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Reliability-Aware Workflow Scheduling Using Monte Carlo Failure Estimation in Cloud

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Proceedings of International Conference on Communication and Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 508))

Abstract

Cloud Computing is a novel paradigm which offers large-scale resources and services through the Internet. These services are supported by huge data centers with thousands of servers. One of the core issues in Cloud is the proper utilization of computation power. Efficient task scheduling can help utilize the cloud resources up to their capacity. Moreover, in real-world scenarios, it is important to consider the reliability of computation resources at the time of scheduling since the failure of tasks can be critical to both the cloud service provider and the user. In this paper, we proposed a Cloud computing framework to model the failure characteristics of a cloud environment. We developed a Monte Carlo Failure Estimation (MCFE) algorithm that considers Weibull distributed failures in cloud, using Monte Carlo simulation method to determine the probable occurrence of failures and a Failure-Aware Resource Scheduling (FARS) algorithm that considers the reliability of task execution while assigning tasks in a workflow application to virtual machines. In order to analyze the performance of our algorithm, we compared it with the popular scheduling algorithm namely HEFT. For simulation analysis, randomly generated task graphs and task graphs for numerical real world problems like Gaussian Elimination (GE) and Fast Fourier Transformation (FFT) were considered. The simulation results show that the proposed algorithm performs better in real world scenarios where reliability is a critical issue.

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Authors and Affiliations

  1. Computer Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

    Nidhi Rehani & Ritu Garg

Authors
  1. Nidhi Rehani
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  2. Ritu Garg
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Corresponding author

Correspondence to Nidhi Rehani .

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Editors and Affiliations

  1. Narsinhbhai Inst. of Comp. Stud. & Mngmt, Professor and Head Narsinhbhai Inst. of Comp. Stud. & Mngmt, Kadi, Mehsana, Gujarat, India

    Nilesh Modi

  2. The University of Oklahoma, Director, Telecommunication Engineering The University of Oklahoma, Norman, Oklahoma, Oklahoma, USA

    Pramode Verma

  3. GLS University, Dean & Faculty of Computer Technology GLS University, Ahmedabad, Gujarat, India

    Bhushan Trivedi

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© 2017 Springer Nature Singapore Pte Ltd.

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Rehani, N., Garg, R. (2017). Reliability-Aware Workflow Scheduling Using Monte Carlo Failure Estimation in Cloud. In: Modi, N., Verma, P., Trivedi, B. (eds) Proceedings of International Conference on Communication and Networks. Advances in Intelligent Systems and Computing, vol 508. Springer, Singapore. https://doi.org/10.1007/978-981-10-2750-5_15

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  • DOI: https://doi.org/10.1007/978-981-10-2750-5_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2749-9

  • Online ISBN: 978-981-10-2750-5

  • eBook Packages: EngineeringEngineering (R0)

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