Energy-Aware Algorithms for Task Graph Scheduling, Replica Placement and Checkpoint Strategies

The energy consumption of computational platforms has recently become a critical problem, both for economic and environmental reasons [35]. To reduce energy consumption, processors can run at different speeds. Faster speeds allow for a faster execution, but they also lead to a much higher (superlinear) power consumption. Energy-aware scheduling aims at minimizing the energy consumed during the execution of the target application, both for computations and for communications. The price to pay for a lower energy consumption usually is a much larger execution time, so the energy-aware approach makes better sense when coupled with some prescribed performance bound. In other words, we have a bi-criteria optimization problem, with one objective being energy minimization, and the other being performance-related. In this chapter, we discuss several problems related to data centers, for which energy consumption is a crucial matter. Indeed, statistics showed that in 2012, some data centers consume more electricity than 250,000 european houses. If the cloud was a country, it would be ranked as the fifth world-wide rank in terms of demands in electricity, and the need is expected to be multiplied by three before 2020. We