GRID SIMULATION FOR DYNAMIC LOAD BALANCING

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1 GRID SIMULATION FOR DYNAMIC LOAD BALANCING Kapil B. Morey 1, Prof. A. S. Kapse 2, Prof. Y. B. Jadhao 3 1 Research Scholar, Computer Engineering Dept., Padm. Dr. V. B. Kolte College of Engineering, Malkapur, kapilmorey@live.com 2 Assistant Professor, Computer Science & Engineering Dept., P.R. Patil College of Engineering and Technology, Amravati, arvind.kapse@yahoo.com 3 Assistant Professor, Computer Engineering Dept., Padm. Dr. V. B. Kolte College of Engineering Malkapur, ybjadhao@yahoo.com ABSTRACT Grid computing technology is an optimistic substitute for implementing high-performance distributed computing. The grid system needs competent load balancing algorithms for the distribution of tasks in order to increase performance and effectiveness. The objective of grid computing is to generate the illusion of virtual computer out of a large collection of connected heterogeneous nodes sharing diverse resources. To utilize the existing resources in the grid environment, the process of grid resource management is implemented. Grid resource management is the process of identifying requirements, matching the resources to applications, allocating those resources, scheduling and monitoring grid resources over time in order to run grid applications efficiently. Resource discovery is the first phase of resource management. The subsequent step is scheduling and monitoring. Scheduling process directs the job to appropriate resource and monitoring process monitors the resources in every aspect. The resources which are heavily loaded act as server of task and the resources which are lightly loaded act as receiver of task. The tasks on heavily loaded node are aimed to be shifted to the lightly loaded node. The main target of load balancing is to provide a dispersed, low cost system which balances the load across all the processors. In this paper a dynamic load balancing algorithm is proposed which fulfils the aim to accomplish high performance computing by best possible usage of varied resources in a simulated grid environment. Keywords: Resource, Workload, Dynamic, Grid, Makespan INTRODUCTION The quick progress in computing resources has enhanced the performance of computers and condensed their expenses. This accessibility of small rate dominant computers combined with the elevated pace network system has led the computing environment to be mapped from the conventionally distributed systems to the computing grid environments. The recent research works on various computing designs are allowed the appearance of a new computing standard known as grid computing [5]. Grid is a kind of dispersed system which supports the sharing and coordinated use of distributed resources, autonomously from their substantial nature and locality, in active implicit organizations that share the same target to solve significant applications. With the intention of accomplishing the customer anticipation in terms of performance and effectiveness, the distribution of tasks should be done using proficient load balancing algorithms. The load balancing algorithm works for improving the response time of applications by ensuring efficient exploitation of existing resources. One of the objectives here is to prevent the aggregation and starvation. These are the conditions where some processors are stuffed with a large number of tasks while others are lightly loaded or even idle. Grid computing is a kind of distributed and parallel system which facilitates the sharing of physical resources with dynamism at runtime depending on their accessibility, capacity, performance and asking price [6]. Load balancing algorithms aim to uniformly distribute the load on each computing node, maximizing their utilization and minimizing the total task execution time. Grid computing utilizes the resources of several simulated computers associated by a network to resolve large scale problems [7]. The individual consumers can retrieve data, transparently and without taking into consideration the system environment (operating system), location, account management, and other details in grid

2 computing. In grid computing, the details are abstracted, and the resources are virtualized. Grid Computing facilitate the job in query to be run on an unused machine in a different place on the network. 2. LOAD BALANCING Load balancing algorithmic program makes grid middleware resourceful and which ultimately leads to fast execution of application in grid computing environment. Load balancing algorithms aspire to equally extend the load on each computing node, increasing their utilization and minimizing the overall task execution time [2]. In this work, an effort has been made to devise a decentralized, sender-initiated load balancing algorithm for grid computing which depends on various parameters. One of the key characteristics of this algorithm is to estimate system parameters such as queue length and CPU utilization of each participating node and to perform workload relocation if required [8]. Load balancing should take place when the load distribution scenario has altered. The load balancing mechanism in grid aims to equally distribute the load on each computing node, increasing their utilization and reducing the total workload execution time. To accomplish these targets, the load balancing strategy needs to be fair in distributing the workload across the nodes. This implies that the difference between the heaviest-loaded node and the lightest-loaded node should be minimized [3]. 3. SIMULATION IN GRID Grid computing environment has appeared as the future parallel and distributed computing methodology, which combines scattered heterogeneous resources for finding numerous types of large-scale parallel applications in science, engineering and commerce. With the purpose of calculating the performance of grid resources management, it is necessary to perform repeatable and controlled testing, which is difficult due to grid's inbuilt heterogeneity and its dynamic nature [1]. In addition, resources are independent and owned by various systems, it is essential to hold different supervision policies at each resource. Thus it is effortless to use grid simulation as a way of analyzing complex scenarios. GridSim is a platform that enables to implement the simulation in grid environment. It provides the users to model and simulate the grid resource characteristics and networks with different system configurations [10]. GridSim is of worth importance for students and researchers who have desire to understand grid environment, and analyse new algorithms and methodologies in a controlled environment. By using GridSim, it is possible to execute repeatable experiments and studies which may not be possible in a real dynamic environment [4]. 4. SYSTEM ARCHITECTURE Fig-1: System Architecture

3 The proposed system architecture takes the job and resource information as input to the initial status. The jobs and resources required for jobs are simulated in gridsim. The information of resources in terms of resource ID, resource characteristics and processing elements necessary for task completion are provided by the gridsim for further computations. The calculations include the processing time, service time, length i.e number of instructions per second for gridlet. The grouping of jobs and various resources is done by clustering and partitioning. After partitioning and applying the balancing criteria using the necessary calculations of processing time, service time the load balancing is applied. The tasks are again checked for load distribution using the rebalancing criteria. If required the migration of jobs is applied from one cluster to another for the necessary resources. Finally the grid utilization is calculated based on the data of jobs completed and resources utilized in the gridsim environment. 4.1 System Workflow Firstly the gridsim is initialized by simulating the nodes, jobs and resources needed for the completion of jobs. Every resource is identified by the unique resource ID. Every node receives allocated resource characterstics. The jobs and required resources along with their characteristics form a gridlet. The next step is to submit the gridlets to the nodes which is also referred as environment of gridsim. The gridlets are grouped into clusters. The nodes present in one cluster can share the required resources by simple load information of the systems. However for sharing resources between the nodes present in different clusters the load information is to be acquired by request type dispatch policy. Prior to implementing request type dispatch policy, the load information from other cluster machines is needed [8]. The processing time of jobs is calculated by the algorithm and depending on the processing time the clusters are arranged in ascending order. The Pre-emption aware workload allocation policy decides the distribution of tasks. Based on the load information of machines present in different clusters, the request type dispatch policy is applied for the dispatch of jobs to other cluster as and when needed. After the load distribution using the preemption aware workload allocation policy and job dispatch using the request type dispatch policy, the balancing of load can be achieved in the grid environment. As the proposed system is simulated in grid environment, the overall performance of the system is briefly demonstrated by the grid utilization. The grid utilization is calculated on the combined outcome of resource utilization, processing time, job dispatch and various other parameters which are simulated over grid environment. 4.2 Grid Utilization The Grid Simulation provides the environment for performing the formation of gridlets, clusters and accordingly the various parameters required for the implementation of load distribution to accomplish the task of load balancing [11]. A key characteristic of grid environment is that the resources (e.g., CPU cycles and network capacities) are shared among various applications, and hence, the quantities of resources available to any given applications vary highly over time. Load balancing is a strategy to enhance resources, by utilizing parallelism, and to reduce response time through proper distribution of the application. The grid utilization for the load balancing in this approach has been increased to above 95 percent for the number of resources required in completion of jobs/loads [9]. The preemption aware workload allocation and request type dispatch policy ensure the grid utilization to maximum extent possible in comparison to earlier approaches. 4.3 Resource Utilization The resources in the grid environment are utilized to maximal extent as per the need of load required. The net utilization (Nr) is fraction of resources used to the complete the job/load in the grid environment. Resources are determined at the initial level prior to the simulation in the grid. Depending on the resources the total loads are managed and allotted to balance the resource distribution. The resource distribution depends on the process time. Grid is a kind of distributed system that supports the sharing and coordinated use of geographically distributed and multi owner resources separately from their physical type location. Thus the resources are efficiently used to manage the load balancing. 5. IMPLEMENTATION OF SYSTEM In this paper implementation, the tasks with a higher order are completed first which means that tasks are taken a higher priority than the others which leads to starvation that increases the completion time of workload and

4 thus load balance is not definite. To handle this issue we propose a load balancing strategy to give consistent load to the resources in order that all workloads are fairly allocated to processor depending on balanced fair rates. The main intention of this strategy is to decrease the overall makespan. Fig-2: Image of getting the characteristics of resources with the resource Ids Above image shows collection of resource characteristics with the node which is allocated to it with their ID number. Fig-3: Image of submission of gridlet to the nodes Above image shows the submission of gridlets to the nodes with their IDs, also collecting the process time and the length of the jobs for those nodes.

5 Fig-4: Image of dispatch policy of workload Above image shows the dispatched for the jobs used for doing the load balancing in the grid. Fig-5: Image of calculation of computing time for jobs Above image shows Computing time for the jobs which are been dispatched to other cluster with the grid utilization.

6 6. CONCLUSION This algorithm has proved the better results in terms of makespan and execution cost in particular the algorithm allocates the task to the available processors so that all requesting task get equal amount of time that satisfied their demand. Through this proposed algorithm, we have described multiple aspects of load balancing algorithm and introduced numerous concepts which demonstrate its broad capabilities. The projected algorithmic strategy is certainly a promising tendency to solve high demanding applications and all kinds of problems. Objective of the grid environment is to achieve high performance computing by optimal usage of distributed system. Resources can be submitted to grid and can be withdrawn from grid at any moment. This grid characteristic makes load balancing one of the significant features of grid infrastructure. There are a number of factors, which can affect the grid application performance like load balancing, heterogeneity of resources and resource sharing in the Grid environment. Hence the proposed system is executed in simulated grid environment. 7. FUTURE SCOPE In the grid environment, numerous sites are capable of providing computing resources. Some of these sites are frequently idle and able to constantly share computing resources, some, however, are not. How to select efficient sites is one of the issues worthy of further investigation. The additional future work may include designing an efficient load-balancing mechanism for data grids. REFERENCES [1] Abhishek M. Kinhekar, Prof. Hitesh Gupta, A Review of Load Balancing in Grid Computing, International Journal of Advance Research in Computer Science and Management Studies, ISSN: Vol. 2, Issue 8, pp , August [2] Sukalyan Goswami, Ajanta De Sarkar, A Comparative Study of Load Balancing Algorithms in Computational Grid Environment, IEEE Fifth International Conference on Computational Intelligence Modelling and Simulation, /13, IEEE DOI /CIMSim , [3] S. Goswami, A. De Sarkar Service Oriented Load Balancing Framework in Computational Grid Environment, International Journal of Computers and Technology, ISSN: Volume 9, Number 3, pp , [4] Gaurav Sharma, Jagjit Kaur Bhatia, Optimal and Queuing Based Approach for Load Balancing in Computational Grid, International Journal of Computer Trends and Technology, ISSN: Vol. 4, Issue 6, pp , June [5] Gaurav Sharma, Jagjit Kaur Bhatia, A Review on Different Approaches for Load Balancing in Computational Grid, Journal of Global Research in Computer Science, ISSN: X Vol. 4, No. 4, pp-82-85, April [6] C.Kalpana, U.Karthick Kumar, R.Gogulan A Randomized Load Balancing Algorithm in Grid Using Max Min Pso Algorithm International Journal of Research in Computer Science, eissn Volume 2 Issue 3 pp , [7] Malarvizhi Nandagopal, Ramanujan and Rhymend Uthariaraj Computing Centre, Anna University Chennai, India, Performance Analysis of Resource Selection Algorithms in Grid Computing Environment, Journal of Computer Science 7 (4): , ISSN Science Publication, [8] U. Karthick Kumar A Dynamic Load Balancing Algorithm in Computational Grid Using Fair Scheduling, International Journal of Computer Science and Informatics, Vol. 8, Issue 5, No 1, ISSN (Online): , September [9] Dr. K. Vivekanandan, D.Ramyachitra, A Study on Scheduling in Grid Environment, International Journal on Computer Science and Engineering (IJCSE), ISSN: Vol. 3 No. 2, Feb [10] Prabhat Kr. Srivastava, Improving Performance in Load Balancing Problem on the Grid Computing System, International Journal of Computer Applications ( ), Volume 16 No.1, February [11] Daphne Lopez, Kashmir Raja, A Dynamic Error based Fair Scheduling Algorithm for Computational Grid, Journal of Theoretical and Applied Information Technology, Technology, JATIT