Energy Efficient Scheduling Based on Marginal Cost and Task Grouping in Data Centers

High energy consumption of data centers has been a widely concerned issue and has become a research hotspot. Recent studies show that both servers and cooling systems are main energy consuming factors of data centers. However, most of these studies only consider energy optimization either in servers or the cooling system, separately. Thus, it is essential to develop a scheme to jointly optimize the energy consumption of servers and cooling systems in the data center. In this paper, a joint energy optimization scheme is proposed to coordinately optimize the energy consumption of servers and the cooling system. Firstly, in the cooling system, different cooling modes, including the outside air cooling and liquid cooling are jointly considered. An energy consumption model for the cooling system is designed, where a segmented function is used to describe different energy consumption rate at different stages of the cooling system. A strategy is designed to use different proportion of the outside air cooling and liquid cooling according to real-time workload characteristics. Then, combining the energy consumption of the cooling system and servers, a joint energy optimization problem for data centers is formulated. Meanwhile, an energy-efficient task scheduling strategy based on marginal cost and task grouping is developed for solving the problem. Simulations have been conducted based on real-world workload traces, and simulation results demonstrate that the proposed approach outperforms previous techniques in energy savings.

[1]  Ariel Oleksiak,et al.  Energy and thermal models for simulation of workload and resource management in computing systems , 2015, Simul. Model. Pract. Theory.

[2]  T. N. Vijaykumar,et al.  Joint optimization of idle and cooling power in data centers while maintaining response time , 2010, ASPLOS XV.

[3]  Zhiling Lan,et al.  Job scheduling with adjusted runtime estimates on production supercomputers , 2013, J. Parallel Distributed Comput..

[4]  Yonggang Wen,et al.  Data Center Energy Consumption Modeling: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[5]  Baochun Li,et al.  Temperature Aware Workload Managementin Geo-Distributed Data Centers , 2013, IEEE Transactions on Parallel and Distributed Systems.

[6]  Xuyun Zhang,et al.  A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems , 2017, Future Gener. Comput. Syst..

[7]  Lemin Li,et al.  Efficient Load Balancing for the VNF Deployment with Placement Constraints , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[8]  Rongliang Zhou,et al.  Optimization and control of cooling microgrids for data centers , 2012, 13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[9]  Baochun Li,et al.  Temperature Aware Workload Managementin Geo-Distributed Data Centers , 2013, IEEE Trans. Parallel Distributed Syst..

[10]  Jun Wang,et al.  Optimization based resource and cooling management for a high performance computing data center. , 2019, ISA transactions.

[11]  Hoang Duong Tuan,et al.  Joint network embedding and server consolidation for energy-efficient dynamic data center virtualization , 2017, Comput. Networks.

[12]  Deshi Ye,et al.  A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers , 2015, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[13]  G. R. Gangadharan,et al.  Energy-aware virtual machine allocation and selection in cloud data centers , 2019, Soft Comput..

[14]  Rusli Abdullah,et al.  Energy-saving framework for data center from reduce, reuse and recycle perspectives , 2019 .

[15]  Massoud Pedram,et al.  Minimizing data center cooling and server power costs , 2009, ISLPED.

[16]  Ratnesh K. Sharma,et al.  A holistic and optimal approach for data center cooling management , 2011, Proceedings of the 2011 American Control Conference.

[17]  Yihai Zhu,et al.  Tensor-Based Optimal Temperature Control of CRACs in Multi-Datacenters , 2019, IEEE Access.

[18]  Minami Yoda,et al.  Improving Energy Efficiency in Data Centers by Controlling Task Distribution and Cooling , 2018 .

[19]  Shaolei Ren,et al.  TECH: A Thermal-Aware and Cost Efficient Mechanism for Colocation Demand Response , 2016, 2016 45th International Conference on Parallel Processing (ICPP).

[20]  Rui Wang,et al.  Real-time Task Scheduling for joint energy efficiency optimization in data centers , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[21]  Wei Lu,et al.  Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks , 2016, IEEE Communications Letters.

[22]  Yumei Wang,et al.  Energy Aware Virtual Machine Scheduling in Data Centers , 2019, Energies.

[23]  Liping Zhang,et al.  A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid , 2010, Cluster Computing.

[24]  Ran Zhang,et al.  Joint Cooling and Server Control in Data Centers: A Cross-Layer Framework for Holistic Energy Minimization , 2018, IEEE Systems Journal.

[25]  Mateusz Jarus,et al.  Performance bounded energy efficient virtual machine allocation in the global cloud , 2014, Sustain. Comput. Informatics Syst..

[26]  Xin Wang,et al.  Cooling-Aware Energy and Workload Management in Data Centers via Stochastic Optimization , 2016, IEEE Journal of Selected Topics in Signal Processing.

[27]  Longchuan Yan,et al.  Temperature and Power Aware Server Placement Optimization for Enterprise Data Center , 2018, 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS).

[28]  Adam Wierman,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012, SIGMETRICS '12.

[29]  Xin Zhou,et al.  DeepEE: Joint Optimization of Job Scheduling and Cooling Control for Data Center Energy Efficiency Using Deep Reinforcement Learning , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[30]  Rajkumar Buyya,et al.  Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach , 2018, 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC).

[31]  Suresh Singh,et al.  Minimizing Energy Consumption of FatTree Data Center Networks , 2014, PERV.