Energy-Aware Grid Scheduling of Independent Tasks and Highly Distributed Data

Data-aware scheduling in today's large-scale computing systems has become a major complex research issue. This problem becomes even more challenging when data is stored and accessed from many highly distributed servers and energy-efficiency is treated as a main scheduling objective. In this paper we approach the independent batch scheduling in grid environment as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We used the Dynamic Voltage and Frequency Scaling (DVFS) model for reducing the cumulative power energy utilized by the system resources for tasks executions. We developed for data transmission a general logical network topology and policy based on the sleep link-based Adaptive Link Rate (ALR) on/off technique. Two developed energy-aware grid schedulers are based on genetic algorithms (GAs) frameworks with elitist and struggle replacement mechanisms and were empirically evaluated for four grid size scenarios in static and dynamic modes. The simulation results show that the proposed schedulers perform to a level that is sufficient to maintain the desired quality levels.