Security, energy, and performance-aware resource allocation mechanisms for computational grids

  • Authors:

  • Joanna Kołodziej;Samee Ullah Khan;Lizhe Wang;Marek Kisiel-Dorohinicki;Sajjad A. Madani;Ewa Niewiadomska-Szynkiewicz;Albert Y. Zomaya;Cheng-Zhong Xu

  • Affiliations:

  • Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland;NDSU-CIIT Green Computing and Communications Laboratory, North Dakota State University, Fargo, ND 58108, USA;Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China;AGH University of Science and Technology, Cracow, al. Mickiewicza 30, 30-059 Cracow, Poland;COMSATS Institute of Information Technology (CIIT), Abbottabad 22060, Pakistan;Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, Poland;School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia;Department of Electrical and Computer Engineering Wayne State University, Detroit, MI, USA

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

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Abstract

Distributed Cyber Physical Systems (DCPSs) are networks of computing systems that utilize information from their physical surroundings to provide important services, such as smart health, energy efficient grid and cloud computing, and smart security-aware grids. Ensuring the energy efficiency, thermal safety, and long term uninterrupted computing operation increases the scalability and sustainability of these infrastructures. Achieving this goal often requires researchers to harness an understanding of the interactions between the computing equipment and its physical surroundings. Modeling these interactions can be computationally challenging with the resources on hand and the operating requirements of such systems. In this paper, we define the independent batch scheduling in Computational Grid (CG) as a three-objective global optimization problem with makespan, flowtime and energy consumption as the main scheduling criteria minimized according to different security constraints. We use the Dynamic Voltage Scaling (DVS) methodology for reducing the cumulative power energy utilized by the system resources. We develop six genetic-based single- and multi-population meta-heuristics for solving the considered optimization problem. The effectiveness of these algorithms has been empirically justified in two different grid architectural scenarios in static and dynamic modes.