DEEP-mon: Dynamic and Energy Efficient Power monitoring for container-based infrastructures
Abstract: In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership (TCO). Power consumption can be observed at different layers of the data-center, from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers both in the cloud computing and High Performance Computing (HPC) scenarios, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. What we propose is DEEP-mon, a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system. Moreover, we show how the proposed approach has a negligible impact on the monitored system and on the running workloads, overcoming the limitations of the previous works in the field.
![Data Center - Power Consumption
Environment
20%
Of Costs
wasted on
power
consumption[1]
Power
Cap
Costs
Y. Cui, C. Ingalz, T. Gao, and A. Heydari, “Total cost of ownership model for data center technology
evaluation,” in Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2017 16th
IEEE Intersociety Conference on. IEEE, 2017, pp. 936–942.
2](https://image.slidesharecdn.com/presentationhppac-180521074011/85/DEEP-mon-Dynamic-and-Energy-Efficient-Power-monitoring-for-container-based-infrastructures-2-320.jpg)
DEEP-mon: Dynamic and Energy Efficient Power monitoring for container-based infrastructures
- 1. DEEP-mon HPPAC - Vancouver 21/05/2018 Dynamic and Energy Efcient Power monitoring for container-based infrastructures Tommaso Sardelli, Rolando Brondolin, Marco D. Santambrogio
- 2. Data Center - Power Consumption Environment 20% Of Costs wasted on power consumption[1] Power Cap Costs Y. Cui, C. Ingalz, T. Gao, and A. Heydari, “Total cost of ownership model for data center technology evaluation,” in Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2017 16th IEEE Intersociety Conference on. IEEE, 2017, pp. 936–942. 2
- 3. Need For Power Awareness Power Aware balancing of IT workloadsPower Efciency Monitoring Data Driven 3
- 6. Application Level Power Attribution 6 CPU: 78 W DRAM: 41W CPU: 121 W DRAM: 34W Server Level power attribution
- 7. Application Level Power Attribution Server Level power attribution Application Level power attribution CPU: 78 W DRAM: 41W CPU: 121 W DRAM: 34W CPU: 21 W DRAM: 5W CPU: 16 W DRAM: 4W CPU: 114 W DRAM: 29 W 7
- 8. DEEP-mon 8 Runtime Monitoring Container Power Consumption Low Overhead CPU: 21 W DRAM: 5W CPU: 16 W DRAM: 4W
- 10. Kernel - Runtime Monitoring 10
- 11. Kernel - Runtime Monitoring 11
- 12. Kernel - Runtime Monitoring 12
- 15. Solution – In Kernel Aggregation Use eBPF to aggregate data in kernel space Use a dynamic window to take aggregated data at intervals Deal with one event instead 200k 15 Container C Container B Container A
- 16. User Space Power Attribution 16 Container C Container B Container A PMC PMC PMC
- 17. User Space – Power Attribution 17 BPMC APMC CPMCBPMC
- 18. User Space – Power Attribution 18 BPMC APMC CPMCBPMC
- 19. User Space – Power Attribution 19 BPMC APMC CPMCBPMC
- 20. Hyper Thread – Overlapped Cycles 20 Multiply by a factor of 1.1 Thread1 Thread2 Time Cycleoverlap
- 21. DEEP-mon CLI Interface 21 Container ID Cycles Exec. Time (s) Power (mW) 4fd630d438 1,756,600 0.0008 1.334 0eb34499da 47,514,323 0.0319 243.746
- 22. Kubernetes – Data Enrichment 22 CPU: 21 W DRAM: 5W CPU: 16 W DRAM: 4W Application Name Hostname IP Address Namespace
- 24. Benchmarks - Setup 24 Objective : Measure performance and power consumption overhead introduced by the agent 2x Intel Xeon E5-2680 40 threads - 380GB RAM Ubuntu 16.04 Linux 4.13 with eBPF support HPC Benchmark Cloud Benchmark Phoronix Test Suite NAS Parallel Benchmark
- 25. Execution Time Overhead 25 Phoronix Test SuiteNas Parallel Benchmark 3.8% 4.2%0.4% 0.4% -2.05% 3.1%
- 26. NGINX - Latency 26 Latency # Requests Transfer Rate 99th Latency 38.35 ms 8720 req/s 140.17 MB/s 1100.0 ms 41.62 ms 8793 req/s 141.08 MB/s 1130.0 ms W/ Agent W/O Agent 20 NGINX threads 20 wrk threads to generate load 30 consecutive runs Setup
- 27. PTS Average Score 27 Average Score Phoronix Test Suite 0.4% 0.3% 3.3%
- 28. Power Consumption Overhead 28 Phoronix Test SuiteNas Parallel Benchmark 0.22% 0.93% 1.56% 1.80% 0.45% 2.98%
- 29. Conclusions 29 Fine grained container power monitoring Low overhead thanks to in-kernel aggregation Validation of the approach with a cloud-like and an HPC-like benchmark
- 30. Future Work 30 Impact on latency-sensitive applications Adjust dynamic window for long running jobs Integration with a power-aware orchestrator
- 31. https://necst.it/ https://www.slideshare.net/necstlab Tommaso Sardelli {tommaso.sardelli@mail.polimi.it} Rolando Brondolin {rolando.brondolin@polimi.it} Marco D. Santambrogio {marco.santambrogio@polimi.it} Thank You