Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters

Transcription

1 Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters Ivan Rodero1, Eun Kyung Lee1, Dario Pompili1, Manish Parashar1, Marc Gamell2, Renato J. Figueiredo3 1 NSF Center for Autonomic Computing, Rutgers University, NJ, USA 2 Open University of Catalonia, Barcelona, Spain 3 NSF Center for Autonomic Computing, University of Florida, FL, USA Energy Efficient Grids, Clouds and Clusters, Brussels, October 26, 21

2 Agenda Context and Motivation Datacenter Thermal Management Energy Efficiency and Tradeoffs Evaluation Methodology Results Next Steps Conclusions 2

3 Energy-Efficient Autonomic Management for High Performance Computing Workloads Cross-infrastructure Power Management Application-aware Controller Actuator Application/ Workload Sensor Observer Controller Actuator Virtualization Sensor Observer Controller Actuator Resources Sensor Observer Controller Actuator Physical Environment Sensor Observer Cloud Cloud (private, public, hybrid, etc.) (private, public, hybrid, etc.) Cross-layer Power Management Virtualized Instrumented infrastructure Goal: Autonomic (self-monitored and self-managed) computing systems: Optimizing energy efficiency while ensuring Quality of Service delivered (performance) 3

4 Cross-Layer Architecture Observer Correla3ons Global Controller Observer 1 Actuator Application/ Workload Sensor 1 Local Controller Actuator 1 Virtualization Sensor 2 Local Controller Actuator 2 Resources Sensor 3 Local Controller Applica3on req. profiles Observer 2 VM efficiency Observer Controls Local Controller Observer 3 Actuator 3 Physical Environment Resource performance Observer 4 Sensor 4 Environment predic3on Managed Environment Observer s Sensing Port Request Flow Actuator Informa3on Flow Resource Flow Sensor Controller s Command 4

5 Cross-Layer Energy-Efficient Autonomic Management Abnormal operational state detection Distributed Online Clustering (e.g., workload) Physical sensing physical layer (e.g., thermal hotspots) Reactive and proactive approaches Reacting to anomalies to return to steady state Predict anomalies in order to avoid them QoS Different paths for reaching steady operational state QoS Abnormal state Energy Efficiency Energy Efficiency Thermal efficiency Steady State? Cross-layer actions Thermal efficiency QoS (AUTONOMIC) Energy Efficiency Thermal efficiency 5

6 Interactions between Autonomic Components Scheduling Global controller HPC Workload Workload Characterization (e.g., DOC) Observer (correlations) Provisioning and maping Pinning Trading with 3rd parties VM Migration Proactive configuration Reactive Cooperate Environment monitoring (temperature, etc.) Component-level Power Management Designs for aggressive power management Depend Reactive configuration Resources monitoring (load, power, etc.) 6

7 Datacenter s Thermal Behavior 8 [C] ï Node Number Time [min] 8 [C] ï Temporal correlation of the measured temperature under different workload distributions 4 5 Node Number 2 Time [min] 8

8 Reacting to Thermal Hotspots 6 Internal Server Environment (Hotspot) Steady (C) Reaction: VM migration Power (W) 22 2 Correlation between server s temperature and power

9 Thermal Management Approaches Assumption: $ $ $ The lower power dissipated # $The lower heat generated Pcpu C α V 2 f Reducing the activity factor (α) VM Migration: move a VM to another server May reduce CPU activity, but also memory activity, etc. Potentially may result in lower CPU frequency if OS support Overhead (suspend, transfer data, resume, etc.) Requires availability in another server (impact on the target server) 1

10 Thermal Management Approaches (2) Reducing the activity factor (α) Pinning (in Xen platform): affinity in VCPUs PCPUs mapping CPUs without VMs running on them OS power management may result in automatic DVFS Penalty on the performance (resource sharing) Reducing the frequency/voltage of CPUs ( V2 f ) Processor DVFS Penalty on the performance (in general higher response time) Different possibilities Different frequencies/voltages Applied to all CPUs/cores or to a subset 11

11 Goals and Tradeoffs Goal: selection of appropriate technique to mitigate the effects of thermal hotspots Energy-Efficient Driven by optimization requirements. Examples: Lower energy consumption Lower maximum/average power dissipation Reduce temperature 5 oc (based on a threshold) A penalty of up to 1% on response time is acceptable There are well known tradeoffs between performance and energy efficiency But also we need to consider other dimensions such as thermal efficiency (temperature) 12

12 Goals and Tradeoffs (2) Example: Tradeoff between temperature and performance of pinning 4 VMs into different PCPUs 13

13 Evaluation Methodology Server configuration: Two servers based on Intel quad-core Xeon processors (but only 3 CentOS Linux operating system with a patched kernel with Xen version 3.1 Additional hardware: A Watts Up?.NET power meter to empirically measure instantaneous power Operate at four frequencies ranging from 1.6GHz to 2.4GHz available under Xen 3.1) Accuracy of ±1.5% of the measured power with sampling rate of 1Hz TelosB motes to measure both internal (not sensors embedded into the CPU) and external temperatures A Sunbeam SFH111 heater (directed to the servers) in order to emulate a thermal hotspot Workload: HPL linpack

14 Energy Consumption Estimation Use case: No running VMs in target server before migration 15

15 Results Power (W) Power (W) 22 4 Correlation between internal and external temperature CPUs at 2.4GHz 2CPUs at 1.6GHz 4CPUs at 2.13GHz 4CPUs at 1.6GHz CPUs at 2.4GHz 2CPUs at 1.6GHz 4CPUs at 2.13GHz 4CPUs at 1.6GHz Reference Pinning VMs to 3CPUs Pinning VMs to 2CPUs Pinning VMs to 1CPU Reference Pinning VMs to 3CPUs Pinning VMs to 2CPUs Pinning VMs to 1CPU Reference Pinning VMs to 3CPUs Pinning VMs to 2CPUs Pinning VMs to 1CPU Power(W) (W) Power DVFS: using 2 CPUs at 1.6 GHz presents similar results than using 4 CPU at 2.13 GHz Correlation between temperature and power 4 Reference Migrate 1VM Migrate 2VMs Migrate 3VMs CPUs at 2.4GHz 2CPUs at 1.6GHz 4CPUs at 2.13GHz 4CPUs at 1.6GHz oc InternalInternal 8 oc External External oc ExternalExternal oc Internal External Reference Migrate 1VM Migrate 2VMs Migrate 3VMs Power (W) Power (W) Reference Migrate 1VM Migrate 2VMs Migrate 3VMs oc ExternalExternal oc InternalExternal

16 Results (2) 17

17 Next Steps Autonomic VM allocation and reactive technique decision Cross-layer design approach Examples: component-level power management, workload clustering, etc. Application-aware (workload characterization into CPU-, I/O-, etc. intensive ) Optimization targets based on self-monitoring Models are required VM migration, DVFS (work presented in this presentation) VM allocation (#VMs, workload characteristics, combinations, etc.) Preliminary results based on a brute force algorithm Models at the server and datacenter level 18

18 Conclusions Tradeoffs exist between: of reactive thermal management techniques for HPC workloads Pinning is an effective mechanism to react to thermal anomalies under certain conditions Performance Energy efficiency Thermal efficiency In addition to VM migration In contrast to DVFS Different mechanisms behaviors observed depending on the system characteristics and optimization goals. Autonomic decision making is required Cross-layer designs should improve datacenter s management 2

19 Thank you! Energy Efficient High Performance Computing Initiative Center for Autonomic Computing, Rutgers University 21