Task scheduling optimization based on heuristic algorithm for heterogeneous cloud computing platforms

Document Type : Research Paper


Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran


In recent years, the issue of power consumption in parallel and distributed systems has attracted a great deal of attention. Regarding the ever-increasing  development data and computing centers due to the contribution of cloud computing systems in such sectors, power consumption has always been of the  concerns due to Carbon dioxide emissions and consequently the Negative impact on the environment. In recent years, the notion of power and also "Green  Computing" has found a crucial spot in the tasks scheduling in cloud data centers. The clustering technique, as well as Dynamic Voltage and Frequency  Scaling (DVFS) techniques, have focused on the reduction of the consumption of power particularly, and the optimization of the performance parameters.  Concerning scheduling Directed Acyclic Graph (DAG) of a data center processors equipped with the technique of DVFS, this paper proposes a power and time  aware algorithm called PATCDD, to apply the combination of the strategies for clustering along with the distribution of slack-time among the tasks of a  cluster. The first phase studies the slack time for non-critical tasks of DAG, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. The main idea of the proposed algorithm involves the achievement of a maximum reduction in power  consumption in the second phase. To this end, the slack time is distributed among non-critical dependent tasks. Eventually, a set of data established for  conducting the examinations and also different parameters of the constructed random DAG were assessed to identify the efficiency of our proposed  algorithm.    


[1] B. Barzegar, S. Habibian and M. Fazlollah Nejad, Heuristic algorithms for task scheduling in cloud computing
using combined particle warm optimization and bat algorithms, J. Adv. Comput. Res. 10 (2019), no. 3, 83–95.
[2] B. Barzegar, H. Motameni and A. Movaghar, EATSDCD: A green energy-aware scheduling algorithm for parallel
task-based application using clustering, duplication and DVFS technique in cloud datacenters, J. Intell. Fuzzy
Syst. 36 (2019), no. 6, 5135–5152.
[3] B. Barzegar and H. Shirgahi, Advanced reservation and scheduling in grid computing systems by gravitational
emulation local search algorithm, Amer. J. Sci. Res. 18 (2011), 62–70.
[4] A. Beloglazov, J. Abawajy and R. Buyya, Energy-aware resource allocation heuristics for efficient management
of data centers for cloud computing, Future Gen. Comput. Syst. 28 (2012), no. 5, 755–768.
[5] R. Buyya, R. Ranjan and R.N. Calheiros, Modeling and simulation of scalable Cloud computing environments
and the CloudSim toolkit: Challenges and opportunities, Int. Conf. High Perform. Comput. Simul. IEEE, 2009.6] Y. Ding, X. Qin, L. Liu and T. Wang, Energy efficient scheduling of virtual machines in cloud with deadline
constraint, Future Gen. Comput. Syst. 50 (2015), 62–74.
[7] S. Fatehi, H. Motameni, B. Barzegar and M. Golsorkhtabaramiri, Energy aware multi objective algorithm for task
scheduling on DVFS-enabled cloud datacenters using fuzzy NSGA-II, Int. J. Nonlinear Anal. Appl. 12 (2021), no.
2, 2303–2331.
[8] F. Juarez, J. Ejarque and R.M. Badia, Dynamic energy-aware scheduling for parallel task-based application in
cloud computing, Future Gen. Comput. Syst. 78 (2018), 257–271.
[9] E. Kabir, J. Hu, H. Wang and G. Zhuo, A novel statistical technique for intrusion detection systems, Future Gen.
Comput. Syst. 79 (2018), 303–318.
[10] H. Kasahara, Standard task graph set, 2004.” URL http://www. kasahara. elec. waseda. ac. jp/schedule/index.
[11] J. Masoudi, B. Barzegar and H. Motameni, Energy-aware virtual machine allocation in DVFS-enabled cloud data
centers, IEEE Access 10 (2021), 3617–3630.
[12] Z. Peng, B. Barzegar, M. Yarahmadi, H. Motameni and P. Pirouzmand, Energy-aware scheduling of workflow
using a heuristic method on green cloud, Sci. Prog. 2020 (2020).
[13] X. Tang, K. Li, R. Li and B. Veeravalli, Reliability-aware scheduling strategy for heterogeneous distributed computing systems, J. Parall. Distrib. Comput. 70 (2010), no. 9, 941–952.
[14] H. Topcuoglu, S. Hariri and M.Y. Wu, Performance-effective and low-complexity task scheduling for heterogeneous
computing, IEEE Trans. Parall. Distrib. Syst. 13 (2002), no. 3, 260–274.
[15] J.D. Ullman, NP-complete scheduling problems, J. Comput. Syst. Sci. 10 (1975), no. 3, 384–393.
[16] V. Venkatachalam and M. Franz, Power reduction techniques for microprocessor systems, ACM Comput. Surveys
(CSUR) 37 (2005), no. 3, 195–237.
[17] L. Wang, S.U. Khan, D. Chen, J. Ko lodziej, R. Ranjan, C.Z. Xu and A. Zomaya, Energy-aware parallel task
scheduling in a cluster, Future Gen. Comput. Syst. 29 (2013), no. 7, 1661–1670.
[18] C.M. Wu, R.S. Chang and H.Y. Chan, A green energy-efficient scheduling algorithm using the DVFS technique
for cloud datacenters, Future Gen. Comput. Syst. 37 (2014), 141–147.
[19] D. Zhu, R. Melhem and B.R. Childers, Scheduling with dynamic voltage/speed adjustment using slack reclamation
in multiprocessor real-time systems, IEEE Trans. Parall. Distrib. Syst. 14 (2003, no. 7, 686–700.
[20] Z. Zong, A. Manzanares, B. Stinar and X. Qin, Energy-aware duplication strategies for scheduling precedenceconstrained parallel tasks on clusters, IEEE Int. Conf. Cluster Comput. IEEE, 2006.
Volume 13, Issue 2
July 2022
Pages 2743-2750
  • Receive Date: 02 September 2021
  • Revise Date: 16 March 2022
  • Accept Date: 20 April 2022