A Cellular Genetic Algorithm for scheduling applications and energy-aware communication optimization

In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on the computing platform. Accounting for these communications is essential for attaining efficient hardware and software utilization. Moreover, energy dissipation due to the transfer of data between processing elements has become a major concern. Therefore, in this paper we develop an energy-aware static algorithm, which intrinsically optimizes the energy consumption due to the transfer of data in a distributed system. This is achieved by properly allocating and scheduling the tasks that constitute the applications on the processing elements, minimizing inter-processor communications. The proposed algorithm is a new Cellular Genetic Algorithm based on task clustering techniques. That is, the genetic operators work considering groups of tasks instead of applying them directly on the tasks. Simulation results showed that this algorithm is very compelling in terms of application completion time, inter-processor communication and energy communication dissipation.