Invited Paper: Improving Data Center Efficiency Through Holistic Scheduling In Kubernetes

Data centers are the infrastructure that underpins modern distributed service-oriented systems. They are complex systems-of-systems, with many interacting elements, that consume vast amounts of power. Demand for such facilities is growing rapidly, leading to significant global environmental impact. The data center industry has conducted much research into efficiency improvements, but this has mostly been at the physical infrastructure level. Research into software-based solutions for improving efficiency is greatly needed. However, most current research does not take a holistic view of the data center that considers virtual and physical infrastructures as well as business process. This is crucial if a solution is to be applied in a realistic setting. This paper describes the complex, system-of-systems nature of data centers, and discusses the service models used in the industry. We describe a holistic scheduling system that replaces the default scheduler in the Kubernetes container system, taking into account both software and hardware models. We discuss the initial results of deploying this scheme in a real data center, where power consumption reductions of 10-20% were observed. We show that by introducing hardware modelling into a software-based solution, an intelligent scheduler can make significant improvements in data center efficiency. We conclude by looking at some of the future work that needs to be performed in this area.

[1]  K. Kockelman,et al.  Forecasting Americans' Long-Term Adoption of Connected and Autonomous Vehicle Technologies , 2016 .

[2]  Toni Mastelic,et al.  Recent Trends in Energy-Efficient Cloud Computing , 2015, IEEE Cloud Computing.

[3]  Hong Zhu,et al.  If Docker is the Answer, What is the Question? , 2018, 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE).

[4]  Mohsen Guizani,et al.  Towards Energy Saving in Computational Clouds: Taxonomy, Review, and Open Challenges , 2018, IEEE Access.

[5]  Anders S. G. Andrae,et al.  On Global Electricity Usage of Communication Technology: Trends to 2030 , 2015 .

[6]  Osama A. Mohammed,et al.  A Survey on Smart Grid Cyber-Physical System Testbeds , 2017, IEEE Communications Surveys & Tutorials.

[7]  Jon Summers,et al.  Validated Thermal Air Management Simulations of Data Centers Using Remote Graphics Processing Units , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[8]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[9]  Yogendra Joshi,et al.  Energy Efficient Thermal Management of Data Centers , 2012 .

[10]  Xue Liu,et al.  GreenPlanning: optimal energy source selection and capacity planning for green datacenters , 2016, ICCPS 2016.

[11]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[12]  Jie Xu,et al.  Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement , 2013, 2013 IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS).

[13]  Luca Castellazzi,et al.  Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency , 2017 .

[14]  Jeffrey Sarkinen,et al.  A demonstration of monitoring and measuring data centers for energy efficiency using opensource tools , 2018, e-Energy.

[15]  Bhagya Nathali Silva,et al.  Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities , 2018 .