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ALVEC, auto-scaling by lotka volterra elastic cloud: a qos aware non linear dynamical allocation model

Author

Listed:
  • Goswami, Bidisha
  • Sarkar, Jyotirmoy
  • Saha, Snehanshu
  • Kar, Saibal
  • Sarkar, Poulami

Abstract

Measurement of the dynamic elasticity of resource allocation in cloud computing continues to be a relevant problem in the related literature. Yet, there is scant evidence on determining the dynamic scaling quotient in such operations. Elasticity is defined as the ability to adapt to the changing workloads by provisioning and de-provisioning of Cloud resources and scaling is essential for maintaining elasticity in resource allocation. We propose ALVEC, as a model of resource allocation in Cloud data centers (Sarkar et al. , 2016) [7,16], to address dynamic allocation by auto-tuning the model parameters. The proposed model, governed by a coupled differential equation known as Lotka Volterra (LV), fares better for management of Service Level Agreement (SLA) and Quality of Services (QoS). We show evidence of true elasticity both in theoretical and numerical applications. Additionally, we show that ALVEC as an example of unsupervised resource allocation, is able to predict the future load and allocate virtual machines efficiently.

Suggested Citation

  • Goswami, Bidisha & Sarkar, Jyotirmoy & Saha, Snehanshu & Kar, Saibal & Sarkar, Poulami, 2018. "ALVEC, auto-scaling by lotka volterra elastic cloud: a qos aware non linear dynamical allocation model," MPRA Paper 103457, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103457
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    More about this item

    Keywords

    Resource allocation Elasticity Lotka Volterra (LV) Auto scaling Cloud systems modeling Simulation;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D2 - Microeconomics - - Production and Organizations
    • L8 - Industrial Organization - - Industry Studies: Services

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