DAG Scheduling on Heterogeneous Distributed Systems Using Learning Automata

Ghader, Habib Moti; KeyKhosravi, Davood; Hosseinalipour, Ali

doi:10.1007/978-3-642-12101-2_26

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Moti Ghader et al [37] proposed a learning automatabased algorithm for static task graph scheduling. This algorithm uses learning automata to represent different possible scheduling.…”

Section: Background and Previous Workmentioning

confidence: 99%

Learning Based Genetic Algorithm for Task Graph Scheduling

Izadkhah

2019

Applied Computational Intelligence and Soft Computing

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Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.

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“…Moti Ghader et al [37] proposed a learning automatabased algorithm for static task graph scheduling. This algorithm uses learning automata to represent different possible scheduling.…”

Section: Background and Previous Workmentioning

confidence: 99%

Learning Based Genetic Algorithm for Task Graph Scheduling

Izadkhah

2019

Applied Computational Intelligence and Soft Computing

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“…b. Computation cost matrix[20] 2010 International Conference of Soft Computing and Pattern Recognitiona. Acquired Gantt chart of DLS Algorithm b.…”

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confidence: 99%

Static task graph scheduling using learner Genetic Algorithm

Ghader

Fakhr

Javadi

et al. 2010

2010 International Conference of Soft Computing and Pattern Recognition

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Task graph scheduling is one of the NP-Hard problems. So many classic and non-classic methods are proposed for solution of this problem. One of the crucial methods that, applied for solving this problem is Genetic Algorithm. In this paper we propose a new algorithm that named Learner Genetic Algorithm (LGA). Our proposed algorithm based on Genetic Algorithm, but in new proposed algorithm the Learning Process is attached for Genetic Algorithm. The scheduling resulted from applying our proposed algorithm to some benchmark task graphs are compared with the existing ones.

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LATA: learning automata-based task assignment on heterogeneous cloud computing platform

Gheisari,

ShokrZadeh

2024

J Supercomput

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A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

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DAG Scheduling on Heterogeneous Distributed Systems Using Learning Automata

Cited by 6 publications

References 10 publications

Learning Based Genetic Algorithm for Task Graph Scheduling

Learning Based Genetic Algorithm for Task Graph Scheduling

Static task graph scheduling using learner Genetic Algorithm

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

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