defined as the though it is simple.mapping of tasks to resources that may be distributedin various administrative domains. This is done III. CHEMICAL REACTION OPTIMIZATIONmainly to balance a system effectively and to achieve (CRO)the target. There are several algorithms such as Chemical Reaction Optimization is asimulated annealing, ant colony optimization, genetic population-based metaheuristic, and it can be usedalgorithm, particle swarm optimization, threshold for solving many problems. CRO mimics theaccepting and so on. interactions of molecules in the chemical reactionsIn the year 1996 Dorigo M. introduced the Ant and it searches for global optimum [9]. A chemicalalgorithm. It is based on real ants and is derived from system undergoes a chemical reaction when it isthe social behavior of ants [2]. Ants work together to unstable, that is when it possesses excessive energy.find the shortest path between their nest and food It manipulates itself to release the excessive energy insource. When an ant looks for food, it deposits some order to stabilize itself and this manipulation is calledamount of chemical substance called pheromone [3] chemical reactions. Generally in a chemical reactionon the path. The shortest path is found using this molecules interact with each other aiming to reachpheromone. Here the decisions on which path to the minimum state of free energy. Through achoose are made at random. It is more applicable to sequence of intermediate reactions, the resultantproblems where source and destination are molecules (i.e. the products in a chemical reaction)predefined. Simulated Annealing algorithm [4] is tend to stay at the most stable state with the lowestbased on the physical process of annealing. For task free energy.scheduling in the grid environment this algorithm When looking at the chemical substances at thestarts by generating an initial solution. In each microscopic level, a chemical system consists ofiteration, it generates a new solution randomly in the molecules, which are the smallest particles of aneighborhood of the present solution, and it will be compound. It retains the chemical properties of theaccepted when it is better, or accepted with a compound. Molecules are classified into differentprobability controlled by a temperature parameter. It species based on the underlying chemical properties.is relatively simple but it may consume more time to A chemical reaction always results in more stablefind the good solution. products with minimum energy and it is a step-wiseThe Genetic Algorithm (GA) is a technique for large process of searching for the optimal point. Aspace search. The general procedure for GA search chemical change of a molecule is triggered by a[5] includes Population generation, Chromosome collision. There are two types of collisions: uni-evaluation and Crossover and mutation. Here each molecular and inter-molecular collisions. The formerchromosome represents a possible solution. The describes the situation when the molecule hits onsearch time cost is high and also the convergence some external substances while the latter representstime is more for this algorithm. The Particle swarm the cases where the molecule collides with otheroptimization (PSO) is a population-based algorithm molecules. The corresponding reaction change is[6] which is modeled on swarm intelligence, like bird called an elementary reaction. An ineffectiveflocking and fish schooling. It starts with a group of elementary reaction is one which results in a subtleparticles known as the swarm. A flock or swarm of change of molecular structure.particles is randomly generated initially with each There are three stages [10] in CRO: initializationparticle’s position representing a possible solution stage, iteration and the final stage. The computerpoint in the problem space. The particles are the task implements CRO by following these three stagesto be assigned. The algorithm starts with random sequentially. Each run starts with the initialization,initialization [7] of particle’s position and velocity. performs a certain number of iterations, andPSO have no overlapping and mutation calculation terminates at the final stage. There are four majorand hence the convergence time for PSO is less. But operations called elementary reactions in CRO: on-this method easily suffers from the partial optimism. wall ineffective collision, synthesis, inter-molecularThreshold Accepting [8] is similar to Simulated ineffective collision and decomposition. In CRO, theAnnealing but with a different acceptance rule. Every on-wall ineffective collision and inter-molecularnew solution would be accepted as long as the ineffective collision correspond to local search,difference is smaller than a threshold. It begins with whereas decomposition and synthesis correspond toan initial solution and an initial threshold value. A remote search. Through a sequence of intermediateneighborhood solution to the current solution is reactions, the resultant molecules (i.e. the products ingenerated by using the perturbation scheme. The a chemical reaction) tend to stay at the most stablecurrent and the candidate solution are evaluated and state with the lowest free energy. An on-walltheir objective function value is obtained. If the ineffective collision occurs when a molecule hits the 718 | P a g e
  3. 3. Gogila G. Nair, Karthikeyan P. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.717-720wall and then bounces back. Some molecular The first level i.e., Permutation based methodattributes change in this collision, and thus, the includes scheduling of jobs to the resources. This ismolecular structure varies accordingly. An inter- done by means of several operators such as Insertionmolecular ineffective collision describes the situation operator, Position-based operator and Two-exchangewhen two molecules collide with each other and then operator. The details regarding these operators arebounce away. A decomposition means that a described in the previous section. Once the jobs aremolecule hits the wall and then decomposes into two scheduled to the resources using permutation basedor more (assume two in this framework) pieces. A method, each resource will have jobs of differentsynthesis depicts more than one molecule which lengths. If these jobs are executed in the same ordercollide and combine together. in which they are allocated to each resource, there isCRO includes permutation based representation, a possibility for the smaller jobs to wait for the largerwhere the jobs allocated to resources is indicated jobs to complete. To avoid this, the next level mustusing a vector. CRO is a variable population based be included.metaheuristic [11] where the total number of The second level of hierarchical scheduling involvessolutions kept simultaneously by the algorithm may selection of shortest job and executing them prior tochange from time to time. Decomposition and the larger jobs. The concept of Shortest Job Firstsynthesis increases and decreases the number of algorithm is utilized here for identifying the smallermolecules, respectively. Several CRO programs jobs. Jobs of different lengths will be allocated tocorresponding to the different modules can be each of the resources. In each resource the shortestimplemented simultaneously. CRO is best suited to job will be selected and executed first. The next stepthose types of problems which will benefit from is to execute the jobs and to compute the flowtime.parallel processing rather than sequential processing. The flowtime is defined as the time consumed by all the tasks to complete its execution. IV. PROPOSED SYSTEM This Hierarchical scheduling process eliminates the The existing CRO algorithm can be used for need for smaller jobs to wait for the larger jobs toscheduling tasks of equal length. The performance of complete. This Hierarchical scheduling methodthis algorithm degrades when it is used for improves the flowtime and it also provides betterscheduling varying length tasks. While scheduling performance.varying length jobs the performance can be improvedby using hierarchical scheduling method. This V. RESULT ANALYSISmethod includes two levels and the flow diagram is The results show that the proposedshown in Fig 1. The first level involves permutation Hierarchical scheduling method is better than thebased scheduling and the second level includes existing techniques. The graph shown in Fig 2Shortest Job First algorithm. The permutation based provides a better understanding about the variationsscheduling is same as in CRO, which is to schedule in flowtime while scheduling the jobs using thethe jobs to resources. The next step includes Shortest existing and proposed algorithms.Job First scheduling algorithm, which selects the 5shortest jobs and executes them. 4 User creation flow time in seconds Receiving job 3 and resource Chemical details creation Reaction 2 Optimization 1 Hierarchical Selecting the Permutation 0 shortest job in based scheduling 1 2 3 4 each resource No. of users Hierarchical scheduling Fig. 2: Variations in flowtime Execution Computing From this graph it is clear that the flowtime for of jobs flowtime Hierarchical scheduling method is lesser than the Chemical Reaction Optimization technique. AlsoFig. 1: Flow diagram for proposed system there is no need for the smaller jobs to wait for the larger jobs to complete, thus the waiting time for the smaller jobs is minimized. Thus the Hierarchical 719 | P a g e
  4. 4. Gogila G. Nair, Karthikeyan P. / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.717-720scheduling method provides better performance than and Advanced Information Management (NCMthe existing methods. ’08), 686-691, 2008. [9] Jin Xu, Albert Y.S. Lam and Victor O.K. Li, VI. CONCLUSION “Chemical reaction optimization for task One of the important research area in the scheduling in grid computing” IEEEfield of computer networking is grid computing. The Transactions on Parallel and Distributedmost important issue to be considered in the field of systems, vol. 22, No. 10, 2011.grid computing is efficient scheduling of jobs. The [10] Albert Y.S. Lam and Victor O.K. Li, “ChemicalChemical Reaction Optimization algorithm is suitable Reaction Optimization: A Tutorial (Invitedfor scheduling equal length jobs. But when used for paper),” Memetic Computing, vol. 4, no. 1, pp.scheduling varying length jobs it results in increased 3-17, Mar. 2012.flowtime. The hierarchical scheduling method is [11] A.Y.S. Lam and V.O.K. Li, “Chemical-suitable for scheduling varying length jobs. The Reaction-Inspired Metaheuristic forresults show that the flowtime in Hierarchical Optimization,” IEEE Trans. Evolutionaryscheduling method is minimized than the existing Computation, vol. 14, 381-399, 2010.method. This Hierarchical scheduling method is agood solution for task scheduling and it makes theprocess of scheduling varying length jobs easier.REFERENCES[1] F. Pop, and V. Cristea, “Optimization of Schedulig Process in Grid Environments,” U.P.B. Sci. Bull., Series C, 2009.[2] W. Chen, and J. Zhang, “An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements”, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, 2009.[3] S. Lorpunmanee, M.N. Sap, A.H. Abdullah and C. Chompoo-inwai, “An Ant Colony Optimization for Dynamic Job Scheduling in Grid Environment,” World Academy of Science, Engineering and Technology, 2007.[4] S. Fidanova, “Simulated Annealing for Grid Scheduling Problem,” Proc. IEEE John Vincent Atanasoff Int’l Symp. Modern Computing (JVA ’06), 41-45, 2009.[5] S. Prabhu, and V. Naveen Kumar, “Multi- Objective Optimization Based on Genetic Algorithm in Grid Scheduling,” International Journal of Advanced Research and Technology(IJART), 54-58, 2011.[6] H. Izakian,B.T. Ladani, K. Zamanifar, and A. Abraham, “A Novel Particle Swarm Optimization approach for Grid Job Scheduling,” Proc. Third Int’l Conf. Information Systems, Technology and Management, 2009.[7] L. Zhang, Y. Chen, R. Sun, S. Jing, and B. Yang, “A Task Scheduling Algorithm Based on PSO for Grid Computing,” International Journal of Computational Intelligence Research, 37–43, 2008.[8] S. Benedict, R.S. Rejitha, and V. Vasudevan, “Threshold Accepting Scheduling algorithm for scientific workflows in Wireless Grids”, Proc. IEEE Fourth Int’l Conf. Networked Computing 720 | P a g e

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