Grid-level Brokering to save energy

Abstract

This chapter presents the time, cost, and energy saving Intelligent ApplicatioN Oriented Scheduling (hereafter ïanos) solution to adapt a set of computing resources to the needs of the applications. A scheduling scenario is proposed in Section 8.2. In Section 8.3, the Cost Function Model (CFM) is described that minimizes an objective function based on one single metric, the financial cost. Then, the ïanos implementation is presented, and validated with a Grid of HPC clusters.

“We choose to go to the moon. We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win, and the others, too.”

John F. Kennedy, 35^th US President