Fast workflow scheduling for grid computing based on a multi-objective Genetic Algorithm

Khajemohammadi, Hassan; Fanian, Ali; Gulliver, T. Aaron

doi:10.1109/pacrim.2013.6625456

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…In addition, many works took into account the workflow budget. Khajemohammadi et al [8] presented a multi-objective genetic algorithm to optimize both workflow cost and makespan. They generated a feasible solution through developing a multi-level workflow model.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the work in [5], we use a provider-type-VM-order nested strategy to encode the workflow scheduling problem. A particle represents a workflow scheduling strategy, and the i th particle in t th iteration is shown in formula (8).…”

Section: B Adpsoga Algorithmmentioning

confidence: 99%

“…Workflow scheduling has been broadly studied for many years in traditional environments [5]- [8], such as clusters and Grid. Most of the previous works aggressively pursued the minimum workflow makespan (i.e., total execution time) and the maximum resource utilization.…”

Section: Introductionmentioning

confidence: 99%

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Cost-Driven Scheduling for Deadline-Based Workflow Across Multiple Clouds

Guo

Lin

Chen

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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With the development of cloud computing, the coexistence of multiple cloud service providers appears in the current cloud market. Due to heterogeneous instance types, different bandwidths and various price models among multiple clouds, it is a challenging issue to schedule a deadline-constrained scientific workflow across multiple clouds. Existing research for workflow scheduling are mostly in the traditional distributed computing environment (such as grid), and only a few primal contributions are made in the cloud environment. This paper proposes a scheduling strategy for a deadline-constrained scientific workflow across multiple clouds. In order to minimize the execution cost of the workflow while meeting its deadline, our strategy utilizes the discrete particle swarm optimization technique, and adopts randomly two-point crossover operator and randomly single point mutation operator of the genetic algorithm. Besides, the strategy optimizes the performance for both computation cost and data transfer cost across multiple clouds. Our strategy is evaluated through well-known workflows, and experimental results show that it performs better than other state-of-the-art strategies.

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Section: Related Workmentioning

confidence: 99%

Section: B Adpsoga Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Cost-Driven Scheduling for Deadline-Based Workflow Across Multiple Clouds

Guo

Lin

Chen

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…The pareto solution is decided by the user based on the constraints and weight. To analyze the objectives like make span, cost, energy and reliability the algorithm is modified [19].…”

Section: Heuristic Approachmentioning

confidence: 99%

Trust Based Meta-Heuristics Workflow Scheduling in Cloud Service Environment

Rathanam¹,

Rajaram²

2016

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Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Workflow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.

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“…One of the suitable algorithms for WFS is the Genetic Algorithm (GA). The GA offers a dynamic search technique and determines a quality solution from an enormous search space in polynomial time [33,36,94,95].…”

Section: Time Complexitymentioning

confidence: 99%