A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Sustainable Computing : Informatics and Systems Year : 2014

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

Abstract

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.
No file

Dates and versions

hal-01249475 , version 1 (01-01-2016)

Identifiers

Cite

Bernabé Dorronsoro, Sergio Nesmachnow, Albert Zomaya, El-Ghazali Talbi, Pascal Bouvry. A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustainable Computing : Informatics and Systems, 2014, 4 (4), pp.252-261. ⟨10.1016/j.suscom.2014.08.003⟩. ⟨hal-01249475⟩
182 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More