Multiobjective Energy-Aware Workflow Scheduling in Distributed Datacenters

  • Sergio Nesmachnow
  • Santiago Iturriaga
  • Bernabé Dorronsoro
  • Andrei Tchernykh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 595)


This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The studied schedulers follow a two-level schema: in the higher-level, we apply a multiobjective heuristic and a multiobjective metaheuristic, to distribute jobs between clusters; in the lower-level, specific backfilling-oriented scheduling methods are used for task scheduling locally within each cluster, considering precedence constraints. A new model for energy consumption in multi-core computers is applied. The experimental evaluation performed on a benchmark set of large workloads that model different realistic high performance computing applications demonstrates that the proposed multiobjective schedulers are able to improve both the makespan and energy consumption of the schedules when compared with a standard Optimistic Load Balancing Round Robin approach.


Pareto Front Greedy Randomize Adaptive Search Procedure Cluster Node Dynamic Voltage Scaling Heterogeneous Early Finish Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sergio Nesmachnow
    • 1
  • Santiago Iturriaga
    • 1
  • Bernabé Dorronsoro
    • 2
  • Andrei Tchernykh
    • 3
  1. 1.Universidad de la RepúblicaMontevideoUruguay
  2. 2.Universidad de CádizCádizSpain
  3. 3.CICESE Research CenterEnsenadaMexico

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