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Portable Energy-Aware Cluster-Based Edge Computers

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The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. This talk was given at SEC'18 (http://acm-ieee-sec.org/2018/) and reports on the paper 'Portable Energy-Aware Cluster-Based Edge Computers'.

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Portable Energy-Aware Cluster-Based Edge Computers

  1. 1. Portable Energy-Aware Cluster-Based Edge Computers Thomas Rausch, Cosmin Avasalcai, Schahram Dustdar TU Wien, Vienna Austria Distributed Systems Group http://dsg.tuwien.ac.at ACM/IEEE Symposium on Edge Computing 2018, Bellevue, WA
  2. 2. 2 Edge Computers Cloudlet Cloud Server Computer Edge Computer Extension to the Edge
  3. 3. 3 Cloudlets for Fieldwork Scenarios Edge CloudIoT Lewis et al., 2014. “Tactical cloudlets: Moving cloud computing to the edge” Edge Computer Requirements ● Performance ● Portable ● Energy-Efficient ● Reliable Edge Computer Requirements ● Performance ● Portable ● Energy-Efficient ● Reliable
  4. 4. 4 Cluster-Based Edge Resources? Sun Modular Datacenter Ubuntu Orange Box (Intel NUC cluster) 1 Elkhatib et al., 2017, “On Using Micro-Clouds to Deliver the Fog” “Micro Clouds” 1 Server Computers SOC & Single Board Computers
  5. 5. 5 Cluster-Based Edge Computer Prototype Motherboard ASUS P10S-I Mini-ITX CPU Intel Xeon E3-1230 (4 cores + HT) RAM 2x16GB Kingston HyperX Fury SSD Intel SSD 600p 128 GB M.2. PSU picoPSU-90 12V
  6. 6. 6 Energy-Aware Clustered Edge Computers 1 13 3 2 2 4 4
  7. 7. 7 Examine Cluster Configurations ● Resource Utilization? ● Energy Consumption? ● System Responsiveness? SqueezeNet MXNet Model Server
  8. 8. 8 Energy Signatures of Node Operations Offline: 2 W Shutdown: 4-6 s ~620 J Boot (WoL) Docker container with MXNet starts Average Idle: 9 W Boot: 45-48 s ~39 J E(idle(t )) = E(boot) + E(shutdown) t = ~110 s Boot Cycle
  9. 9. 9 ∑(E(ni)) 17.0 Wh 19.4 Wh 19.1 Wh 19.3 Wh n1 n2 RTT .99 .95 μ CPU n1 : 100% n2 : off n3 : off n4 : off n1 : 90% n2 : 10% n3 : off n4 : off n1 : 80% n2 : 20% n3 : off n4 : off n1 : 70% n2 : 30% n3 : off n4 : off 300r/s
  10. 10. 10 ∑(E(ni)) 19.4 Wh 19.4 Wh 19.4 Wh 21.5 Wh n1 : 60% n2 : 40% n3 : off n4 : off n1 : 50% n2 : 50% n3 : off n4 : off n1 : 33% n2 : 33% n3 : 33% n4 : off n1 : 25% n2 : 25% n3 : 25% n4 : 25%
  11. 11. 11 Conventional Wisdom [R]ecent studies show the CPU utilization has a linear relationship on power consumption, when dynamic voltage and frequency scaling is applied. [R]ecent studies show the CPU utilization has a linear relationship on power consumption, when dynamic voltage and frequency scaling is applied. Farahnakian et al., 2014. Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning Kusic et al., 2009. Power and performance management of virtualized computing environments via lookahead control 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 20 40 60 80 100 120 140 160 HP ProLiant G5 HP ProLiant G4 CPU (%) W
  12. 12. 12 Intricacies of Power Management CPU % Freq (MHz) Power (W) RTT Segmented relation
  13. 13. 13 Workload Centric View Questions that arise ● How to cooperate with hardware? ● Pareto optimality energy vs. responsiveness? ● How to measure for multi-tenancy? Frequency 1.0 3.3.5 GHz
  14. 14. 14 Dipl.-Ing. (MSc), BSc Thomas Rausch Research Assistant TU Wien Information Systems Engineering Argentinierstrasse 8-194-02, Vienna, Austria T: +43 1 58801-184838 E: trausch@dsg.tuwien.ac.at http://dsg.tuwien.ac.at/staff/trausch

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