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CRFF.GP: cloud runtime formulation framework based on genetic programming

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Abstract

The runtime environment of cloud computing requires a scheduler to be adapted with the current runtime conditions or make itself ready for the prospective events by predicting the future that requires adaptive scheduling algorithms. A description of the runtime environment based on a formula can help scheduler not only to control the current state of the runtime environment, but also to speculate about the future. In the previously published literature, static formulas were used to model the runtime, and dynamic runtime conditions are not considered. In this paper, cloud runtime formulation framework is introduced to create a formula based on the current runtime conditions. Then, using the genetic programming technique, the formula is evolved based on the feedback received from the runtime environment. This framework creates a suitable formula using length, deadline and priority features of the tasks, and frequency of virtual machines. Accordingly, the scheduler is able to (a) place the tasks in the virtual machines; and (b) set the processor frequency of the virtual machines, accordingly. The simulation of the presented idea compared to the baseline research works in this field, makes it possible to achieve a service level agreement (SLA)’s conformance 97% in average, with an increase of 19% compared to the baseline research works. In addition, the proposed algorithm, when it has 100% throughput, showed 10% improvement in SLA commitment in comparison with the fundamental algorithms.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Shokooh Kamalinasab, Faramarz Safi-Esfahani & Majid Shahbazi

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Shokooh Kamalinasab, Faramarz Safi-Esfahani & Majid Shahbazi

Authors
  1. Shokooh Kamalinasab
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  2. Faramarz Safi-Esfahani
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  3. Majid Shahbazi
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Correspondence to Faramarz Safi-Esfahani.

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Kamalinasab, S., Safi-Esfahani, F. & Shahbazi, M. CRFF.GP: cloud runtime formulation framework based on genetic programming. J Supercomput 75, 3882–3916 (2019). https://doi.org/10.1007/s11227-019-02750-8

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