Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms

  • Authors:

  • George Kousiouris;Andreas Menychtas;Dimosthenis Kyriazis;Spyridon Gogouvitis;Theodora Varvarigou

  • Affiliations:

  • Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, GR-15773 Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, GR-15773 Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, GR-15773 Athens, Greece and Department of Digital Systems, University of Pira ...;Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, GR-15773 Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, GR-15773 Athens, Greece

  • Venue:
  • Future Generation Computer Systems
  • Year:
  • 2014

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Abstract

Delivering Internet-scale services and IT-enabled capabilities as computing utilities has been made feasible through the emergence of Cloud environments. While current approaches address a number of challenges such as quality of service, live migration and fault tolerance, which is of increasing importance, refers to the embedding of users' and applications' behaviour in the management processes of Clouds. The latter will allow for accurate estimation of the resource provision (for certain levels of service quality) with respect to the anticipated users' and applications' requirements. In this paper we present a two-level generic black-box approach for behavioral-based management across the Cloud layers (i.e., Software, Platform, Infrastructure): it provides estimates for resource attributes at a low level by analyzing information at a high level related to application terms (Translation level) while it predicts the anticipated user behaviour (Behavioral level). Patterns in high-level information are identified through a time series analysis, and are afterwards translated to low-level resource attributes with the use of Artificial Neural Networks. We demonstrate the added value and effectiveness of the Translation level through different application scenarios: namely FFMPEG encoding, real-time interactive e-Learning and a Wikipedia-type server. For the latter, we also validate the combined level model through a trace-driven simulation for identifying the overall error of the two-level approach.