HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

Delavar, Arash Ghorbannia; Aryan, Yalda

doi:10.1007/s10586-013-0275-6

Cited by 78 publications

(11 citation statements)

References 12 publications

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“…Delava and Aryan [45] proposed a hybrid heuristic algorithm (HSGA) for finding the optimal solution in terms of makespan and load balancing of workflow scheduling in a cloud computing environment. The proposed hybrid algorithm used Best-Fit and Round Robin algorithms to obtain the genetic algorithm's initial population.…”

Section: Hybrid Using Gamentioning

confidence: 99%

Hybrid scheduling algorithms in cloud computing: a review

Arora

Banyal

2022

IJECE

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<p><span>Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms.</span></p>

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Section: Hybrid Using Gamentioning

confidence: 99%

Hybrid scheduling algorithms in cloud computing: a review

Arora

Banyal

2022

IJECE

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“…Each row in the text file describes the size of a specific job in terms of Millions of Instructions (MI). The Monte Carlo simulation method [2] is employed to generate the dataset comprised of any required number of jobs. The specification of the GoCJ dataset is presented in Table 1.…”

Section: Data Descriptionmentioning

confidence: 99%

GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures

Hussain¹,

Aleem²

2018

Data

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Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmark their applications using an open-source benchmark. To meet these objectives, the contemporary state of the art has been scrutinized to explore a real workload behavior in Google cluster traces. Starting from smaller- to moderate-size cloud computing infrastructures, the dataset generation process is demonstrated using the Monte Carlo simulation method to produce a Google Cloud Jobs (GoCJ) dataset based on the analysis of Google cluster traces. With this article, the dataset is made publicly available to enable other researchers in the field to investigate and benchmark their scheduling and resource-allocation schemes for the cloud. The GoCJ dataset is archived and available on the Mendeley Data repository.

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“…The algorithm would stop upon meeting a stop conditions. The conditions to end the process are [13]:  Number of generations, will reach to a maximum bound …”

Section: Stop Conditionsmentioning

confidence: 99%

“…The experiments are conducted considering HDS systems with respect to heterogeneity in resources and tasks properties as in some previous works such as random DAG generator in [21,25] with different complexity rate, in order to simulate workflow applications. Some other parameters applied are from [19,21,26,13] that are listed in tables 1. The parameters used in GA values such as probability for crossover operation and mutation operation are 0.2 and 0.125 respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems

Delavar¹,

Aryan²

2012

IJCA

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In heterogeneous distributed systems like grid and cloud computing infrastructures, the major problem is the task scheduling which can have much impact on system performance. For some reasons, such as heterogeneous and dynamic features and the dependencies among the requests, this issue is known as a NP-hard problem. In this article a hybrid meta-heuristic method based on Genetic Algorithm (GMSW) is being proposed in order to find a suitable solution for mapping the requests on resources. The proposed method tries to obtain the response quickly, with some goal-oriented operations. It begins, through making a good initial population by merging some features of the Best-Fit and Round Robin methods and a bi-directional tasks prioritization in unbalanced-structured workflow, considering their impact on each other, based on graph topology. Some other operations control and lead the algorithm steps in order to obtain the solution by using efficient parameters in the mentioned systems. Here the focus is on optimizing the makespan and reliability, by considering a good distribution of workload on resources. The experiments here indicate that the GMSW improves the results, with the increasing number of tasks in application graph, for the mentioned objectives. The results are compared with other studied algorithms.

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HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

Cited by 78 publications

References 12 publications

Hybrid scheduling algorithms in cloud computing: a review

Hybrid scheduling algorithms in cloud computing: a review

GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures

A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems

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