A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints

As the scale increases and performance improves, the energy consumption of high-performance computer systems is rapidly increasing. The energy-aware task scheduling for high-performance computer systems has become a hot spot for major supercomputing centers and data centers. In this paper, we study the task scheduling problem to minimize the schedule length of parallel applications while satisfying the energy constraints in heterogeneous distributed systems. The existing approaches mainly allocate unassigned tasks with minimal energy consumption which cannot achieve optimistic scheduling length in most cases. Based on this situation, we propose a reformed scheduling method with energy consumption constraint algorithm, which is based on an energy consumption level to pre-allocate energy consumption for unassigned tasks. The experimental results show that compared with the existing algorithms, our new algorithm can achieve better scheduling length under the energy consumption constraints.

[1]  Amin Vahdat,et al.  Managing Energy and Server Resources for a Hosting Center , 2001, SOSP 2001.

[2]  Guillaume Aupy,et al.  Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling , 2018, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[3]  Chi-Yeh Chen An Improved Approximation for Scheduling Malleable Tasks with Precedence Constraints via Iterative Method , 2018, IEEE Transactions on Parallel and Distributed Systems.

[4]  Dakai Zhu,et al.  Shared recovery for energy efficiency and reliability enhancements in real-time applications with precedence constraints , 2013, TODE.

[5]  Hiroaki Takada,et al.  Energy-aware task migration for multiprocessor real-time systems , 2016, Future Gener. Comput. Syst..

[6]  Henry Hoffmann,et al.  A Divide and Conquer Algorithm for DAG Scheduling under Power Constraints , 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.

[7]  Kenli Li,et al.  Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems , 2017, Inf. Sci..

[8]  Samee Ullah Khan,et al.  An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment , 2015, Journal of Grid Computing.

[9]  Ling Qiu,et al.  Energy-efficient scheduling with individual packet delay constraints and non-ideal circuit power , 2014, Journal of Communications and Networks.

[10]  Kenli Li,et al.  Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries , 2017, Inf. Sci..

[11]  Kenli Li,et al.  A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues , 2014, Inf. Sci..

[12]  Philip S. Yu,et al.  A Bi-layered Parallel Training Architecture for Large-Scale Convolutional Neural Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[13]  Yang Yang,et al.  MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks , 2018, IEEE Internet of Things Journal.

[14]  Tongquan Wei,et al.  Sustainability-Oriented Evaluation and Optimization for MPSoC Task Allocation and Scheduling under Thermal and Energy Variations , 2018, IEEE Transactions on Sustainable Computing.

[15]  Pengcheng Zhu,et al.  DOTS: Delay-Optimal Task Scheduling Among Voluntary Nodes in Fog Networks , 2019, IEEE Internet of Things Journal.

[16]  Siamak Mohammadi,et al.  CMV: Clustered Majority Voting Reliability-Aware Task Scheduling for Multicore Real-Time Systems , 2019, IEEE Transactions on Reliability.

[17]  Amlan Chakrabarti,et al.  Dynamic Scheduling of Real-Time Tasks in Heterogeneous Multicore Systems , 2019, IEEE Embedded Systems Letters.

[18]  Albert Y. Zomaya,et al.  Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions , 2011, IEEE Transactions on Parallel and Distributed Systems.

[19]  Rong Ge,et al.  Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[20]  Kenli Li,et al.  A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment , 2017, IEEE Transactions on Parallel and Distributed Systems.

[21]  Keqin Li,et al.  Minimizing Schedule Length of Energy Consumption Constrained Parallel Applications on Heterogeneous Distributed Systems , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[22]  Omprakash Kaiwartya,et al.  Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing , 2018, IEEE Access.

[23]  Kenli Li,et al.  Energy-Efficient Stochastic Task Scheduling on Heterogeneous Computing Systems , 2014, IEEE Transactions on Parallel and Distributed Systems.

[24]  Kenli Li,et al.  Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems , 2015, IEEE Transactions on Computers.

[25]  Kenli Li,et al.  Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer , 2019, IEEE Transactions on Parallel and Distributed Systems.

[26]  Kenli Li,et al.  Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster , 2015, Inf. Sci..

[27]  Luca Zaccarian,et al.  A Hybrid Control Law for Energy-Oriented Tasks Scheduling in Wireless Sensor Networks , 2018, IEEE Transactions on Control Systems Technology.

[28]  Kenli Li,et al.  Energy-Aware Data Allocation and Task Scheduling on Heterogeneous Multiprocessor Systems With Time Constraints , 2014, IEEE Transactions on Emerging Topics in Computing.

[29]  Nan Wang,et al.  Security-Aware Task Scheduling Using Untrusted Components in High-Level Synthesis , 2018, IEEE Access.

[30]  Bronis R. de Supinski,et al.  Adagio: making DVS practical for complex HPC applications , 2009, ICS.

[31]  Nan Yang,et al.  A disease diagnosis and treatment recommendation system based on big data mining and cloud computing , 2018, Inf. Sci..

[32]  Laurence T. Yang,et al.  Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model , 2019, IEEE Transactions on Sustainable Computing.

[33]  Philip S. Yu,et al.  Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing , 2019, IEEE Transactions on Industrial Informatics.

[34]  Min Chen,et al.  Opportunistic Task Scheduling over Co-Located Clouds in Mobile Environment , 2018, IEEE Transactions on Services Computing.

[35]  Huazhong Yang,et al.  PATH: Performance-Aware Task Scheduling for Energy-Harvesting Nonvolatile Processors , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[36]  Tao Li,et al.  CASpMV: A Customized and Accelerative SpMV Framework for the Sunway TaihuLight , 2021, IEEE Transactions on Parallel and Distributed Systems.