Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

Xiaolong Xu*, Lingling Cao, Xinheng Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the "shut down the redundant, turn on the demanded" strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted: a cubic exponential smoothing algorithm based on the conservative control (CESCC) strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching (RA-PM) are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing (RA-ISA) is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Original languageEnglish
Article number7514434
Pages (from-to)457-469
Number of pages13
JournalJournal of Systems Engineering and Electronics
Issue number2
Publication statusPublished - 20 Apr 2016
Externally publishedYes


  • green cloud computing
  • power consumption
  • prediction
  • probabilistic matching
  • resource allocation
  • simulated annealing

Cite this