Energy Efficiency for Autonomic Scalable Systems: Research Objectives and Preliminary Results

Power awareness and power management techniques emerged as an interesting topic in the last decade in both cloud computing and fog computing scenarios. In this context, several emerging technologies are opening novel research challenges that should put together both power management and performance of the running workloads. In this context, energy proportionality can help those systems to match power and performance needs. In this paper we introduce E2 ASY, which aims at building an energy proportionality toolbox for cloud and embedded fog systems that provides solutions able to observe performance and power and managing them towards the desired goals. Preliminary results obtained on cloud systems shows negligible overhead on the monitored system and good results on power consumption reduction.

[1]  John Kubiatowicz,et al.  Power Consumption Models for Multi-Tenant Server Infrastructures , 2017, ACM Trans. Archit. Code Optim..

[2]  Steven McCanne,et al.  The BSD Packet Filter: A New Architecture for User-level Packet Capture , 1993, USENIX Winter.

[3]  Steven McCanne,et al.  BPF+: exploiting global data-flow optimization in a generalized packet filter architecture , 1999, SIGCOMM '99.

[4]  Marco D. Santambrogio,et al.  DockerCap: A Software-Level Power Capping Orchestrator for Docker Containers , 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).

[5]  Marco D. Santambrogio,et al.  Towards a performance-aware power capping orchestrator for the Xen hypervisor , 2018, SIGBED.

[6]  Efraim Rotem,et al.  Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge , 2012, IEEE Micro.

[7]  John Kubiatowicz,et al.  Enabling power-awareness for the Xen hypervisor , 2018, SIGBED.

[8]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[9]  No License,et al.  Intel ® 64 and IA-32 Architectures Software Developer ’ s Manual Volume 3 A : System Programming Guide , Part 1 , 2006 .

[10]  Eric A. Brewer,et al.  Borg, Omega, and Kubernetes , 2016, ACM Queue.

[11]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[12]  Rajkumar Buyya,et al.  A Framework and Algorithm for Energy Efficient Container Consolidation in Cloud Data Centers , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[13]  Marco D. Santambrogio,et al.  A fog-computing architecture for preventive healthcare and assisted living in smart ambients , 2017, 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI).

[14]  Marco D. Santambrogio,et al.  DEEP-Mon: Dynamic and Energy Efficient Power Monitoring for Container-Based Infrastructures , 2018, 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[15]  Sarma B. K. Vrudhula,et al.  Energy management for battery-powered embedded systems , 2003, TECS.

[16]  Christoforos E. Kozyrakis,et al.  Towards energy proportionality for large-scale latency-critical workloads , 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).