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Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers

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Abstract

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm.

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Notes

  1. In this paper, critical conditions are conditions defined based on the system status from the aspect of processing power and available memory value. On that basis, an overloaded system with poor processing power or low memory is in critical conditions.

  2. Local Regression.

  3. This has been considered with the assumption that the VM images and data are stored in a shared storage in the network. In fact, there is no need for copying the storage space concerning the VM with this assumption. This has been considered for further simplification of the job. On the other hand, half of the bandwidth has been considered in the simulations made for migration and the other half for VM communication.

  4. Critical server.

  5. CPU Usage.

  6. In our implementation, it is accepted in case it is better and it is accepted conditionally and with the probability of \(p= \frac{-\triangle f}{T}\) in case it is worse. However, some other functions can be provided for p and it only needs some certain conditions.

  7. http://www.spec.org.

  8. Local regression-maximum correlation.

  9. Local regression-minimum migration time.

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Acknowledgments

This work is sponsored by Islamic Azad University Science and Research Branch. We thank them for their support.

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Authors and Affiliations

  1. Science and Reaserch Branch, Islamic Azad University, Tehran, Iran

    Mehdi Rajabzadeh

  2. Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Abolfazl Toroghi Haghighat

Authors
  1. Mehdi Rajabzadeh
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  2. Abolfazl Toroghi Haghighat
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Correspondence to Mehdi Rajabzadeh.

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Rajabzadeh, M., Haghighat, A.T. Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J Supercomput 73, 2001–2017 (2017). https://doi.org/10.1007/s11227-016-1900-y

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  • DOI: https://doi.org/10.1007/s11227-016-1900-y

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