Energy saving in multiprocessor system is emerging as a primary issue in recent years. Mostly the work available has stressed on reducing makespan of application and little attention has been paid to energy management. In this paper, a novel dynamic power management based clustering task scheduling algorithm is proposed for heterogeneous computing platforms. The prime objective of this paper is to reduce the dynamic and static power consumption of the processors. This paper couples clustering scheduling heuristics with dynamic power management technique to reduce energy consumption. The simulation based results of the proposed energy-aware clustering task scheduling algorithm with dynamic power management are analysed and compare with well-known list based heterogeneous earliest finish time (HEFT) and list based energy-aware precedence-constrained tasks algorithm (PASTA) algorithm. The result carried out for an extensive set of random and real world graphs demonstrate the potency of the proposed algorithm.
TOP 500 supercomputers sites, TOP500 list generated for the year 2018, https://www.top500.org/lists/(2018)/11/.
2.
OrgerieA.C.AssuncaoM.D.D. and LefevreL., A survey on techniques for improving the energy efficiency of large-scale distributed systems, ACM Computing Surveys (CSUR)46(4) (2014), 47.
3.
VenkatachalamV. and FranzM., Power reduction techniques for microprocessor systems, ACM Computing Surveys (CSUR)37(3) (2005), 195–237.
4.
LegoutV.JanM., and PautetL., Scheduling algorithms to reduce the static energy consumption of real-time systems, Real-Time Syst51(2) (2015), 153–191.
5.
SueurE. and HeiserG., Dynamic voltage and frequency scaling: The laws of diminishing returns, in: Proceedings of the International Conference On Power Aware Computing And Systems, USENIX Association (Oct 3 2010), 1–8.
6.
BaskiyarS. and PalliK.K., Low power scheduling of dags to minimize finish times, in: High Performance Computing-HiPC, Springer Berlin Heidelberg (2006), 353–362.
7.
BaskiyarS. and Abdel-KaderR., Energy aware DAG scheduling on heterogeneous systems, Cluster Computing13(4) (2010), 373–383.
8.
ShekarV. and IzadiB., Energy aware scheduling for DAG structured applications on heterogeneous and DVS enabled processors, In Green Computing Conference IEEE (2010), 495–502.
9.
NirmalK.BansalS. and BansalR.K., Energy efficient duplication-based scheduling for precedence constrained tasks on heterogeneous computing cluster, Multiagent and Grid Systems12(3) (2016), 239–252.
10.
NirmalK.BansalS. and BansalR.K., Energy conscious scheduling with controlled threshold for precedence-constrained tasks on heterogeneous clusters, Concurrent Engineering25(3) (2017), 276–286.
11.
WangL.KhanS.U.ChenD.KołodziejJ.RanjanR.XuC.Z. and ZomayaA., Energy-aware parallel task scheduling in a cluster, Future Generation Computer Systems29(7) (2013), 1661–1670.
12.
LiuW.DuanY. and DuW., An energy efficient clustering-based scheduling algorithm for parallel tasks on homogeneous DVS-enabled clusters, In Computer Supported Cooperative Work in Design (CSCWD), IEEE 16th International Conference on IEEE (2012), 575–582.
13.
TangX.Y. and TanW.Z., Energy-efficient reliability-aware scheduling algorithm on heterogeneous systems, Scientific Programming (March 2016), Article ID 9823213, 14.
14.
MaY.GongB. and ZouL., Energy-efficient scheduling algorithm of task dependent graph on DVS-Unable cluster system, in: Grid Computing, 10th IEEE/ACM International Conference on IEEE (2009), 217–224.
15.
MeiJ.LiK. and LiK., Energy-aware task scheduling in heterogeneous computing environments, Cluster Computing17(2) (2014), 537–550.
16.
NirmalK.BansalS. and BansalR.K., Duplication-controlled static energy-efficient scheduling on multiprocessor computing system, Concurrency and Computation: Practice and Experience29(12) (2017), e4124.
17.
SharifiM.ShahrivariS. and SalimiH., PASTA: A power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources, Computing95(1) (2013), 67–88.
18.
NirmalK., Energy efficient scheduling techniques for distributed computing systems, Ph. D thesis IKG-Panjab Technical University, Kapurthala (2017).
19.
De LangenP. and JuurlinkB., Leakage-aware multiprocessor scheduling for low power, In Parallel and Distributed Processing Symposium, IPDPS 20th International IEEE 20068-pp.
20.
NesmachnowS. et al, Energy-aware scheduling on multicore heterogeneous grid computing systems, J Grid Computing (11) (2013), 653–680.
21.
Kyle TarpleeM. et al., Energy-aware profit maximizing scheduling algorithm for heterogeneous computing systems2014, 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 595–603.
22.
KaurH. and ChhabraA., Fault-aware advance reservation scheduling in heterogeneous computing systems, International Journal of Applied Engineering Research13(11) (2018), 9636–9645.
23.
TopcuogluH.HaririS. and WuM.Y., Performance-effective and low-complexity task scheduling for heterogeneous computing, Parallel and Distributed Systems, IEEE Transactions on13(3) (2002), 260–274.
24.
Measuring Processor Power TDP vs. ACP https://www.intel.com/content/dam/doc/white-paper/resources-xeon- measuring-processor-power-paper.pdf, 2011.
