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'.

Technology

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

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

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

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

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
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

My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm. Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.

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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process. In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds. - These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

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Removing Uninteresting Bytes in Software Fuzzing

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Portable Energy-Aware Cluster-Based Edge Computers

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

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