Path-Based Heuristic Task Scheduling Algorithm for Heterogeneous Distributed Computing Systems

2014

IJCA

Efficient task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems. Several algorithms are proposed for heterogeneous distributed computing systems. In this paper, a new static scheduling algorithm is proposed called Highest Communicated Path of Task (HCPT) algorithm to efficiently schedule tasks on the heterogeneous distributed computing systems. Our algorithm is based on the list-scheduling technique. The algorithm not only is focused on reducing the makespan, but also provides better performance than the other algorithms in terms of speedup and efficiency. It consists of three phases, level sorting phase, taskprioritizing phase and processor selection phase. From the theoretical analysis of the HCPT algorithm with other algorithms for a Directed A-cyclic Graph (DAG), the better performance is observed.

“…Efficiency as the speedup divided by the number of processors used [4] that is described in Equation (12).…”

Section: Efficiencymentioning

confidence: 99%

“…The PHTS algorithm is proposed for a bounded number of heterogeneous processors consisting of three phases namely, a path-prioritizing phase, task selection phase, and processor selection phase [12]. Path prioritizing phase for computing the priorities for all possible paths.…”

Section: Path-based Heuristic Task Scheduling Algorithmmentioning

confidence: 99%

Task Scheduling Optimization in Heterogeneous Distributed Systems

Nasr¹,

2014

IJCA

“…The PHTS algorithm is proposed for a bounded number of heterogeneous processors consisting of three phases namely, a path-prioritizing phase, task selection phase, and processor selection phase [25]. Path prioritizing phase for computing the priorities for all possible paths.…”

Section: Path-based Heuristic Task Scheduling Algorithmmentioning

confidence: 99%

Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems

Nasr¹,

2015

ijacsa

Abstract-Efficient task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems. Some algorithms have been proposed for both homogeneous and heterogeneous distributed computing systems. In this paper, a new static scheduling algorithm is proposed called Node Duplication in Critical Path (NDCP) algorithm to schedule the tasks efficiently on the heterogeneous distributed computing systems. The NDCP algorithm focuses on reducing the makespan and provides better performance than the other algorithms in metrics of speedup and efficiency. It consists of two phases, priority phase and processor selection phase. From the theoretical analysis of the NDCP algorithm with other algorithms for a Directed Acyclic Graph (DAG), the better performance is observed.

“…List scheduling consists of two phases: a task prioritization phase, where a certain priority is computed and is assigned to each node of the DAG, and a machine assignment phase, where each task (in order of its priority) is assigned to machine that minimizes a suitable cost function. List-scheduling is generally accepted as an attractive approach since it pairs low complexity with good results [9,10]. Examples of list-based algorithms are: Heterogeneous Earliest Finish Time (HEFT) and Critical Path On Processor (CPOP) [11].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Algorithm for High Performance Heterogeneous Distributed Computing Systems

Nasr¹,

2015

IJCA

The main objective of task scheduling is to assign tasks onto available processors with the aim of producing minimum schedule length and without violating the precedence constraints. Several algorithms have been proposed for solving task-scheduling problem. The most of them doesn't take into account the average communication of parents and data ready time. In this paper, a new static scheduling algorithm is proposed called Communication Leveled DAG with Duplication (CLDD) algorithm to efficiently schedule tasks on the heterogeneous distributed computing systems. It solves most limitations of existing algorithms. The algorithm not only focuses on reducing the makespan, but also provides better performance than the other algorithms in terms of speedup, efficiency and time complexity. It consists of three phases, level sorting phase, task-prioritizing phase and processor selection phase. We evaluate the performance of our algorithm by applying it on random DAGs. According to the evolved results, it has been found that our algorithm outperform the others.