Energy efficient scheduling strategies in Federated Grids

• Performance based scheduling and self-adjusting resource sharing strategies reduce the total amount of computation power required by themselves.
• Because everything generates a carbon footprint, our strategies have been thought to minimize the number of messages required per job scheduled.
• Our algorithms do not flood the network with negotiation messages.
• None of our policies strongly depend on resource discovery services which reduces computing power and network communication cost.

The impact of energy consumption and CO2 emissions in high-performance grid computing is no longer ignored. So far, most efforts have focused on energy efficiency measures of grid infrastructures's underlying hardware and on direct reduction of CO2 emissions. Thus, in the field of scheduling and resource allocation in computational grids some algorithms have been proposed for selecting grid infrastructures in energy- and CO2-aware manner or based on hardware power consumption. However, these hardware energy efficiency measures are not suitable at meta-scheduler level. Instead, this paper presents software energy efficiency measures that reduce computing power and communication bandwidth, and hence reduce algorithm's total communication cost and power consumption. The main idea is to restrict to the maximum the number of needed messages per job scheduled. Thus, we proposed performance based scheduling policies and self-adjusting resource sharing strategies that accomplish with applications makespan and resources performance while reducing the total amount of computation power and communication bandwidth required by the algorithm itself.