Reducing carbon emissions of distributed systems: a multi-objective approach
Resumo
Context The growing usage of platforms for distributed computing and their workloads are requiring more energy to power data centers, and the current consumption is already high. The increasing availability of green energy sources brings opportunities to reduce carbon emissions. Problem It is hard to create software aiming for both performance (makespan) and low brown energy usage. To reduce carbon emissions on distributed platforms, developers need an easy way to program efficiently applications and achieve these objectives. Solution Using OpenMP is an easy way to create and run distributed applications. This paper proposes the use of OpenMP with a new energy-aware scheduling algorithm that aims to minimize brown energy consumption and makespan. IS theory Our multiobjective algorithm (G-MOHEFT) deals with Complexity theory to leverage the Dynamic capabilities of modern distributed platforms. The algorithm implements a heuristic for adapting and redistributing workloads accordingly to different scenarios. Method Containers were used to simulate a distributed OpenMP Cluster (OMPC) platform. Different simulations using previously measured data were used to distribute workloads in different combinations of green energy availability. Summary of Results We study the solution tradeoffs obtained using G-MOHEFT, which varies from saving none to some brown energy consumption by keeping the same or increasing the makespan in exchange. Depending on the scenario, it could even reduce the brown energy consumption to zero. Contributions and impacts to IS Developers can use our algorithm to easily develop distributed software with a reduced carbon footprint using OpenMP in OMPC.
