A GPU Based Approach for Solving the Workflow Scheduling Problem

2019 ◽  
Vol 9 (4) ◽  
pp. 1-12
Author(s):  
Mohammed Benhammouda ◽  
Mimoun Malki
Keyword(s):  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Main Idea ◽  
Massively Parallel ◽  
Workflow Scheduling ◽  
Scheduling Problem ◽  
On Demand ◽  
Parallel Version ◽  
Np Hard Problem ◽  
Annealing Algorithm

Cloud computing is considered a new way to use on-demand computing resources. When executing a workflow process in such an environment, task scheduling, a well-known NP-hard problem is a very important step. Many heuristic algorithms have been proposed to solve this problem. In this article, the authors present a GPU-based approach for solving the workflow scheduling problem. The main idea of the approach is to implement a massively parallel version of the simulated annealing algorithm, in an asynchronous way where no information is exchanged among parallel runs. The proposed approach, called PSA algorithm, is against another well-established scheduling HEFT heuristic. Experiments with randomly generated graphs show a much better performance from the proposed approach.

scholarly journals Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Feifeng Zheng ◽  
Zhaojie Wang ◽  
Yinfeng Xu ◽  
Ming Liu
Keyword(s):  
Numerical Experiments ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Open Map ◽  
Annealing Algorithm ◽  
Mapreduce Scheduling

Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume that all the operations cannot be processed in parallel, and the machine settings are unrelated machines. For solving the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function. We then propose a genetic algorithm, a simulated annealing algorithm, and an L-F algorithm to solve this problem. Numerical experiments show that L-F algorithm has better performance in solving this problem.

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

Hybrid Method for Solving Flexible Open Shop Scheduling Problem with Simulated Annealing Algorithm and Multi-Agent Approach

2011 ◽  
Vol 383-390 ◽  
pp. 4612-4619 ◽  
Author(s):  
Tadeusz Witkowski ◽  
Paweł Antczak ◽  
Arkadiusz Antczak
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
The Other ◽  
Scheduling Problem ◽  
Open Shop Scheduling ◽  
Hand Search ◽  
Shop Scheduling ◽  
Multi Agent ◽  
Annealing Algorithm ◽  
Metaheuristic Optimization Algorithm

In this study we propose metaheuristic optimization algorithm, in which simulated annealing, multi agent approach with fuzzy logic are used. On the first level of solution search the multi agent approach is used, and on the second level – the simulated annealing. Two types of routing were considered: a serial and a parallel one. The multi-agent approach emphasizes flexibility rather than the optimality of solutions. On the other hand, search approaches such as simulated annealing, which focus more on the optimality of solutions.

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