Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Yang, Jinn‐Shyong; Xu, Hua; Pan, Li; Jia, Peifa; Long, Fei; Ming, Jie

doi:10.1016/j.asoc.2010.11.029

Cited by 34 publications

(7 citation statements)

References 26 publications

(45 reference statements)

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“…This algorithm uses different features to select tasks that enables algorithm to efficiently schedule selected tasks in a heterogeneous computing environment. Authors in the work (Yang et al, 2011) proposed a scheduling algorithm based on the Bayesian Optimization Algorithm (BOA) for heterogeneous computing environments. BOA initializes a Bayesian networks and learns all the dependencies between tasks in the network according to the task graph of multiprocessor scheduling problems to choose few best possible combinations of the task schedule.…”

Section: Related Workmentioning

confidence: 99%

Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

Shahid

Raza

Sajid

2015

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

Shahid

Raza

Sajid

2015

Journal of Systems and Software

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“…In general, those algorithms mimic natural mechanisms such as evolution, colony organisation, etc. Genetic Algorithms [6,14,21,41,43] and Particle Swarm Optimisation techniques [24,27,33] are widespread approaches used to address the scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

Min-heap-based scheduling algorithm: an approximation algorithm for homogeneous and heterogeneous distributed systems

Gabriel

Albertini

Castelo

et al. 2015

International Journal of Parallel, Emergent and Distributed Sys

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2015): Min-heap-based scheduling algorithm: an approximation algorithm for homogeneous and heterogeneous distributed systems, International Journal of Parallel, Emergent and Distributed Systems,One of the most important issues in the design of distributed systems is process scheduling, which maps applications to resources in an attempt to reduce the application execution time or maximise resource utilisation. The complexity involved in finding good scheduling solutions has motivated the design of several heuristics and approximation algorithms. Although heuristics aim at finding good solutions within acceptable time constraints, they do not guarantee solution quality. On the other hand, approximation-based algorithms provide optimal solution bounds, but they are usually designed to address simple scenarios. In an attempt to combine the best of both algorithmic approaches, this paper proposes the min-heap-based scheduling algorithm (MHSA), which finds solutions for homogeneous and heterogeneous distributed environments within acceptable time constraints and optimal solution bounds. MHSA considers applications that can be bag-of-tasks or which communicate among each other. Such solutions minimise the application execution time and maximise resource utilisation; therefore, MHSA is a system-oriented scheduling algorithm. Results confirm that MHSA provides better solutions than schedulers such as Random, Round-Robin, Workload-Queue and List-Scheduling. In addition, MHSA provides an a-approximation ratio for reaching optimal solution bounds.

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“…Jiadong et al (Yang et al 2011) made use of Bayesian optimization algorithm with structural learning capabilities to solve the task scheduling problem. The innate structure of a Bayesian network resembles that of directed acyclic graph, with nodes representing to some known variable such as the computation cost in the final schedule length and the directed arcs represent the inter task communication dependencies.…”

Section: Bayesian Neural Network (Bnn)mentioning

confidence: 99%

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

Hussain

Azam

Sharif

et al. 2016

Nepal Journal of Science and Technology

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Heterogeneous Distributed Computing Systems (HeDCS) efficiently utilize the heterogeneity of diverse computational resources which are interlinked through high speed networks for executing a group of computing intensive applications. Directed acyclic graphs (DAGs) are usually used to represent these parallel applications with varied computational requirements and constraints. The optimal scheduling of the given set of precedence constrained tasks to available resources is a core concern in HeDCS and is known to be NP Complete problem. Non deterministic nature of application programs and heterogeneous environment are the main challenges in designing, implementing and analyzing phases of task scheduling techniques. A myriad of heuristic and meta-heuristic approaches have been proposed in the literature to solve this complex problem. The basic purpose of this study is to cover ANN based task scheduling strategies in the distributed computing environment perspective. Further existing scheduling heuristics could be classified in a new state of art classification including the description of frequently used parameters in the mentioned scheduling strategies. The flexible and powerful nature of ANN for identifying the data patterns, underlying time and other constraints and learning capabilities have shown to be a promising candidate among other heuristics.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 69-78

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Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Cited by 34 publications

References 26 publications

Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

Min-heap-based scheduling algorithm: an approximation algorithm for homogeneous and heterogeneous distributed systems

A Survey on ANN Based Task Scheduling Strategies in Heterogeneous Distributed Computing Systems

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