A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems

• Definition of a novel multi-objective problem for energy-efficient scheduling in distributed data-centers.
• Design of a hierarchical two-level scheduler that allows dividing the problem into simpler and smaller sub-problems.
• Evaluation and comparison of 16 different variants of the scheduler on large sets of workflows.
• Accurate solutions found by the best performing schedulers, achieving important improvements over classical strategies.

This article presents a two-level strategy for scheduling large workloads of parallel applications in multicore distributed systems, taking into account the minimization of both the total computation time and the energy consumption of solutions. Nowadays, energy efficiency is of major concern when using large computing systems such as cluster, grid, and cloud computing facilities. In the approach proposed in this article, a combination of higher-level (i.e., between distributed systems) and lower-level (i.e., within each data-center) schedulers are studied for finding efficient mappings of workflows into the resources in order to maximize the quality of service, while reducing the energy required to compute them. The experimental evaluation demonstrates that accurate schedules are computed by using combined list scheduling heuristics (accounting for both problem objectives) in the higher level, and ad-hoc scheduling techniques to take advantage of multicore infrastructures in the lower level. Solutions are also evaluated with two user- and administrator-oriented metrics. Significant improvements are reported on the two problem objectives when compared with traditional load-balancing and round-robin techniques.