scholarly journals Task Scheduling in Heterogeneous Multiprocessor Environments – An Efficient ACO-Based Approach

2018 ◽  
Vol 10 (1) ◽  
pp. 320 ◽  
Author(s):  
Nekiesha Edward ◽  
Jeffrey Elcock
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Directed Acyclic Graphs ◽  
Food Sources ◽  
Scheduling Problem ◽  
Ant Colonies ◽  
Acyclic Graphs ◽  
Critical Issues

In heterogeneous computing environments, finding optimized solutions continues to be one of the most important and yet, very challenging problems. Task scheduling in such environments is NP-hard, so efficient mapping of tasks to the processors remains one of the most critical issues to be tackled. For several types of applications, the task scheduling problem is crucial, and across the literature, a number of algorithms with several different approaches have been proposed. One such effective approach is known as Ant Colony Optimization (ACO). This popular optimization technique is inspired by the capabilities of ant colonies to find the shortest paths between their nests and food sources. Consequently, we propose an ACO-based algorithm, called rACS, as a solution to the task scheduling problem. Our algorithm utilizes pheromone and a priority-based heuristic, known as the upward rank value, as well as an insertion-based policy and a pheromone aging mechanism to guide the ants to high quality solutions. To evaluate the performance of our algorithm, we compared our algorithm with the ACS algorithm and the ACO-TMS algorithm using randomly generated directed acyclic graphs (DAGs). The simulation results indicated that our algorithm experienced comparable or even better performance, than the selected algorithms.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

Maintaining Constrained Path Problem for Directed Acyclic Graphs

2021 ◽  
Vol 21 (03) ◽  
Author(s):  
Chenying Hao ◽  
Shurong Zhang ◽  
Weihua Yang
Keyword(s):  
Shortest Path ◽  
Time Complexity ◽  
Directed Acyclic Graph ◽  
Shortest Paths ◽  
Directed Acyclic Graphs ◽  
Shortest Path Algorithm ◽  
Acyclic Graphs ◽  
Main Technique ◽  
Number Formula ◽  
The One

In order to restore the faulty path in network more effectively, we propose the maintaining constrained path problem. Give a directed acyclic graph (DAG) [Formula: see text] with some faulty edges, where [Formula: see text], [Formula: see text]. For any positive number [Formula: see text], we give effective maintain algorithm for finding and maintaining the path between source vertex [Formula: see text] and destination [Formula: see text] with length at most [Formula: see text]. In this paper, we consider the parameters [Formula: see text] and [Formula: see text] which are used to measure the numbers of edges and vertices which are influenced by faulty edges, respectively. The main technique of this paper is to define and solve a subproblem called the one to set constrained path problem (OSCPP) which has not been addressed before. On the DAG, compared with the dynamic shortest path algorithm with time complexity [Formula: see text] [16] and the shortest path algorithm with time complexity [Formula: see text] [18], based on the algorithm for OSCPP, we develop a maintaining constrained path algorithm and improve the time complexity to [Formula: see text] in the case that all shortest paths from each vertex [Formula: see text] to [Formula: see text] have been given.

Approximately Counting Approximately-Shortest Paths in Directed Acyclic Graphs

2014 ◽  
Vol 58 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Matúš Mihalák ◽  
Rastislav Šrámek ◽  
Peter Widmayer
Keyword(s):  
Shortest Paths ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Debabrata Senapati ◽  
Arnab Sarkar ◽  
Chandan Karfa
Keyword(s):  
Heterogeneous Computing ◽  
Computation Time ◽  
Directed Acyclic Graphs ◽  
Heuristic Solution ◽  
Task Graphs ◽  
Solution Generation ◽  
Acyclic Graphs ◽  
Heterogeneous Distributed Systems ◽  
Computing Platforms ◽  
Search Approach

The problem of scheduling Directed Acyclic Graphs in order to minimize makespan ( schedule length ), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presents HMDS-Bl , a list-based heuristic makespan minimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently, HMDS-Bl has been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm called HMDS . HMDS has been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance ( makespan ) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown that HMDS is able to comprehensively outperform state-of-the-art algorithms such as HEFT , PEFT , PPTS , etc., in terms of archived makespans while incurring bounded additional computation time overhead.

Improved Decomposition-Based Global EDF Scheduling of DAGs

2018 ◽  
Vol 27 (07) ◽  
pp. 1850101 ◽  
Author(s):  
Xu Jiang ◽  
Xiang Long
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Directed Acyclic Graphs ◽  
Scheduling Problem ◽  
Multicore Systems ◽  
Decomposition Approach ◽  
The Real ◽  
Acyclic Graphs ◽  
Different Types ◽  
Time Systems

Recently, an increasing number of real-time systems are implemented on multicore systems. To fully utilize the computation power of multicore systems, the scheduling problem of the real-time parallel task model is receiving more attention. Different types of scheduling algorithms and analysis techniques have been proposed for parallel real-time tasks modeled as directed acyclic graphs (DAG). In this paper, we study the scheduling problem for DAGs under the decomposition paradigm. We propose a new schedulability test and corresponding decomposition strategy. We show that this new decomposition approach strictly dominates the latest decomposition-based approach. Simulations are conducted to evaluate the real-time performance of our proposed scheduling algorithm, against the state-of-the-art scheduling and analysis methods of different types. Experimental results show that our method consistently outperforms other global methods under different parameter settings.

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