A formal approach to the quantification of sustainability and dependability metrics on data center infrastructures

  • Authors:

  • G. Callou;E. Sousa;P. Maciel;E. Tavares;B. Silva;J. Figueirêdo;C. Araujo;F. Magnani;F. Neves

  • Affiliations:

  • Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil;Federal University of Pernambuco (UFPE), Recife, Brazil

  • Venue:
  • Proceedings of the 2011 Symposium on Theory of Modeling & Simulation: DEVS Integrative M&S Symposium
  • Year:
  • 2011

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Abstract

Sustainability has received great attention by the scientific community, due to concerns for meeting current needs of energy without compromising, for instance, non-renewable resources for future generations. In addition, as a result of stringent availability constraints, dependability plays an prominent role in the infrastructure that supports business service through the Internet, particularly, the growth of cloud computing paradigm. In this context, tools are important to support data center designers to estimate the environmental impact, dependability as well as the cost associated to the infrastructure before implementing it. This paper presents a methodology for estimating sustainability impact and dependability metrics, supported by an integrated environment, namely, ASTRO, which considers the advantage of both Reliability Block Diagrams (RBD) and Stochastic Petri Nets (SPN). ASTRO has been developed to evaluate data center infrastructures, but the environment is generic enough to evaluate general systems. Besides, real-world case studies considering 5 different data center power infrastructures are provided to demonstrate the applicability of the proposed methodology as well as the environment.