Efficient Job Scheduling in Computational Grid Systems Using Wind Driven Optimization Technique

2018 ◽  
Vol 9 (1) ◽  
pp. 49-59
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Optimization Technique ◽  
Computational Grid ◽  
Grid System ◽  
Swarm Optimization ◽  
Grid Systems ◽  
Geographically Distributed ◽  
Job Scheduler ◽  
Computational Resources

Computational Grid has been employed for solving complex and large computation-intensive problems with the help of geographically distributed, heterogeneous and dynamic resources. Job scheduling is a vital and challenging function of a computational Grid system. Job scheduler has to deal with many heterogeneous computational resources and to take decisions concerning the dynamic, efficient and effective execution of jobs. Optimization of the Grid performance is directly related with the efficiency of scheduling algorithm. To evaluate the efficiency of a scheduling algorithm, different parameters can be used, the most important of which are makespan and flowtime. In this paper, a very recent evolutionary heuristic algorithm known as Wind Driven Optimization (WDO) is used for efficiently allocating jobs to resources in a computational Grid system so that makespan and flowtime are minimized. In order to measure the efficacy of WDO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are considered for comparison. This study proves that WDO produces best results.

scholarly journals Fault Tolerant Heterogeneous Limited Duplication Scheduling algorithm for Decentralized Grid

2013 ◽  
Vol 4 (3) ◽  
pp. 765-775
Author(s):  
DR. NITIN ◽  
Neha Agarwal ◽  
Piyush Chauhan
Keyword(s):  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Heterogeneous Environment ◽  
Computing Systems ◽  
Node Failure ◽  
Grid Systems ◽  
Task Duplication ◽  
Geographically Distributed ◽  
Grid Nodes ◽  
Computational Resources

Fault tolerance is one of the most desirable property in decentralized grid computing systems, where computational resources are geographically distributed. These resources collaborate in order to execute workflow applications as fast as possible. In workflow applications, tasks are dependent on each other, so it becomes extremely vital that scheduling techniques should also have some decentralized fault tolerant mechanism. In this paper, we have proposed a decentralized fault tolerant mechanism which utilize the checkpoint concept; for Heterogeneous Limited Duplication (HLD) algorithm. HLD is based on task duplication scheduling in heterogeneous environment. There are two fold benefits firstly; if node failure occurs then rest of grid nodes sustain the execution of application. Secondly, less makespan of application is obtained using checkpoint concept. Therefore, application scheduled over decentralized grid systems (which are known for their unreliable behavior) will yield results fast utilizing algorithm proposed in this paper.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

scholarly journals Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Piyush Chauhan ◽  
Nitin
Keyword(s):  
Distributed Computing ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Communication Cost ◽  
Computation Cost ◽  
Grid Systems ◽  
Acyclic Graph ◽  
Computational Resources ◽  
Decentralized Scheduling ◽  
Grid Resources

Complex problems consisting of interdependent subtasks are represented by a direct acyclic graph (DAG). Subtasks of this DAG are scheduled by the scheduler on various grid resources. Scheduling algorithms for grid strive to optimize the schedule. Nowadays a lot of grid resources are attached by P2P approach. Grid systems and P2P model both are newfangled distributed computing approaches. Combining P2P model and grid systems we get P2P grid systems. P2P grid systems require fully decentralized scheduling algorithm, which can schedule interreliant subtasks among nonuniform computational resources. Absence of central scheduler caused the need for decentralized scheduling algorithm. In this paper we have proposed scheduling algorithm which not only is fruitful in optimizing schedule but also does so in fully decentralized fashion. Hence, this unconventional approach suits well for P2P grid systems. Moreover, this algorithm takes accurate scheduling decisions depending on both computation cost and communication cost associated with DAG’s subtasks.

METHOD FOR TASK SCHEDULING WITH GRANULARITY OF TASKS AND HOSTS FOR GRID-SYSTEM

2019 ◽  
Author(s):  
Michael Dibrova
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Communication Channels ◽  
Task Graph ◽  
Graph Structure ◽  
Grid System ◽  
Grid Systems ◽  
System Nodes ◽  
The Impact ◽  
Modified Algorithm

This study aims to increase the productivity of grid systems by an improved scheduling method. A brief overview and analysis of the main scheduling methods in grid systems are presented. A method for increasing efficiency by optimizing the task graph structure considering the grid system node structure is proposed. Depending on the selection of the optimization criterion of the optimal node search problem, a subset of nodes is determined, which provide the start-up of the complement in a minimum time, then, node with minimum cost is selected. Task granularity (the ratio between the amount of computation and transferred data) is considered to increase the efficiency of planning. An analysis of the impact on task scheduling efficiency in a grid system is presented. A correspondence of the task graph structure considering the node structure (in which the task is immersed) to the effectiveness of scheduling in a grid system is shown. The basic scheduling algorithm for consideration and modification is the Maui hierarchical scheduler algorithm. A modified method for scheduling tasks while considering their granularity is proposed. As part of this work, you have developed the GridSim toolbox by adding new entities to simulate planning and workflow processes in the grid-environment. The relevant algorithm for task scheduling in a grid system is developed. Simulation of the proposed algorithm using the modeling system GridSim is conducted. A comparative analysis between the modified algorithm and the algorithm of the hierarchical scheduler Maui is shown. The general advantages and disadvantages of the proposed algorithm are discussed. As a result of program operation, generated diagrams of loading of Grid-system nodes and communication channels. With the help of this program there was performed analysis of load of system nodes at different relations between number of tasks and Grid-system nodes. As the task queue increases, the efficiency of the modified scheduling algorithm increases significantly due to the higher and even loading of nodes and communication channels. With a modified algorithm, scheduling increases scheduler decision time.

AN EXPERIMENTAL STUDY ON GENETIC ALGORITHMS FOR RESOURCE ALLOCATION ON GRID SYSTEMS

2007 ◽  
Vol 08 (04) ◽  
pp. 427-443 ◽  
Author(s):  
FATOS XHAFA ◽  
LEONARD BAROLLI ◽  
ARJAN DURRESI
Keyword(s):  
Experimental Study ◽  
Genetic Algorithms ◽  
Computational Grid ◽  
The Other ◽  
Diverse Population ◽  
Challenging Problem ◽  
Distributed Resources ◽  
Grid Systems ◽  
Geographically Distributed ◽  
Grid Resources

Computational Grid (CG) is an emerging paradigm in which geographically distributed resources are logically unified as a computational unit. A challenging problem in such systems is the allocation of jobs to resources that minimizes both makespan and flowtime parameters. In this paper, we present an experimental study on Genetic Algorithms (GAs) for scheduling independents jobs to Grid resources based on two replacement strategies: Steady-State GA (SSGA) and Struggle GA (SGA). SSGA distinguishes for its accentuated convergence of the population that rapidly reaches good solutions though it is soon stagnated. The SGA is based on struggle replacement and adaptively maintains diverse population, reducing thus convergence rapidity. The experimental results, based on a benchmark simulation model, showed that SGA outperforms SSGA for moderate size instances. On the other hand, the time needed by the SGA to reach makespan values obtained by the SSGA rapidly increases as more jobs and machines are added to the Grid. Thus, for larger size instances, SGA is not able to improve the results of the SSGA. Finally, we also report and analyze flowtime values for the considered benchmark.

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