The document discusses a prototype of energy-aware, cluster-based edge computers intended for improved performance and efficiency in edge computing environments. Key topics include the requirements for edge computers, configurations for energy consumption, and considerations for power management in relation to CPU utilization. It highlights the research contributions of various studies and poses questions regarding hardware cooperation and energy responsiveness.

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
Edge Computers
Cloudlet
Cloud
Server Computer
Edge Computer
Extension to the Edge

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
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
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
Energy-Aware Clustered Edge Computers
1
13
3
2
2
4
4

7. 7
Examine Cluster Configurations
● Resource Utilization?
● Energy Consumption?
● System Responsiveness?
SqueezeNet
MXNet Model Server

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
∑(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
∑(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
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
Intricacies of Power Management
CPU %
Freq (MHz)
Power (W)
RTT
Segmented relation

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
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