Exploiting heterogeneity in grid computing for energy-efficient resource allocation

The growing computing demand from industry and academia has lead to excessive power consumption which not only impacting long term sustainability of Grids like infrastructure in terms of energy cost but also from environmental perspective. The problem can be addressed by replacing with more energy efficient infrastructures, but the process of switching to new infrastructure is not only costly but also time consuming. Grid being consist of several HPC centers under different administrative domain, make problem more difficult. Thus, for reduction in energy consumption, we address the challenge by effectively distributing compute-intensive parallel applications on grid. We presented a metascheduling algorithm which exploits the heterogeneous nature of Grid to achieve reduction in energy consumption. Simulation results show that out algorithm HAMA can significantly improve the energy efficiency of global grids by a factor of typically 23% and as much as a factor of 50% in some cases while meeting user's QoS requirements.