Towards Deploying Decommissioned Mobile Devices as Cheap ... · Towards Deploying Decommissioned...
Transcript of Towards Deploying Decommissioned Mobile Devices as Cheap ... · Towards Deploying Decommissioned...
Towards Deploying Decommissioned Mobile Devices as Cheap Energy-Efficient Compute Nodes
Mohammad Shahrad and David Wentzlaff
Monday, July 10, 2017
Growing Dominance of Smartphones
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[Gartner, IDC, Zuberbühler Associates AG][Ericsson Mobility Report 2016]
2015 2021
3.2 billion
6.3 billion
Smartphones
PCsTablets
Decommissioned Smartphones
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Physical Damage Expanding OSs
New Features Fashion
Bulk Recycling
Electronic Waste
Refurbish
Reusing smartphones as compute nodes?
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Using the computational capacity of decommissioned devices
Outline
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Why using mobile SoCs now?
How to integrate mobile devices into data center fabric?
What to do with such mobile devices in data centers?
Does such deployment make economic sense?
Trend 1: Commodity vs. Mobile Performance
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[Rajovic et al., Supercomputing with commodity CPUs: Are mobile SoCs ready for HPC?, SC ’13]
Trend 2: Diminishing Performance Growth
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End of Moore’s Law is starting to kick in for mobile SoCs.
The performance gap between a new vs. a 3-year old phone is decreasing.
8,000
80,000
CPU
Mar
k R
atin
g
Release Date
Extending Effective Lifetime to Improve Carbon Footprint
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[Ercan et al., Life cycle assessment of a smartphone, ICT4S ’16]
Proposed Integration Fabric
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Mobile Device Cage(180 × 80 × 9 mm)
FansRouter
Power Supply
2U Height
84 × 470~~ SoC TDP = 3.5 W
Networking
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1. USB Tree + Master Node
101010
(a) USB Tree A (b) USB Tree B
1010
Master Node Master Node
Smartphones
3. Intra-server WiFi
Supply
Low SNR and high congestion.
Low BW High
Energy
High Latency
2. USB On-The-Go (OTG)
USB OTG Ethernet Ethernet Switch
Upstream Network
Ethernet Switch Cost (price+space)
High BW per DeviceNo Need for a Master Node
Establish virtual Ethernet through master nodeMaster becomes single point of failure
Mobile Batteries as Distributed UPSs
Batteries could be used to perform power capping in data centers.
Mobile batteries are designed forhigh-density energy storage.
Average mobile battery: > 3100mAh After 3 years (15% dep/yr): >1900mAh
84 device per server: ~160Ah (4x-8x the typical UPS density)
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Time
Pow
er D
raw
Power Cap
Battery Power Draw
Battery Recharge
Targeted Applications?
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Non-CPU Elements of Mobile SoC Lower Reliability
Lower CPU PerformanceHigh Relative I/O Bandwidth
I/O-intensive Application
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Smaller cores generally have better I/O to compute ratio.
Mobile cores have better performance-per-Watt.
Prior examples:
Fast Array of Wimpy Nodes [Andersen et al., SOSP ’09] • Using low-end embedded CPUs in a key-value store system. • Two orders of magnitude more queries per Joule.
Web Search Using Mobile Cores [Reddi et al., ISCA ’10] • Comparing Atom to Xeon processors or perform web search. • Better energy efficiency • Worse QoS
Low-end VM Provisioning
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t2.nano
t2.micro
f1-micro
g1-small Multiple tiny burstable instances can be provisioned on each mobile device.
Each mobile device (on average): • > 2GB of memory • > 5 cores
vCPU Mem (GB)11
0.51
vCPU Mem (GB)0.20.5
0.61.7
GPU-accelerated Dwarfs
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Lower-reliability Nodes
•Current GPU-accelerated IaaS instances use beefy GPUs (e.g. EC2 P2 instances)
•Mobile SoCs are equipped with smaller GPUs
•Enabling low-end GPU acceleration for small VMs
•Dynamic reliability platforms use resource heterogeneity for cost saving while meeting SLAs
•Mobile devices can be used as low-cost low-reliability nodes
Does it make economic sense?
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TCO Comparison with a Similar-density Server
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Two simplifying assumptions:
1.Parallelizable workload (using Geekbench 4 cross-platform benchmark)
2.Considered eBay retail price for used phones (could be much cheaper)
TCO: Total Cost of Ownership
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Lifetime (month)
400
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1400
Month
ly T
CO
($)
A, A=45%
B, B
=45%
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Lifetime (month)
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1000
1200
1400
Month
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CO
($)
A, A=45%
B, B
=77.5%
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Lifetime (month)
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($)
A, A=100%
B, B
=100%
A=45%,
B=45%
TCOB
<TCOA
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Server A Lifetime (month)
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Serv
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ifetim
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A=45%,
B=77.5%
TCO B<TCO A
TCO A<TCO B
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Server A Lifetime (month)
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Serv
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B L
ifetim
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month
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A=100%,
B=100%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Serv
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B L
ifetim
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month
)6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
800
1000
1200
1400
Month
ly T
CO
($)
A, A=45%
B, B
=45%
6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
800
1000
1200
1400
Month
ly T
CO
($)
A, A=45%
B, B
=77.5%
6 12 18 24 30 36 42 48 54 60
Lifetime (month)
0
1000
2000
3000
4000
5000
6000
Month
ly T
CO
($)
A, A=100%
B, B
=100%
A=45%,
B=45%
TCOB
<TCOA
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Serv
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month
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A=45%,
B=77.5%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Serv
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B L
ifetim
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month
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A=100%,
B=100%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Serv
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B L
ifetim
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month
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TCO comparison with a Similar-density Server
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6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
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1000
1200
1400
Mo
nth
ly T
CO
($
)
A, A=45%
B, B
=45%
6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
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1000
1200
1400
Mo
nth
ly T
CO
($
)
A, A=45%
B, B
=77.5%
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Lifetime (month)
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6000M
on
thly
TC
O (
$)
A, A=100%
B, B
=100%
A=45%,
B=45%
TCOB
<TCOA
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Server A Lifetime (month)
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Se
rve
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Life
time
(m
on
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A=45%,
B=77.5%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Se
rve
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Life
time
(m
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A=100%,
B=100%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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A: Lenovo Flex server B: Our proposed server
𝛿: annual depreciation rate (determines the residual value)
Thanks for your attention!
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Next slide: Discussion Topics
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(a) USB Tree A (b) USB Tree B
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Master Node Master Node
Smartphones
8,000
80,000
CPU
Mar
k R
atin
g
Release Date 6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
800
1000
1200
1400
Month
ly T
CO
($)
A, A=45%
B, B
=45%
6 12 18 24 30 36 42 48 54 60
Lifetime (month)
400
600
800
1000
1200
1400
Month
ly T
CO
($)
A, A=45%
B, B
=77.5%
6 12 18 24 30 36 42 48 54 60
Lifetime (month)
0
1000
2000
3000
4000
5000
6000
Month
ly T
CO
($)
A, A=100%
B, B
=100%
A=45%,
B=45%
TCOB
<TCOA
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
6
12
18
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30
36
42
48
54
60
Serv
er
B L
ifetim
e (
month
)
A=45%,
B=77.5%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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30
36
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54
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Serv
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B L
ifetim
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month
)
A=100%,
B=100%
TCO B<TCO A
TCO A<TCO B
6 12 18 24 30 36 42 48 54 60
Server A Lifetime (month)
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Serv
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B L
ifetim
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month
)
Some Discussion Topics
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•Reliability of used mobile devices under data center workload
•The best management scheme (centralized, distributed, server-level, etc.)
•Using SoC accelerators for domain-specific applications. (security, neural net, etc.)
•Dealing with extreme heterogeneity
•Security concerns with used phones