Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems

López, María del Mar; Heymann, Elisa; Senar, Miquel A.

doi:10.1109/ispdc.2006.9

Cited by 28 publications

(12 citation statements)

References 12 publications

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“…1. Comparative studies in [25,101,165] show that static scheduling outperforms dynamic scheduling in most cases from different perspectives. The reason is that static scheduling is able to globally search from solution space at workflow level.…”

Section: Taxonomy Of Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

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To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem-solution manner. Based on the analysis, we also highlight some research directions for future investigation.

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Section: Taxonomy Of Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

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“…Lòpez and Senar [26] analyzed the performance of several DAG scheduling heuristics and their counterpart dynamic version (with rescheduling) with a stochastic model. However, these studies do not address the issue of how to take advantage of a stochastic model to produce efficient full-ahead schedules for the DAGs.…”

Section: Background and Related Workmentioning

confidence: 99%

Stochastic DAG scheduling using a Monte Carlo approach

Zheng

Sakellariou

2013

Journal of Parallel and Distributed Computing

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“…Lòpez and Senar [34] analyzed the performance of several DAG scheduling heuristics and their counterpart dynamic version (with rescheduling) with a stochastic model. However, these studies do not address the issue of how to take advantage of a stochastic model to produce efficient full-ahead schedules for the DAGs.…”

Section: Background and Related Workmentioning

confidence: 99%

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

Zheng

Sakellariou

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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Abstract-In most heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, then we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte-Carlo method. The approach is built on the top of a classic static scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement on average application performance can be achieved by the proposed approach at a reasonable execution time cost.

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Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems

Cited by 28 publications

References 12 publications

Workflow scheduling in cloud: a survey

Workflow scheduling in cloud: a survey

Stochastic DAG scheduling using a Monte Carlo approach

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

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