Prediction and control of bursty cloud workloads: A fractal framework

Mahboobeh Ghorbani, Yanzhi Wang, Yuankun Xue, Massoud Pedram, Paul Bogdan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Cloud Computing is a promising approach to handle the growing needs for computation and storage in an efficient and cost-effective manner. Towards this end, characterizing workloads in the cloud infrastructure (e.g., a data center) is essential for performing cloud optimizations such as resource provisioning and energy minimization. However, there is a huge gap between the characteristics of actual workloads (e.g., they tend to be bursty and exhibit fractal behavior) and existing cloud optimization algorithms, which tend to rely on simplistic assumptions about the workloads. To close this gap, based on fractional calculus concepts, we present a fractal model to account for the complex dynamics of cloud computing workloads (i.e., the number of request arrivals or CPU/memory usage during each time interval). More precisely, we introduce a fractal operator to account for the time-varying fractal properties of the cloud workloads. In addition, we present an efficient (online) parameter estimation algorithm, an accurate forecasting strategy, and a novel fractal-based model predictive control approach for optimizing the CPU utilization, and hence, the overall energy consumption in the system while satisfying networked architecture performance constraints like queue capacities. We demonstrate advantages of our fractal model in forecasting the complex cloud computing dynamics over conventional (non-fractal) models by using real-world cloud (Google) traces. Unlike non-fractal models, which have very poor prediction capabilities under bursty workload conditions, our fractal model can accurately predict bursty request processes, which is crucial for cloud computing workload forecasting. Finally, experimental results demonstrate that the fractal model based optimization outperforms the non-fractal based ones in terms of minimizing the resource utilization by an average of 30%.

Original languageEnglish (US)
Title of host publication2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450330510
DOIs
StatePublished - Oct 12 2014
Event2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014 - New Delhi, India
Duration: Oct 12 2014Oct 17 2014

Publication series

Name2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014

Other

Other2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014
CountryIndia
CityNew Delhi
Period10/12/1410/17/14

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

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    Ghorbani, M., Wang, Y., Xue, Y., Pedram, M., & Bogdan, P. (2014). Prediction and control of bursty cloud workloads: A fractal framework. In 2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014 [a12] (2014 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2014). Association for Computing Machinery, Inc. https://doi.org/10.1145/2656075.2656095