Scheduling scientific workflow tasks in cloud using swarm intelligence

Vinothina, V.

doi:10.1109/icctac.2017.8249991

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…e algorithms NSGA-I, NSGA-II, NSGA-III [10], and MOEA/D and the RIOPSO algorithm proposed in this paper are used to solve the problem of cloud resource scheduling.…”

Section: Evaluation and Analysis Of Algorithm Performance Indexmentioning

confidence: 99%

“…Step 2 Build the non-base column (8) For k � 2 to Jdo (9) Begin (10) 14) End (15) Step 3 Add 1 to ai, j (16) 1 ≤ i ≤ row and 1 ≤ j ≤ Ele 5. e outcomes of performance measures are reported as mean values.…”

Section: Evaluation and Analysis Of Algorithm Performance Indexmentioning

confidence: 99%

“…Scholars in the United States and overseas frequently employ clever optimization techniques to tackle the challenge of cloud resource scheduling. Genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], and ant colony optimization (ACO) [11] are examples of intelligent optimization techniques. Because the PSO method has no overlap and mutation in processing and implementation as compared to the GA algorithm, it is quicker at handling cloud resource scheduling challenges.…”

Section: Introductionmentioning

confidence: 99%

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Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

AlSuwaidan

Khan

Almakki

et al. 2022

Mathematical Problems in Engineering

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A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.

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“…e algorithms NSGA-I, NSGA-II, NSGA-III [10], and MOEA/D and the RIOPSO algorithm proposed in this paper are used to solve the problem of cloud resource scheduling.…”

Section: Evaluation and Analysis Of Algorithm Performance Indexmentioning

confidence: 99%

Section: Evaluation and Analysis Of Algorithm Performance Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

AlSuwaidan

Khan

Almakki

et al. 2022

Mathematical Problems in Engineering

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“…In the traditional method, oversubscription is used to enhance system utilization. V. March et al [10] suggested a batch scheduler for HPC with virtualization technology.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch

Kumar

Sharma

2022

IJEER

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Recent technological advancements in the IT field have pushed many products and technologies into the cloud. In the present scenario, the cloud service providers mainly focus on the delivery of IT services and technologies rather than throughput. In this research paper, we used a scalable cost-effective approach to configure AWS Batch with AWS Fargate and CloudFormation and implemented it in order to handle a heavy workload. The AWS service configuration procedure, GitHub repository, and Docker desktop applications have been clearly described in this work. A cost-effective configuration and architecture of AWS Batch processing are given to provide high throughput. The processing of heavy workload by AWS Batch is represented in terms of execution time and the result shows that the concurrent execution reduces the execution time. To enhance the throughput heavy workload using batch processing an "Amazone FSx for Lustre" can also be used.

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“…The work of Reference proposes a Ant Colony algorithm for scheduling tasks. They show improvement of previous work on makespan minimizing, but lack of other metrics.…”

Section: Related Workmentioning

confidence: 99%

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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Scheduling scientific workflow tasks in cloud using swarm intelligence

Cited by 6 publications

References 13 publications

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

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