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

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.

► We developed a generic security-aware scheduling model in computational grids.
► Makespan, flowtime and energy consumption are defined as scheduling criteria.
► We evaluated six multi-population energy-aware GA-based schedulers for this model.
► The effectiveness of the schedulers has been empirically justified in risky and secure scenarios.