Cluster Computing

, Volume 16, Issue 3, pp 591-609

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Hierarchical genetic-based grid scheduling with energy optimization

  • Joanna KołodziejAffiliated withInstitute of Computer Science, Cracow University of Technology Email author 
  • , Samee Ullah KhanAffiliated withNDSU-CIIT Green Computing and Communications Laboratory, North Dakota State University
  • , Lizhe WangAffiliated withCenter for Earth Observation, Chinese Academy of Sciences
  • , Aleksander ByrskiAffiliated withAGH University of Science and Technology
  • , Nasro Min-AllahAffiliated withDepartment of Computer Science, COMSATS Institute of Information Technology
  • , Sajjad Ahmad MadaniAffiliated withDepartment of Computer Science, COMSATS Institute of Information Technology


An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds.

In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.


Genetic algorithm Hierarchical genetic strategy Computational grid Scheduling Dynamic voltage Frequency scaling