Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

and Applied Analysis 3 particle swarm optimization PSO was first proposed by Kennedy and Eberhart 16 . In PSO, a swarm of particles spread in the search space and the position of a particle presents a solution. Each particle would move to a new position decided with the individual experience and the global experience heading toward the global optimum. However, many variations of PSO have been studied, and one of them was named “standard” PSO, proposed by Clerc and Kennedy 35 indicating how PSO can be significantly improved. Hence, this investigation aims at enhancing the “standard” PSO for solving the interesting task scheduling in grid. Each particle represents a possible task schedule corresponding to a task-resource assignment graph T-RAG and regards the longest path of the task-resource assignment graph as fitness value. Hence, this investigated task scheduling problem became an optimal task-resource assignment graph selection problem. To increase the efficiency of “standard” PSO, heuristics can be used as an aid for problem solving. Thus, this study enhances “standard” PSO by introducing additional heuristics to solve the task scheduling problem for minimizing task completion time. Nevertheless, a heuristic is usually problem specific, hence different heuristics were surveyed and evaluated. Least total resource usage LTRU and shortest feasiblemode SFM heuristics are frequently applied for determining operating mode for multimode project scheduling problems. Greatest rank positional weight GRPW , latest finish time LFT , latest start time LST , minimum slack MSLK , and most total successors MTS heuristics are commonly used for deciding tasks’ priority in project scheduling problems. Two intuitional heuristics were then proposed for speeding up the PSO’s search when solving investigated task scheduling problems. They are the best performance resource BPR heuristic and the latest finish time LFT heuristic based on multimode project scheduling problems; they are herein called project scheduling heuristics. Moreover, the performance of the proposed PSO scheme with different swarm communication topology is also evaluated. Restated, global communication and local communication topologies for obtaining the global experience are analyzed and compared. Finally, the experiment results indicate that the scheme proposed in this work is effective for solving similar class task scheduling problems. This article is organized as follows. Section 2 introduces the task scheduling problem. The traditional PSO is described in Section 3. Section 4 illustrates application of the PSO to the task scheduling problem, and introduces the additional heuristics proposed in this study. The simulations are presented in Section 5. Finally, Section 6 presents the conclusions. 2. The Task Scheduling Problem Most grid applications usually involve partially ordered tasks and heterogeneous resources distributed in grid. Hence, the studied new task scheduling problem addresses precedence considerations and resource heterogeneity in grid environment. A simple example is given to illustrate the complexity and difficulty of the investigated scheduling problem; suppose a grid application is decomposed into 5 partially ordered tasks with different workloads as shown in Figure 1—two heterogeneous resources with different abilities in the grid environment. Therefore, tasks assigned to different resources would require different processing times to run. Additionally, involvement of communication costs such as data transfer for partially ordered tasks is considered. Meanwhile, assume that resource M2 has better performance than resource M1, but resource M1 has higher bandwidth than resource M2. Thus, communication time depends on the communication cost and the minimum 4 Abstract and Applied Analysis