Lecture 3
Energy consumption"
Presented by:
Ekhiotz Jon Vergara
Real-time Systems Laboratory
Department of Computer and Information Science (IDA)
Linköping University
Sweden
17 November 2015
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
1
Electrical
units
Electrical units
Current
Resistance
Potential
difference
Metric
Unit
Description
Potential
difference
Difference of charges
Unit
Symbol
Volt Description
or voltage
between two points
Potential difference
V
ChargeElectric
differencecharges
CurrentVolt
or
Ampere
or voltage
between two points
intensity
movement rate
Current
Ampere I
Movement
of theopposition
charge (flow)to
Circuit
Resistance
Ohms
current flow
Resistance
Ohm
R
Circuit opposition
to current flow
Storage of energy as
Farad
Capacitance Capacitance
Farad
C
Storage of energy as electric field
an electric field
Metric
S. Gibilisco, "Teach Yourself Electricity and Electronics", 4rt edition, McGraw-Hill Professional
Publishing , 2006 January 18, 2012
S. Gibilisco, "Teach Yourself Electricity and Electronics", 4rt edition, McGraw-Hill Professional Publishing , 2006
January 18, 2012
4
2
Energy
metrics
Energy
metrics
Electrical energy is transformed in heat!
Power = Voltage * Intensity
Power = Voltage * Current
Metric
Unit
Description
Rate of energy
Metric
Power
Unit
Watt
Symbol
Description
expenditure
Power
Energy Watt
P Joule
Powerofdissipated
over per
Amount
energy consumed
length
of time
unita of
time (rate)
E
Power dissipated over
a time period
Energy
Joule
1 Joule = 1 Watt * second = 1 Ws
3600 Joules = 1 Watt * hour = 1 Wh
1 Joule = 1 Watt * 1 second = 1 Ws
January 18,
! 2012
3600 Joules = 1 Watt * 51 hour = 1 Wh
!
3
Energy metrics: Example
SMS
!
The cost of sending a SMS
!
Energy is power over time: 2.23 Joules
Average power: 0.2 Watts
1
0.8
Power (Watts)
!
0.6
0.4
0.2
0
0
2
4
6
Time (seconds)
4
8
10
12
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
5
Is energy just another resource?
!
Energy has a global system nature
!
A system is composed by several components
consuming concurrently
Component
Component
Component
Energy source
!
Components have different management techniques
"
!
Energy consumption at component level is already complex
Applications share the different components
6
Energy efficiency goals
!
Optimise the resource consumption (avoid waste)
!
Reduce energy consumption
!
Prolong battery lifetime
!
Reduce cooling requirements and noise
!
Reduce operating costs
7
General sources of energy waste
!
System design is full of complex tradeoffs
"
"
"
"
!
General-purpose system
Peak performance
Worst-case tolerance
Availability
System functionality as independent modules
"
"
Modularity and interaction
System components designed separately
!
CPU, network interface…
8
Images:
http://openclipart.org/detail/3402/tachometer-by-digitalink-3402
http://openclipart.org/detail/120691/business-people-siluete---by-systemedic
General-purpose solutions
!
Good performance for a multitude of different applications
"
"
"
Union of maximum requirements of each application class
E.g., smartphone vs. MP3 player
Legacy solutions
9
Image under CC license by cdwaldi on Flickr
http://openclipart.org/detail/14794/red_sledgehammer-by-halfhaggis
http://openclipart.org/detail/184624/walnut-by-frankes-1846240
Peak performance and growth
!
Overprovisioning to plan for the future
"
Ensure enough capacity
Vs.
10
Images:
http://openclipart.org/detail/182940/bus-2-mono-by-Jarno-182940
http://openclipart.org/detail/173172/people-hitchhiking-by-vlodco_zotov-173172
Design process structure
!
Hardware and software separately
!
Divided system functionality across components
!
Layers
Layer 3
"
Local optimisations not optimal for
global efficiency
Layer 2
Layer 1
11
Energy efficiency at design stage
!
Replacement with a more power-efficient alternative
!
Holistic solutions
"
"
Broad scope of the problem
Cross-layer interaction
!
Optimise energy efficiency for the common case
!
Design only for required functionality and requirements
Image from: https://openclipart.org/detail/201475/recipe-book-by-bnsonger47-201475
12
Energy efficiency at runtime
!
Trade off some other metrics for energy
!
Disable or scale down unused resources
!
Combination of multiple tasks in a single energy event
!
Spend someone's else power
!
Spend power to save power
13
Key architectural elements of solutions
!
Analysis tools to predict resource usage trends
!
Energy awareness
"
!
Monitoring infrastructure
Control algorithms and policies
http://openclipart.org/detail/35353/tango-utilities-system-monitor-by-warszawianka
http://openclipart.org/detail/160057/machine-control-blue-by-zxmon21
14
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
15
Energy proportionality
!
Def.: Power must be proportional to the utilisation
A system must have:
"
"
Wide dynamic power range
Low base power
!
"
!
If the room is empty, turn off the lights
Low-power active modes
Most systems present
low energy proportionality
Proportional consumption
High idle consumption
Constant consumption
Power
consumption
!
Utilisation
L. A. Barroso and U. Hölzle, The Case for Energy-Proportional Computing, IEEE Computer, vol. 40. 2007
16
Match the utilisation
!
Overconsumption of energy
Constant consumption
High idle consumption
Proportional consumption
Designed for the utilisation
Power
consumption
!
Low energy proportionality
Average utilisation
!
Utilisation (%)
L. A. Barroso and U. Hölzle, The Case for Energy-Proportional Computing, IEEE Computer, vol. 40. 2007
17
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
18
Load consolidation
!
Switching off the light is not enough!
!
The efficiency of the strategy is increased by the
interaction with “consolidation”:
Group people in one room to switch off other rooms’ lights
Systems usually work at low utilization,
which means low efficiency
!
Change the utilisation!
Power
consumption
!
19
Utilisation
Consolidation in time
Time
Load
Capacity
Server 2
Load
Capacity
Time
Server 2
Load
Capacity
Time
20
Consolidation
Server 1
Time
No Consolidation
Server 1
Load
Capacity
Consolidation in time
Time
Load
Capacity
Server 2
Load
Capacity
Delay
Server 2
Time
Load
Capacity
Time
21
Consolidation
Server 1
Time
No Consolidation
Server 1
Load
Capacity
Case study: Cellular traffic
!
More consuming
!
Energy consumption at component level is complex
Example case: cellular transmission (3G)
3 main states:
High
6 Mbps
Medium
32 kbps
Power
consumption
!
Low
Utilisation
State is controlled by the
network operator
(Data rate)
22
Power profile of sending 800 bytes
High
Inactivity timers and transition delays
!
"
T1
Overheads
Medium
Transmission
T2
1.6
1.4
Low
Power (Watt)
1.2
1
0.8
0.6
0.4
0.2
0
0
T1
Transition
delay
1
2
3
4
5
Transition
delay
6
7
8
T2
9
Time (seconds)
23
10
Transition
delay
11
12
13
14
15
yp
e
k
Sk
Ta
l
G
M
W
ha
Packet size (bytes)
0
er
1500
Vi
b
1000
er
500
50
ng
0
0
Communication energy
se
!
100
Ki
k
0.2
WhatsApp
Messenger
GTalk
150
es
0.4
200
pp
Instant Messaging
0.6
250
ts
A
0.8
Data sent (kilobytes)
Empirical CDF of
packet size
1
100
Energy (Joules)
80
More
than
twice
60
40
20
0
WhatsApp
Kik
Messenger
Viber
GTalk
Skype
E. J. Vergara, S. Andersson, and S. Nadjm-Tehrani, When Mice Consume Like Elephants:
Instant Messaging Applications, e-Energy '14, ACM, 2014.
24
Intermittent transmissions are wasteful
!
Example: Instant Messaging (Skype)
Energy overhead > transmission energy
Power (Watts)
"
1.5
DCH!
High
1
FACH!
Medium
0.5
PCH!
Low
0
0
5
10
15
20
25
30
35
40
25
30
35
40
Time (seconds)
Data (bytes)
800
600
400
200
0
0
5
10
15
20
Time (seconds)
25
Power (Watts)
Aggregate the traffic
1.5
!
!
1
!
0.5
Background data traffic scheduler Background Traffic
Maximum time to wait (Tw) for each transmission
3G radio level information to schedule transmissions
"
Power (Watts)
0
0"
1.5
"
RRC state information
20 buffer
40
60
80
100
120
RLC
thresholds
Time (seconds)
Inactivity timers
FACH
Medium
Low
PCH
140
160
180
200
Shaped Traffic
1
High
DCH
DCH
High
Timer = 90 s
FACH
Medium
0.5
Low
PCH
0
0
20
40
60
80
100
120
140
160
180
Time (seconds)
Ekhiotz Jon Vergara and Simin Nadjm-Tehrani. (e-Energy '12). ACM. 2012
26
200
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
27
Switch off or scale down?
!
Component power management
!
Different levels
"
"
"
!
Circuits
Architecture
Compiler and systems
Applied to different hardware
Matthew Garret. Powering Down, Communications of the ACM 51, 9 (September 2008), 42-46
28
Center image center under CC license by jpstanley on Flickr and
right image under GPL license
Example: Processor
!
Does not run at 100% capacity all the time
!
Architecture techniques
"
"
"
CPU dynamic frequency and voltage scaling
Low power states
Tickless kernel
29
Dynamic voltage and frequency scaling
!
Dynamic adjustment of voltage/frequency
"
!
Trade off power dissipation / performance
Decision level
"
System level (OS)
!
!
"
Program level
!
!
"
Idleness of the system drives decision
Voltage/frequency scaled to eliminate idle periods
Program behaviour drives decision
E.g., scale down when program knows that has to wait
Hardware level
!
Exploits different timings of hardware components and system
techniques
30
DVFS in Linux
!
5 governors to control the performance level of processors
"
"
"
Performance: highest frequency
Powersave: lowest frequency
Ondemand: periodically monitor the system
User-space
Increase when system load > threshold
Decrease when system load < threshold
Monitoring
application
!
!
"
Conservative:
!
"
ondemand and adjust performance level
conservatively
Interactive:
!
!
!
Periodic load-based estimation at user space
Real-time kernel thread to adjust frequency
Fast adjusting from idle
DVFS
module
Kernel-space
Sangwook Kim, Hwanju Kim, Jongwon Kim, Joonwon Lee, and Euiseong Seo. 2013.
Empirical analysis of power management schemes for multi-core smartphones. ICUIMC '13
31
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
32
Energy management
!
Planning and operating the energy resource
"
!
Optimise the resource consumption (avoid waste)
Dimensions:
"
System components (e.g., CPU, memory or wireless interface)
Power management of components (DVFS, radio resource allocation)
"
Entities sharing system components (e.g., applications)
Allocate energy to software (tasks) to run
App 1
App 2
App 3
App 4
OS
Component
Component
Energy source
33
Component
Is energy management easy?
!
Energy has a global system nature
"
!
Hard to manage!
Two common alternatives:
"
Best effort - trust on entities
"
Energy budgeting
34
Energy accounting
!
Quantify, analyse and report the energy consumption of
entities/activities in the system
!
Applications of energy accounting:
"
"
"
Evaluate energy efficiency
Transparency # energy awareness
Efficient energy management
!
Quantify energy consumption from the components ✓
!
Prescribe it to system entities/activities ?
"
Cost allocation is a hard problem without a best solution
35
Energy accounting – example
Power%
Timeout%energy%
Ac1on%
An%example%system%
High%power%
Energy%consumed%by%en1ty%1%in%isola1on%
Ac1ve%
Power%
Time%
Energy%consumed%by%en1ty%2%in%isola1on%
Transi1on%
delay%
Timeout%
Power%
Time%
The%total%actual%energy%consump1on%
Low%power%
Time%
En#ty&1&
?"
?"
.&.&.&.&.&.&.&.&.&
Inac1ve%
En#ty&2&
How should we divide
the total energy
consumption?
E(N):&Total&energy&consump#on&
36
This lecture
!
Basic energy background
!
Energy as a resource
!
Energy proportionality
!
Load consolidation
"
Case study
!
Scaling down power consumption
!
A brief look to energy management
!
Application context matters
37
Context matters
!
!
Application needs to be considered
Wireless Sensor Networks
"
"
!
Body Area Network (Healthcare)
Cattle monitoring
Application requirements
"
"
"
"
"
Scalability
Coverage
Real-time delay
Quality of service
Handling Mobility
T. Rault et al. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, Elsevier. 2014
38
Analysing energy-efficient mechanisms
!
Energy-efficient mechanisms
"
Data reduction
!
!
"
Sleep/wakeup schemes
!
!
!
Aggregation
Compression
Duty-cycling
Topology control
Application requirements
"
"
"
"
"
Scalability
Coverage
Real-time delay
Quality of service
Handling Mobility
T. Rault et al. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, Elsevier. 2014
39
Final remarks
!
Energy is a vital resource
!
Energy has a global system nature
"
The resource of the resources
"
Hard to manage
!
Energy proportionality is desired
!
Energy-efficient mechanisms
"
Avoid waste – Energy-efficient recipes
"
But focus on the requirements!
40
Reading guidelines
!
P. Ranganathan. Recipe for efficiency: principles of
power-aware computing. Communications of the ACM 53, 4
(April 2010), 60-67.
!
Optional
"
"
L. A. Barroso and U. Hölzle, The Case for Energy-Proportional
Computing, IEEE Computer, vol. 40. 2007
T.Rault, A. Bouabdallah, Y. Challal, Energy efficiency in wireless
sensor networks: A top-down survey, Computer Networks, Volume
67, 4 July 2014, Pages 104-122, ISSN 1389-1286, http://dx.doi.org/
10.1016/j.comnet.2014.03.027.
41