Post on 21-Dec-2015
Mobile Computing – A Distributed Systems Perspective
Prof. Nalini VenkatasubramanianDepartment of Computer Science
University of California, Irvine
Outline
The Future: Mobile and Pervasive Computing An Applications View A Content View
Multimedia Content QoS Basics and End-to-end support
A Systems View Device, Operating System Networks, Middleware Application, Content A Cross-Layer Approach to Systems Support
Supporting Cross Cutting Concerns (Security, Reliability, Heterogeneous Interoperability)
Pervasive Computing and Communication
Proliferation of devices System support for multitude of smart (mobile) devices
that • attach and detach from a distribution infrastructure• produce a large volume of information at a high rate• limited by communication and power constraints
Require a customizable global networking backbone. That can handle heterogeneous needs of applications
• Quality of Service (QoS), security, reliability That can make effective use of the underlying infrastructure
Intelligent Middleware is key to supporting application needs in a highly dynamic environment
Distributed Mobile Applications
In the last 5 years….. New devices w/ new capabilities
Data capture, storage, presentation
New apps Social networking,…. Multimedia applications, gaming, data capturing,
download/upload/streaming Information seek/notification
New network infrastructures DTN, WiMax, UWB,…
New computational infrastructure Cloud computing, grids…
New problems and challenges…
IntroductionIntroduction
Internet
GSM or CDMA
BSBS
Cell PhoneCell Phone
PDAPDA
LaptopLaptopWLANAccess Point
ad hoc network
Users
Networks
Applications
Mobile Devices
Mobile Ecosystem
Technology Incentive
Growth in computational capacityMM workstations with audio/video processing capabilityDramatic increase in CPU processing power Dedicated compression engines for audio, video etc.
Rise in storage capacityLarge capacity disks (several gigabytes)Increase in storage bandwidth,e.g. disk array technology
Surge in available network bandwidthhigh speed fiber optic networks - gigabit networksfast packet switching technology
Enabler: Mobile Communication Networks
Cellular - GSM (Europe+), TDMA & CDMA (US) FM: 1.2-9.6 Kbps; Digital: 9.6-14.4 Kbps (ISDN-like services)
Public Packet Radio - Proprietary 19.2 Kbps (raw), 9.6 Kbps (effective)
Private and Share Mobile Radio Wireless LAN - wireless LAN bridge (IEEE 802.11)
Radio or Infrared frequencies: 1.2 Kbps-15 Mbps
Paging Networks – typically one-way communication low receiving power consumption
Satellites – wide-area coverage (GEOS, MEOS, LEOS) LEOS: 2.4 Kbps (uplink), 4.8Kbps (downlink)
Mobile Network Architecture
FIXED NETWORK
PDA
FIXEDHOSTBASE
STATION
BASESTATION
BASESTATION
Mbps to Gbps
MOBILE HOST
WIRELESS LAN CELL2Kbps - 15Mbps
WIRELESS RADIO CELL9Kbps - 14Kbps
BASESTATION
PDA
Wireless network characteristics
Variant Connectivity Low bandwidth and reliability
Frequent disconnections • predictable or sudden
Asymmetric Communication Broadcast medium
Monetarily expensive Charges per connection or per message/packet
Connectivity is weak, intermittent and expensive
Device Characteristics
Battery Power restrictions Transmit/receive, disk spinning, display, CPUs,
memory consume powerResource constraints
Mobile computers are resource poor Reduce program size Computation and communication load cannot be
distributed equally Small screen sizes
Mobility Characteristics
Location changes• location management - cost to locate is added to communication
Heterogeneity in services bandwidth restrictions and variability
Dynamic replication of data• data and services follow users
Querying data - location-based responses Security and authentication
System configuration is no longer static
Challenges
ChallengesChallenges
Glo
bal
Con
text
Netw
ork
Context
Need high degree of “network awareness” and “customizability”congestion rates, mobility patterns etc.QoS driven resource provisioningHeterogeneous networks
Dev
ice
Conte
xt
Heterogeneous devicesLimited battery lifetimeSize/weight limitationsComputation/Communication constraints
Application
Context
Application Context Increasing QoS., security, reliability demandsSoft Real Time Constraints Support for traditional media (text, images) and continuous media (audio/video)Synchronization (e.g. lip sync., floor control)
System support for multitude of componentsAttach and detach from a distributed infrastructureDeal with large vol. of information at a high rateChanging global system state
Mobile Multimedia – Rich Media applications on mobile devices
Challenges• Soft Real time Requirements• High demands on CPU / Network• Loss in performance directly affects user perception
Opportunities• Predictable regular behavior allows for interesting optimizations and
adaptations
Wide Area Network
Wireless Network
Low-powermobile device
Access pointMEDIA SERVER Video request
Video stream
OBJECTIVE : Stream rich multimedia content at highest possible quality (user experience) over wired and wireless networks
Multimedia Information Systems: Challenges
Sheer volume of dataNeed to manage huge volumes of data
Timing requirementsamong components of data computation and communication.Must work internally with given timing constraints - real-time
performance is required.
Integration requirementsneed to process traditional media (text, images) as well as
continuous media (audio/video).Media are not always independent of each other - synchronization
among the media may be required.
High Data Volume of Multimedia Information
Speech 8000 samples/s 8Kbytes/s
CD Audio 44,100 samples/s, 2bytes/sample
176Kbytes/s
SatelliteImagery
180X180 km 2̂30m 2̂ resolution
600MB/image(60MBcompressed)
NTSC Video 30fps, 640X480pixels, 3bytes/pixel
30Mbytes/s(2-8 Mbits/scompressed)
Quality of Service in MM Applications
QoS: A Design Parameter for MM Minor violations of
performance requirements
Generally used to express constraints on Timing, availability, reliability,security, resource utilization
User
NetworkMM devices
System
Application
(Network QoS)(Device QoS)
(System QoS)
(Application QoS)
(Perceptual QoS)
(Operating and Communication System)
QoS Classes
QoS Service Classes determine reliability of offered QoS utilization of resources
Guaranteed Service Class QoS guarantees are provided based on deterministic and statistical QoS
parameter values. Predictive Service Class
QoS parameter values are estimated and based on the past behavior of the service
Best-effort Service Class No guarantees or only partial guarantees provided No QoS parameters are specified or some minimal bounds are given.
Supporting continuous media: Approaches
Admission controlProvide different service classes
Fast storage and retrieval of Multimedia contentOptimized organization (placement) of multimedia
files on diskSpecial disk scheduling algorithms
Efficient Memory ManagementSufficient buffers to avoid jitter Intelligent Caching
End-to-end QoS for Wired Applications
Usually achieved through QoS Brokers Coordinates interactions between multiple sessions
with QoS needs
Typical Functions of a QoS broker Resource Provisioning
Deal adaptively with incoming requestsAllocate server, network and client resources
Predictive and Adaptive Data Placement Re(configure) data to service requests more efficiently
Must maintain resource allocation invariants
Supporting QoS in Mobile Applications
New challenges Constantly changing system conditions
Network connectivity, user mobility
Device constraints Energy, CPU, display, bandwidth
This needs Provisioning, re-provisioning based on local conditions of wireless network Data Placement based on current and projected data access patterns
Cross-layer awareness required Resource provisioning algorithms utilize current system resource availability
information to ensure that applications meet their QoS requirements
QoS-based resource provisioning
Provisioning Network and Server Resources Effectively State information enables decision making for resource provisioning - e.g. Routing,
Scheduling and Placement Maintaining accurate and current system information is important
Existing Approaches In network : QoS Based Routing At Server : Server Load Balancing
Future Middleware will support CPSS (Combined Path and Server Scheduling)
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Data Placement in Mobile Environments
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Initial placement
After replication degree enforcement
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Design replication and intelligent
data placement mechanisms that Ensure effective resource
management Ensure QoS for admitted clients
Design caching mechanisms
Partition data/processing between mobile clients and infrastructure
Allows disconnected operation
Efficient power management
Need for a Cross Layer Approach
Information exchange needed To provide information good enough for resource provisioning tasks
such as admission control, load balancing etc.Need an information collection mechanism that is :
• is aware of multiple levels of imprecision in data
• is aware of quality requirements of applications
• makes optimum use of the system (network and server) resources
Sample Parameters Network link status, Data server capacity (Remote disk bandwidth,
Processor capacity), device constraints (battery power, memory limitations)
Case Study: Power Management Power Optimization in battery operated mobile
devices is a crucial research challenge Devices operate in dynamic distributed environments Power Management strategies need to be aware of
global system state and exploit it.
NETWORK CARD
DISPLAY
CPU
Display
NIC
CPU
Misc.
Existing Work in Power Optimization
Architecture
Architecture(cpu, memory)
• Soderquist (ACM Multimedia 97)• Azevedo (AWIA 2001)• Hughes, Adve (MICRO 01, ICSA 01)• Brooks (ISCA 2000), Choi (ISLPED 02)• Leback (ASPLOS 2000), Microsoft’s ACPI
• Flinn (ICDSP 2001), Yau (ICME 2002)• Krintz, Wolski (ISLPED 2004)• Noble (SOSP 97, MCSA 1999)• Li (CASES 2002), Othman (1998)• Abeni (RTSS 98)•Rudenko ( ACM SAC 99), Satyanarayan (2001)
Power Optimization has been extensively researched
Network
Network Interface Card
• Chandra (MMCN 04,02, USENIX 2002, ICPP 04)• Shenoy (ACM MM 2002) • Feeney, Nilson ( Infocom 2001)• Katz (IEICE 97)
Operating System
DVS, DPM, DriverInterfaces, system calls
• Ellis, Vahdat (IEEE pervasive 05, Usenix 03, ASPLOS 02) • Hao, Nahrstedt (ICMCS 99, HPDC 99, Globecom 01)• Yuan (MMCN 03,04, SOSP 03, ACM MM 04), • Rajkumar (03), Anand (Mobicom 03, Mobisys 04) • DVS (Shin, Gupta, Weiser, Srivastava, Govil et. al.)• DPM (Douglis, Hembold, Delaluz, Kumpf et. al.)
User/Application
Quality of Service Application/user feedback
Limitations of Current Approaches
Limited co-ordination between the different system layers• Address concerns at one or two system levels• Make assumptions about adaptations at other system levels (lack awareness)
Device Centric • Cannot exploit global system knowledge (e.g. congestion, mobility, location)• Reactive Adaptations
Lack of generalized framework
ServerWired
Network Proxy WirelessNetwork
Client
Illustration
Client
Client
Client
congestion
What if new applications are started or residual
battery energy changes?
Power-aware middlewareA power-cognizant distributed middleware framework can
o dynamically adapt to global changeso co-ordinate techniques at different system levels o maximize the utility (application QoS, power savings) of a low-power device.o study and evaluate cross layer adaptation techniques for performance vs. quality vs. power tradeoffs for mobile handheld devices.
Wide Area Network
Wireless Network
Low-powermobile device
proxy
Use a Proxy-Based Architecture
Execute Remote Tasks
Caching Compression
State Monitor
Traffic Shaping
Compositing
Transcoding
server
Use proxy to dynamically adapt to
global changes
Coordinate techniqueson device
Power-Aware Adaptive Middleware
Architecture
Architecture(cpu, memory)Network
Operating System
Middleware
Distributed Adaptation Cross-Layer AdaptationAppl. specific Adaptation
• DYNAMO SYSTEMMohapatra et. al (ICN 05, ITCC 05, ACM Middleware 04, DATE 04, ICDCS 03, MWCN 03, ACM MM 03, RTAS/RTSS Workshops 03, Estimedia 03, CIPC 03, ICDCS 01)
Distributed Adaptation Cross-Layer Adaptation
Power-Aware API
User/Application
Quality of Service Application/user feedback
Cross-Layer Approach
device
Architecture
Operating System
Distributed Middleware
User/Application
LOCALCROSS LAYER ADAPTATION
1. Expose “state” information to other layers
2. Design strategies at each layer to dynamically adapt to changes at other layers
Proxy
network
Middleware
GLOBAL (End-to-End)PROXY BASED ADAPTATION
1. Design end-to-end adaptations that can exploit global state (network noise, mobility patterns, device state etc.)2. Use control information to notifymobile device of adaptations3. Adapt strategies on device
Energy-Sensitive Video Transcoding for Mobile devices
► We conducted a survey to subjectively assess human perception of video quality on handhelds.► Hard to programmatically identify video quality parameters
► We identified 8 perceptible video quality levels that produced noticeable difference in power consumption (Compaq iPaq 3600)
5.38 W3.88 WQSIF, 20fps,100kbpsQ8 (Terrible)
5.5 W3.95 WQSIF, 20fps, 150KbpsQ7 (Bad)
5.63 W4.06 WHSIF, 24fps, 150KbpsQ6(Poor)
5.73 W4.15 WHSIF, 24fps, 200KbpsQ5 (Fair)
5.81 W4.24 WHSIF, 24fps, 350KbpsQ4 (Good)
5.86 W4.31 WSIF, 25fps, 350KbpsQ3 (Very Good)
5.99 W4.37 WSIF, 25fps, 450KbpsQ2 (Excellent)
6.07 W4.42 WSIF, 30fps, 650KbpsQ1 (Like original)
Avg. Power (Linux)
Avg. Power (Windows CE)
Video transformation parameters
QUALITY
VIDEO TRANSCODING PARAMETERS
Quality/Power Matrix for COMPAQ IPAQ 3600 ( Grand Theft Auto Action Video Sequence)
Energy-Sensitive Video Transcoding: Visual Comparison
Parameters varying: frame size, bit-rate, frame rate• Profile power for each quality• Optimize system for each quality
Quality 1(like original)
Avg. Power : 6.24 W
Quality 4(good)
Avg. Power : 5.81 W
Quality 7(bad)
Avg. Power : 5.41 W
Energy-Aware Video Transcoding
Residual Energy (ERES)User defined Quality (Qthreshold)
ProxyMobileDevice
QSTREAM = Max(Qi) such that PSTREAM * T < ERES
QSTREAM > QTHRESHOLDQSTREAM = Video streamed by proxy
Qthreshold = Threshold quality level acceptable to the userPSTREAM = Avg. Power consumption for video playbackERES = Residual Energy of deviceT = Time of playback
Video Stream (QSTREAM)
0
2
4
6
8
10
12
Q1 Q2 Q3 Q4 Q5 Q6
No
rmal
ized
En
erg
y sa
ved
(%
)
Energy-Aware Application Adaptation
CPU NICLinux OS
Dynamo Middleware
DISPLAY
NIC
CPU
Mobile Device
Dynamo Middleware
Proxy
Wireless Network
Wireless Network
QoS
Monitor
Application
UserProfile
Negotiation
CommunicationE-Q profileTranscoder
Network Card Optimization
Wireless NIC cards can operate in various power modes Avg. power consumption in sleep mode (0.184 W) whereas idle/receive modes
consume (1.34/1.435 W) respectively. NIC can be transitioned to sleep mode (high energy savings) Packets can get lost (quality drop)
Adaptation Proxy buffers video and sends it in bursts to the device Control info added - when device should wake up
Allows for long sleep intervals of the network card on device
Limitations Large bursts can result in high packet loss rates Access point and mobile device buffering limitations
Adaptation at Proxy
Residual Energy, Quality threshold+ Noise (SL), Buffer capacity (Bf), Decode rate (Fd)
ProxyMobileDevice
Video Stream (QSTREAM)# of frames, buffer size, quality
QSTREAM = Max(Qi) such that PSTREAM * T < ERES
QSTREAM > QTHRESHOLD
Set local buffer size based on noise level (empirical)Fix quality level (Qi) to be streamed (QSTREAM ... QTHRESHOLD)
Let N = number of transcoded frames in local buffer
Burst Size (I) = N / Fd
Send next burst after I seconds.
Adaptation at Mobile Device
ProxyMobileDevice
Video Stream (QSTREAM)
# of frames (N), buffer size, quality, σ
Burst Size (I) = N / Fd
Worst case transmission delay of burst (D) = σ x TAP
Therefore total sleep time (δ) for NIC at the device δ = I – D + γ. DEtoE
total number of packets at the Access Point
Psaved ≥ δ x (PIDLE - PSLEEP)
Switch network card to “sleep” mode for δ seconds after receiving video
Network Card Adaptation in Dynamo
CPU NICLinux OS
Dynamo Middleware
DISPLAY
NIC
CPU
Mobile Device
Dynamo Middleware
Proxy
Wireless Network
Wireless Network
QoS
Monitor
Application
UserProfile
Negotiation
Communication
NIC adaptation
Network Transmission
E-Q profile
Transcoder
Network Energy Savings NIC
CPUBacklight
399 FramesEnergy Savings – 50% - 75% (over no optimization)Frames Lost – < 5%
1738 FramesEnergy Savings – 35% - 57% (over no optimization)Frames Lost – < 12%
2924 FramesEnergy Savings – 25% - 45% (over no optimization)Frames Lost – < 10%
Backlight Compensation
Adaptation1) Enhance luminance of video frames at proxy
2) Dim backlight on the device to compensate luminance enhancement
Problems Technique to achieve the suitable (optimal) backlight dimming factor Reduce flicker induced by frequent backlight switching
Backlight AdaptationBacklightLevel
Quality(PSNR)
PowerSavings
Fair 149 30.17 41.8%Good 162 34.28 36.7%Excellent 205 42.31 27.3%
BacklightLevel
PowerSavings
Fair 80 44.8%Good 125 39.7%Excellent 186 30.3%
BacklightLevel
PowerSavings
Fair 172 34.8%Good 162 27.7%Excellent 205 21.4%
Backlight Adaptation in Dynamo
Residual Energy, Quality threshold+ Noise (SL), Buffer capacity (Bf), Decode rate (Fd)
ProxyMobileDevice
Video Stream (QSTREAM)# of frames, buffer size, quality+ Backlight Setting
Stream luminance compensated video
Use backlight quantization table to
set backlight
Backlight Adaptation using Dynamo
CPU NICLinux OS
Dynamo Middleware
DISPLAY
NIC
CPU
Mobile Device
Dynamo Middleware
Proxy
Wireless Network
Wireless Network
QoS
Monitor
Application
UserProfile
Negotiation
Communication
NIC adaptor
Network TransmissionE-Q profile
Transcoder
Backlightadaptor
B/L Scaling
NIC
CPUBacklight
Architecture/CPU Adaptation DVS idea: trade off processor speed for power MPEG frame decoding – good candidate
Frame decoding takes less than the frame delay Decoding time – depends on frame type: I, P, B
I B B B B PP
MPEG Stream
0 time
Vo
ltag
e
FdD
with DVS
no DVS
CPU Adaptation using Dynamo
Residual Energy, Quality threshold+ Noise (SL), Buffer capacity (Bf), Decode rate (Fd)
ProxyMobileDevice
Video Stream (QSTREAM)# of frames, buffer size, qualityBacklight Setting + Profiled WCET, BCET, Avg. Execution time
• Middleware communicates execution characteristics to scheduler• Scheduler can now dynamically re-compute slowdown parameters for the new video quality
• Determine video quality • Use a rule base of profiled data to send execution parameters to the mobile device
Linux OS
CPU Adaptation
scheduler
Dynamo Middleware
DISPLAY
NIC
CPU
Mobile Device
Dynamo Middleware
Proxy
Wireless Network
Wireless Network
QoS
Monitor
Application
UserProfile
Negotiation
Communication
NIC adaptor
Network Transmission
Backlightadaptation
CPUadaptation
Rule BaseE-Q profile
B/L ScalingTranscoder
Overall Energy Savings
Network (37.7%)
Display (26.9%)
CPU (27.2%)
Other (8.2%)
Other (8.2%)
Network (15.09%)
NIC Savings (22.64%)
Display (14.8%)
DisplaySavings (12.1%)
CPU (13.6%)
CPU Savings(13.6%)
Energy Distribution(Before Optimization)
Energy Distribution(After Optimization)
Note: These are avg. energy savings
Total energy savings ~ 48% (for a medium action video clip called Foreman)
After Cross-LayerOptimizations
• Avg. Battery lifetime of the mobile device increases by approximately 2 times its original lifetime under similar load (i.e. battery power consumption).
Implications for a commercial PDA
Secure Mobile Multimedia – Energy Implications
Mobile multimedia applicationsare vulnerable to security attacksin wireless networks Significant computation for video encryption is expectedon battery-operated mobiles
InsecurenetworkSymmetric
EncryptionTechnique
Video Encoder
Secure Video Encoder
Attacks
SymmetricDecryptionTechnique
Video Decoder
Secure Video Decoder
Encoding without Encryption
Encoding with Encryption (Selective)
Encoding with Encryption (Naïve)
0
10
20
30
40
50
60
70
8090
FOREMAN.qcif NEWS.qcifVideo Clips
Me
as
ure
d E
ne
rgy
(J
ou
les
)
Negligible Energy OverheadExperimental Study
Battery-OperatedDevices
▶ Evaluate symmetric videoencryption schemes w.r.t. energy
Problem and Motivation
Reliable Mobile Multimedia – Energy Implications
Problem and Motivation
Video streams over a wireless networkData loss Error control
•Channel coding : FEC, Retransmission•Source coding: Error-resilient coding
PNSR Variation
0
6
12
18
24
30
36
1 3 5 7 9
11
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15
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19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
Frame Number
PSN
R (
dB)
PBPAIR PGOP-1 GOP-8 AIR-10
Encoding Energy Consumption (iPAQ)
0
5
10
15
20
25
foreman akiyo garden
Enco
ding
Ene
rgy
(J)
NOPBPAIRPGOP-3GOP-3AIR-24
0 0.2 0.4 0.6 0.8 10
0.3
0.60.9
0
5
10
15
20
25
Enco
ding
En
ergy
(J)
PLR
Intra_Th
Encoding Energy Consumption (iPAQ)
0-5 5-10 10-15 15-20 20-25
Energy efficiency Fast recovery Flexibility
Experimental Study
Probability Based Partial intra-coding trade-offs among error resiliency, encoding efficiency, energy consumption
Most energy consuming operation Avoiding ME can improve energy profile at the cost of encoding efficiency High impact on image quality
Making ME algorithm robust against packet loss can improve error resiliency
Idea: ME (Motion Estimation) is..
Multiplexing,Packetizing& ChannelEncoding
ME DCT Q VLC
Video Encoder
RawVideo
LossyNetwork
Mobile Mobile Grid/Cloud Grid/Cloud Computing Computing
Dealing with mobility
Proxy must be close to device for effectiveness Idea – use a network of proxies and identify how to
use the proxy network for multiple moving devices…Where to obtain proxies
Fixed backbone infrastructure (Akamai)– Could be effective, but proprietary
Grid infrastructure (MAPGRID)– Open access, but intermittently available
Cloud infrastructure (Charisma)– Open access, limited ability to tailor use of proprietary
backbone resources
Observations (I) – Proxy-based Techniques
Network proxies support for mobile applications, e.g.: Proxy caching:
[Hadjiefthymiades WWW01], [Xu TKDE04],[Wang TM 07], etc.
Proxy-based content transcoding: [Shenoy, MMCN03] [Change EUROMICRO Journal 07], etc.
Offloading tasks: [Kejariwal, Trans VLSI 06], [Mohapatra, ACM 2003],[ Li, NOSSDAV 2003], etc.
► Grid/Cloud infrastructure support for implementing proxy-based solutions, e.g: using grid machines as proxies for mobile applications [Phan MobiCom 02]
► Online Server or third party Storage► e.g. iPhone 3G MobileMe keeps all user information in the “cloud”
“
Observations (II)- Context Awareness and Predictability
Importance of Context information, e.g. Enabling mobile applications:
Location-based services
Achieving service flexibility and adaptability: Layered video transcoding [Shanableh Transaction of Multimedia 05], etc.
Improving system performance: Mobility-aware data allocation (increasing cache hit ratio) [Peng ICPP00], etc.
Predictability and Patterns of Context Exploited for improving handoff performance, reducing task failure rate and dynamic
resource allocation, etc Mobility prediction [Song, IEEE Transaction on Mobile Computing 06] Mobility models [ MCNett SIGMOBILE review 05] [Hong MSWiM99] Characterizing resource availability in desktop grids [Kondo FGCS 07]
MAPGrid: A New ParadigmGrid Enabled Mobile Applications
VS 3
VS 2
VS 1
Broker
Research Challenges Resource Discovery
Application aspect Ensure user QoS satisfaction
System View Define an “optimal” grid resource allocation
• User mobility• resource heterogeneity• Grid intermittent availability
Adapt to changing context • User mobility, device energy, and proxy availability
► Data Placement Application aspect
► Guarantee data availability► Improve information retrieval efficiency with user mobility
System View► Address both short term on-demand caching and long term data placement ► Make caching or data replication at finer granularity► Balance availability efficiency tradeoff
MapGrid
Uses (idle) grid computing resources as the intermediate node cache proxy for mobile applications.
Resources on grid (storage and computation) are intermittently available. MapGrid uses the interval tree data structure for storing the information
about available resources on grid. Mobility pattern has been used to optimal data replication policy in
MapGrid.
56
Mobile Mobile ClientClient
BrokerBroker
Mobile ClientMobile ClientMonitoringMonitoring
Directory Directory ServiceService
Request Request SchedulerScheduler
Resource Resource ReservationReservation
Placement Placement ManagementManagement
Tertiary Tertiary StorageStorage
Volunteer Servers(Grids)
Grid Grid MonitoringMonitoring
MapGrid Middleware Service ArchitectureMapGrid Middleware Service Architecture
ReqReq
InfoInfo
Moving_ProfileMoving_Profile
ReqReqReq*Req*
Req#Req#
Req@Req@
DataData
1. Resource Discovery2. Schedule Planning
1. Resource Discovery2. Schedule Planning
Reports changes in grid resources
Reports changes in grid resources
Admission ControlAdmission Control
Contains information about available resources on Grid
and Clients
Contains information about available resources on Grid
and Clients
Replace the data to grid resources
Replace the data to grid resources
Mobility Pattern detectionMobility Pattern detection
57
MapGrid Request: R(VID, itinerary) where VID is the video and itinerary is mobility
information. Grid Loading Factor:
Grid Factor:
10 j. gridon resource available to
resource requested of ratio theis X where
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j
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),(),(
),(),(
RjDisttRLF
tjtyAvailabilitRGf
jj
If grid j is available at time t equal 1 else 0.
If grid j is available at time t equal 1 else 0.
Distance of grid J from request R.
Distance of grid J from request R.
58
Grid Resource Discovery for Mobile Services
Partition Service Period
All chunks have VS assignments?
Proxy (VS) Selection
Y
N
• apply mobility or location information• choose the number of chunks (partition)
• decide the size of each chunk
• choose available VSs• choose lighter loaded VSs
• choose “closer” VSs• Satisfy different QoS reqirements on service
response time, service continuity…
Rescheduling if dynamic changes happen
R: < IDR: < IDRR , T , Q , T , QL L , Q, QHH, , , , EERR , itinerary> , itinerary>
• Rescheduling when device has no enough residual energy for the current QoS level
• Rescheduling when grid node fails
Grid Resource Discovery for Mobile Services
Partition Service Period
All chunks have VS assignments?
Volunteer Server Allocation
Y
N
Problem:► Determine number of partitions and size of
each partitionMachine learning approach using mobility info
Rescheduling if dynamic changes happen
Grid Resource Discovery for Mobile Services
Partition Service Period
All chunks have VS assignments?
Volunteer Server Allocation
Y
N
Rescheduling if dynamic changes happen
Optimizing grid resource selection► Factors: Load, location, availability,
capacity ► Achieve system-wide load balancing ► Maximize the number of accepted
servicesGraph theoretic approach
Grid Service Discovery (Multimedia Application)
P2-T2
0
1000
2000
3000
4000
5000
6000
7000
8000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
time (hours)
num
be
r o
f re
ject
ion
s
IS-C-SS
IS-C-MS
DS-C-SS
DS-C-MS
DS-D-SS
DS-D-MS
P2: Deterministic data placementP2: Deterministic data placementT2: Random VS availabilityT2: Random VS availability
Collections of services support by Collections of services support by grid significantly increases grid significantly increases
request acceptance and request acceptance and Completion ratios Completion ratios
Uncompleted requests when VSs become unavailable simultaneously
1036
6982
116 118
146168
190
224 231
266
0 0 0 0 0 0 0 0 11
193
104
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
number of VSs that become unavailable simultaneously
nu
mb
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f re
qu
ests
th
at
can
no
t b
e co
mp
lete
d no adaptation
with adaptation
MAPGrid Prototype
Client
Volunteer Servers
BrokerGlobus Toolkit
The Volunteer Server can join and disjoin the grid dynamically .
Failure detection for both client and VSs using Globus life time management control.
Data
Information Services
Life time control
GridFTP
JMF (Java Media Framework)
JADEHttp
Client can specify QoS requirements via friendly GUI
Mobile Cloud Mobile Cloud ComputingComputing
What is Cloud Computing
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services).
It can be rapidly provisioned and released with minimal management effort.
It provides high level abstraction of computation and storage model.
On-Demand Self Service, Heterogeneous Access,Resource Pooling.
Essential Characteristics
On-Demand Self Service: A consumer can unilaterally provision computing
capabilities, automatically without requiring human interaction with each service’s provider.
Heterogeneous Access: Capabilities are available over the network and accessed
through standard mechanisms that promote use by heterogeneous thin or thick client platforms.
66
Resource Pooling: The provider’s computing resources are pooled to serve multiple
consumers using a multi-tenant model. Different physical and virtual resources dynamically assigned and
reassigned according to consumer demand.
Measured Service: Cloud systems automatically control and optimize resources used
by leveraging a metering capability at some level of abstraction appropriate to the type of service.
It will provide an analyzable and predictable computing platform.
67
Essential Characteristics (cont.)
Service Models Cloud Software as a Service (SaaS):
The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure.
The applications are accessible from various client devices such as a web browser (e.g., web-based email).
The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage,…
Examples: Caspio, Google Apps, Salesforce, Nivio, Learn.com.
68
Cloud Platform as a Service (PaaS): The capability provided to the consumer is to deploy onto the
cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider.
The consumer does not manage or control the underlying cloud infrastructure.
Consumer has control over the deployed applications and possibly application hosting environment configurations.
Examples: Windows Azure, Google App.
69
Service Models (cont.)
Cloud Infrastructure as a Service (IaaS): The capability provided to the consumer is to provision
processing, storage, networks, and other fundamental computing resources.
The consumer is able to deploy and run arbitrary software, which can include operating systems and applications.
The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Examples: Amazon EC2, GoGrid, iland, Rackspace Cloud Servers, ReliaCloud.
70
Service Models (cont.)
Service Model at a glance: Picture From http://en.wikipedia.org/wiki/File:Cloud_Computing_Stack.svg
71
Service Models (cont.)
Deployment Models Private Cloud:
The cloud is operated solely for an organization. It may be managed by the organization or a third party and may exist on premise or off premise.
Community Cloud: The cloud infrastructure is shared by several organizations and supports a
specific community that has shared concerns. It may be managed by the organizations or a third party and may exist on
premise or off premise.
Public Cloud: The cloud infrastructure is made available to the general public or a large
industry group and it is owned by an organization selling cloud services.
Hybrid cloud: The cloud infrastructure is a composition of two or more clouds (private,
community, or public).
72
Service Model at a glance: Picture From http://en.wikipedia.org/wiki/File:Cloud_computing_types.svg
73
Deployment Models (cont.)
Evolution of Computing Environment: Evolution of Computing Environment: [Satyanarayanan_2001] : [Satyanarayanan_2001] :
Toward Pervasive Computing environmentToward Pervasive Computing environment74
Cloud Cloud Computing Computing
PromisesPromises
Towards Pervasive Computing
Cloudlet It has been shown that a one hop connection from mobile
device to internet is not efficient. Humans are sensitive to the current delay in clouds
latency is unlikely to improve significantly security and firewall it is unlikely that latency improves (although
increase in bandwidth).
Cloudlet Between mobile devices and cloud pools (Infrastructure).
A cloudlet (micro edition of cloudmicro edition of cloud)only contains soft states such as cache copies of data or code.
75
Cloudlets are as the infrastructure for mobile cloud computing. From: [Satyanarayanan_2009]
76
The prototype based on this systems is implemented in CMU and called Kimberley [Satyanarayanan_2009] .
Cloudlet (cont.)
77
Dynamic Task Reconfiguration
How can we support dynamic task/service reconfiguration to achieve energy gains (i.e. migrate computationally expensive tasks from the device to the proxy and use the results locally)
Approach:Identify “energy intensive” components that can be dynamically migrated to a proxy► What to reconfigure?
computation/communication characteristics of tasks (use Profiling) Current residual power of the device
► When to reconfigure? Identify set of policies that dictate how often or under what conditions reconfigurations should be initiated
78
Dynamo: Dynamic Task Reconfiguration
►Cast the distribution problem as a “Source Parametric Flow Network”.
►Use Current residual energy at device to make the flow graph “source parametric”
How to maintain an optimized component distribution under dynamic device power conditions?
Problem
Solution
79
Source Parametric Flow Graph
D P
device proxy
Rd
Rp
Runtime on Device
Runtime on Proxy
M1
M2
Mn
A1
Ai = Energy cost of
executing task Mi at the
proxy.
infinite
Ap
A2
An
B1
Bi = Energy cost of
executing task Mi at the device.
Bd
infin
ite
B2
Bn
X1
Yn
• If Mi is executing on the Device, then Xi = communication costs in energy terms
• If Mi is executing on the Proxy, then Yi = communication costs in energy terms
Xn
Y1
Y2
X2
80
High Level Algorithm► The minimum cut of the graph determines which
components can be moved to the proxy.► Can be solved using a modified FIFO Pre-Flow push
algorithm. ► Complexity = O(n3)
FOR (each reconfiguration interval) DOBEGIN
o update list of components/residual energy on device
o generate network flow grapho determine component partitioning (min cut)
o IF (new partition) o reconfigure components between device
and proxyEND
AlfredO Platform (ETH Zurich)
SaaS distribution model for physical services (MW 2008, 2009) Mobile Phones as universal interfaces to cloud applications Supports partition and distribution of modularized applications in a client-server setup
OSGI-based (Open Services Gateway Initiative) Focus on presentation and logic modules
81
Virtual ticket machineTurn the phone into an
interface for theticket machine
Home 3D Planner
AlfredO architecture and applications (MW 2009)
Component Based Architecture Supports optimal task migration between server and mobile client [Middleware 2009].
Generate an application graph and determine a cut (tasks done on server side and remaining will be done on mobile side) to meet the required optimization issues.
Premise: Modularized application and Model of resource consumption and dependencies among application modules
Goal: Partition the application between phone and server based on different criteria (bandwidth, memory)
83
S1 S2 S3
S4
S5S7
S6S6
S8
S1 S2 S3
S4
S5S7
S6
S8
Mobile CutMobile CutMobile CutMobile CutMobile CutMobile CutMobile CutMobile Cut
S1 S2 S3
S4
S5S7
S6
S8
Original PlanOriginal PlanAlfredO
AlfredO (MW 2009)
Just like ISPs cloud computing could be considered in different Tiers.
The backbone Cloud which usually large organization such as Google, Yahoo, Microsoft support them.
The second tier is edge Cloud which is near end user.
Tier 2-Cloud (Edge Cloud)
Tier 1-Cloud (Backbone Clouds,. Amazon EC2, Azure,… )
CHARISMA: Tiered Clouds
Mobile ClientMobile Client Middleware(Broker)Middleware(Broker) Cloudlet and Cloud Cloudlet and Cloud PoolPool
Cloud Service Registry and Discovery
AdmissionControl
Mobile Profile Monitoring
Qos Monitoring , Service Discovery
Scheduler
Mobile Client
Cloudlet Pools
Mobile Profile Analyzer
Qos Analyzer
Cloud Pools
Qos Monitoring
87
Middleware BlocksMiddleware Blocks FunctionalitiesFunctionalities
Admission Control Accepts or rejects mobile client requests according to the resources available and mobile profile (it could be power-aware, resource –aware and…. Policies, Maybe Game Theory could be used for optimal admission policy).
Mobile Profile Monitoring
Monitors mobile profile.
Mobile Profile Analyzer
Analyzes mobile profile specially power level, location pattern,… for optimal scheduling.
Scheduler It schedules the accepted requests and design a plan according to the negotiations done with Cloudlets (Schedule Queue).
Qos Monitoring, Service Discovery (Cloud and Cloudlets)
It discovers cloudlets (maybe based on ontology and semantics) in the area (like UDDI), its Qos during service from mobile client. It also registers its services and middleware business on UDDI for accessing mobile clients.
Qos Analyzer It is analyzing the Cloudlets services and make a list of suitable cloudlets and policy of admission.
Mobile Client Block Functionalities
Qos Monitoring Reports the Middleware about the Qos of the cloudlets.
Barcode Reader: Sample Application
Simple barcode reader service has been implemented. In this scenario the user takes a picture of the barcode for
getting some information about the object, for example the cheapest price location.
The picture is sent to cloudlet for processing. The cloudlet extracts the information and queries Amazon
or other clouds for price and returns back to the user
88
CloudCloudCloudletCloudlet
89
Cloud(Amazon)Cloud(Amazon)
CloudletCloudlet
Cloud(Yahoo)Cloud(Yahoo)
Yahoo Cloud (Flicker) as a Cache.
90
Cloud computing
Remote access to distributed and shared cluster resources Potentially owned by someone else (e.g. Amazon, Google, …)
Users rent a small fraction of vast resource pools• Advertised service-level-agreements (SLAs)
• Resources are opaque and isolated
Offer high availability, fault tolerance, and extreme scale
Relies on OS, network, and storage virtualization/isolation
SLAs
Web Services
Virtualization
Ad Hoc
Future: Interoperable NetworkingHeterogeneous access technologies available
End devices equipped with multiple radio interfaces
Applications are run over the most efficient access
networks
802.11b
802.11a
Bluetooth
GPRS
UMTS
Wi-Fi
Cellular......
Reliable Secure QoS over
multiple access networks
Always Best Connected: Solution Components
access discovery
access selection
AAA support
mobility management
content adaptation
profile handling
• what accesses are currently available?• what are their bandwidth and delay?
• what access to choose; what is “best”?• user/network-based solution• one or multiple accesses in parallel
• session continuity, session transfer• support for real-time services
• authentication, authorization, accounting• accesses, services... single logon
• applications adapting to access & device
• personal profiles for ABC• access selection & content adaptation
Access Selection: Benefits Continual connectivity
Uninterrupted Internet access even when some of the access networks are unavailable
High throughput/QoS Connect via the access network with highest data rate and/or
shortest delay Broader bandwidth
Aggregate the bandwidth offered by multiple access networks Energy Conservation
Different power consumption properties of access networks provide chances for better energy efficiency.
Enhanced Security Transmitting sensitive data on multiple access networks
potentially provides higher security.
Access Selection: Research Challenges
Non-predetermined traffic flows start/terminate at discrete time points.
Access networks’ bandwidth and delay might fluctuate.
How to select appropriate access networks for application traffic flows?
QoS requirements Energy consumption User/application
specified access preferences
Dynamicity Multiple Constraints
Groupware apps
Multiparty collaborationsSegutiReliability + Security
Summary
New Generation of Pervasive and Mobile Computing Applications with diverse content Need significant enhancements in system and software support
Distributed mobile middleware can significantly improve performance of next generation mobile applications. Open interfaces will yield much more optimized solutions for distributed
mobile devices.
The use of cross-layer design architectures is inevitable for future mobile systems, if we are to realize cumulative benefits of independent research at each system layer.
Wireless Networking 103
Networks for Mobile Computing
Mobile Internet Mobile IP (MIP)
Introduction (goals, architecture, protocol, examples)
MIP messages, encapsulations MIP issues: triangular routing,
reverse tunneling, MobileIPv6
Micro-mobility Protocols Cellular IP HMIP MobileNAT
Summary
Mobile Ad Hoc Networks Introduction: difference from
Wireless Sensor Networks (WSNs)
Routing Protocols WRP AODV DSR Improvements:
• interference levels
• topology control/management
Summary
Wireless Networking 104
Motivation for Mobile IP
Internet routing based on IP destination address and network prefix change of physical subnet implies that the mobile node should
change its IP address to have a topologically correct address
Challenges – 1) find the MS, 2) avoid disconnection DNS? Updates take to long time Per-host routing? Too many entries in the RT.
Solution: Mobile IP is an open standard, defined by the Internet Engineering Task Force (IETF) RFC 2002,
that allows users to keep the same IP address, stay connected, and maintain ongoing applications while roaming between IP networks
Network supported Mobile IP (tunneling in IPv4)
Host supported Mobile IPv6 (Routing extension header)
Wireless Networking 105
Requirements to Mobile IP (RFC 3344)
Transparency Mobile keeps its IP address when changing subnets Communication continues after interruption of link possible
Compatibility no changes to current end-systems and routers required - tunneling no changes to fixed systems – HA-FA tunneling
Security authentication of all registration messages – MIP messages
Efficiency and scalability only a few additional messages to the mobile system required (connection typically via a
low bandwidth radio link) – MIP messaging Global support of a large number of mobile systems in the whole Internet – HA for MN
Wireless Networking 106
Architectural Components1. End system elements
Mobile Node (MN) Correspondent Node (CN): communication partner
2. Network elements Home Agent (HA): System in the home network of the MN, typically a router
Registers the location of the MN, tunnels IP datagrams to the COA Foreign Agent (FA): the default router for MN
Forwards the tunneled datagrams to the MN Can be collocated with MN
Care-of Address (COA): address of the current tunnel end-point for the MN
Can be associated with the MN or FA.
Wireless Networking 107
Mobile IP Protocol Agent Advertisement using ICMP
HA and FA periodically send advertisement messages into their physical subnets in ICMP messages
MN listens to these messages and detects, if it is in the home or a foreign network (standard case for home network)
MN reads a COA from the FA advertisement messages MN may send out router (agent) solicitation.
Registering the COA using UDP binding for the mobile node – tuple (home address, COA,
registration lifetime, authentication) binding update sent by MN to HA for remote redirect.
Traffic forwarding using Tunneling HA advertises the IP address of the MN (as for fixed
systems) via proxy ARP routers adjust their entries, these are stable for a longer
time (HA responsible for a MN over a longer period of time)
packets to the MN are sent to the HA, which tunnels to the COA.
independent of changes in COA/FA
Wireless Networking 108
Example network
mobile end-systemInternet
router
router
router
end-system
FA
HA
MN
home network
foreign network
(physical home networkfor the MN)
(current physical network for the MN)
CN
Wireless Networking 109
Data transfer to the mobile system
Internet
sender
FA
HA
MN
home network
foreignnetwork
receiver
1
2
3
1. Sender sends to the IP address of MN, HA intercepts packet (via proxy ARP)2. HA tunnels packet to COA, here FA,
by encapsulation3. FA forwards the packet to the MN
CN
Wireless Networking 110
Data transfer from the mobile system
Internet
receiver
FA
HA
MN
home network
foreignnetwork
sender
1
1. Sender sends to the IP address of the receiver as usual,
FA works as default routerCN
Wireless Networking 111
Agenda
Mobile Internet Mobile IP (MIP)
Introduction (goals, architecture, protocol, examples)
MIP messages, encapsulations MIP operations: triangular
routing, reverse tunneling, MobileIPv6
Micro-mobility Protocols Cellular IP HMIP MobileNAT
Summary
Mobile Ad Hoc Networks Introduction: difference from
Wireless Sensor Networks (WSNs)
Routing Protocols WRP AODV DSR Improvements:
• interference levels
• topology control/management
Summary
Wireless Networking 112
Mobile Ad-hoc Networks (MANET)
Ad-hoc network: A collection of wireless mobile hosts forming a
temporary network without the aid of any established infrastructure or centralized administration.
Significant differences to existing wired networks: Wireless Self-starting No administrator Cannot assume, that every computer is within
communication range of every other computer Possibly quite dynamic topology of interconnections
Traffic types: unicast/multicast/anycast/geocast
Wireless Networking 113
Routing in MANET
Routing assumptions for unicast traffic Flat topology assumption
Proactive: DSDV, TORA, WRPReactive: AODV, DSR, STAR
Hierarchical topology assumptionClustering: CBRP, PATM
Geographic assumptionLocation aided routing: LAR, GeoCast
Wireless Networking 114
Classification of Routing Protocols for MANETS
Unicast-Routing Protocol for MANET (Topology-based)
Table-Driven/Proactive
Hybrid On-Demand /Reactive
Clusterbased/Hierarchical
DistanceVector
Link-State
ZRP DSRAODVTORA
LANMARCEDAR
DSDV OLSRTBRPF
FSRSTAR
MANET: Mobile Ad hoc Network
(IETF working group)
Wireless Networking 115
Desired Properties of Ad Hoc Routing Protocols
Distributed Bandwidth efficient
Reduce control traffic/overhead Battery efficient Fast route convergence Correct: loop free
Reduce overhead Unidirectional Link Support
Wireless Networking 116
Performance Metrics of Ad Hoc Routing Protocols
Maximize end-to-end throughput delivery ratio load balancing (congestion)
Minimize end-to-end delay packet loss shortest path/minimum hop (route length) overhead (bandwidth) energy consumption
Wireless Networking 117
Mobile Ad hoc Networks (MANET) vs. Sensor Networks
MANET SensorNet
applications meeting, grp collaboration smart building, habitat monitoring
comm. address-centric comm. data centric comm.
topology peer-to-peer sensors base & peer-to-peer
traffic random periodic, synchronous
platform laptops, PDAs motes: more resource constrained
scale 10’s >1000: larger scale and more redundancy
mobility slow (meeting) ~ fast (cars): focus on mobility
slow (habitat) ~ fast: less focus on mobility so far
similarity No infrastructure, multi-hop, wireless networks
Wireless Networking 118
Address Centric Routing (AC)Temperature Reading
(source 2)Temperature Reading(source 1)
Give Me The Average Temperature?( sink )
source 1
source 1
source 2
source 2
source 2
B
Z
Wireless Networking 119
Data Centric Routing (DC)Temperature Reading
(source 2)Temperature Reading(source 1)
Give Me The Average Temperature?( sink )
source 1
source 2
source 2
source 1 & 2
B
Z
Wireless Networking 120
Distance-Vector routing
Each node maintains a routing table containing list of all available destinations number distance to each each destination next hop to reach a destination
The succession of next hops leads to a destination Each node periodically broadcasts its current estimate of
the shortest distance to each available destination to all of its neighbors
Typical representative: Distributed Bellman-Ford (DBF)
Wireless Networking 121
AODV (Ad Hoc On-Demand Distance Vector)
AODV is based on the DSDV (Destination-Sequenced Distance Vector) algorithm Distance vector Sequence numbers controlled by the destination.
Creation of routes on a demand basis – traffic reactive Nodes that are not on a selected path do not maintain
routing information or participate in routing table exchanges!
Goal: Minimize broadcast overhead and transmission latency
Wireless Networking 122
Route Requests from S to D in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y
Represents a node that has received RREQ for D from S
M
N
L
Wireless Networking 123
Route Requests from S to D in AODV
B
A
S E
F
H
J
D
C
G
IK
Represents transmission of RREQ
Z
YBroadcast transmission
M
N
L
Wireless Networking 124
Route Requests from S to D in AODV
B
A
S E
F
H
J
D
C
G
IK
Represents links on Reverse Path
Z
Y
M
N
L
Wireless Networking 125
Reverse Path Setup from S to D in AODV
B
A
S E
F
H
J
D
C
G
IK
• Node C receives RREQ from G and H, but does not forward it again, because node C has already forwarded RREQ once
Z
Y
M
N
L
Wireless Networking 126
Reverse Path Setup from S to D in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
N
L
Wireless Networking 127
Reverse Path Setup in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y• Node D does not forward RREQ, because node D is the intended target of the RREQ
M
N
L
Wireless Networking 128
Route Reply from D to S in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y
Represents links on path taken by RREP
M
N
L
Wireless Networking 129
Route Reply in AODV
Intermediate node may also send a Route Reply (RREP) provided that it knows a more recent path than the one previously known to sender S To determine whether the path known to an intermediate
node is more recent, destination sequence numbers are used
The likelihood that an intermediate node will send a RREP not as high as DSR An intermediate node which knows a route, but with a
smaller sequence number, cannot send Route Reply
Wireless Networking 130
Forward Path Setup in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
N
L
Forward links are setup when RREP travels alongthe reverse path
Represents a link on the forward path
Wireless Networking 131
Data Delivery in AODV
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
N
L
Routing table entries used to forward data packet.Route is not included in packet header.
DATA
Wireless Networking 132
AODV Key Advantages
“Partial” routing tables are constructed reactively Entries are updated only when a node sends to another unreachable
node No periodic updates Node not on active paths maintain no routing entries Reduce packet overhead
Routing table No source routing needed reduce bit overhead “Route caching” reduce establishment latency Sequence number loop freedom
Push link failure to relevant nodes Reduce establishment latency
Wireless Networking 133
Dynamic Source Routing (DSR) [Johnson96]
When node S wants to send a packet to node D, but does not know a route to D, node S initiates a route discovery using Route Request (RREQ)
Each node appends own identifier when forwarding RREQ
Wireless Networking 134
Route Discovery in DSR
B
A
S E
F
H
J
D
C
G
IK
Z
Y
Represents a node that has received RREQ for D from S
M
N
L
Wireless Networking 135
Route Discovery in DSR
B
A
S E
F
H
J
D
C
G
IK
Represents transmission of RREQ
Z
YBroadcast transmission
M
N
L
[S]
[X,Y] Represents list of identifiers appended to RREQ
Wireless Networking 136
Route Discovery in DSR
B
A
S E
F
H
J
D
C
G
IK
• Node H receives packet RREQ from two neighbors: potential for collision
Z
Y
M
N
L
[S,E]
[S,C]
Wireless Networking 137
Route Discovery in DSR
B
A
S E
F
H
J
D
C
G
IK
• Node C receives RREQ from G and H, but does not forward it again, because node C has already forwarded RREQ once
Z
Y
M
N
L
[S,C,G]
[S,E,F]
Wireless Networking 138
Route Discovery in DSR
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
• Nodes J and K both broadcast RREQ to node D• Caveat: Since nodes J and K are hidden from each other, their
transmissions may collide
N
L
[S,C,G,K]
[S,E,F,J]
Wireless Networking 139
Route Discovery in DSR
Destination D on receiving the first RREQ, sends a Route Reply (RREP) RREP is sent on a route obtained by reversing the
route appended to received RREQ RREP includes the route from S to D on which
RREQ was received by node D
Wireless Networking 140
Route Reply in DSR
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
N
L
RREP [S,E,F,J,D]
Represents RREP control message
Wireless Networking 141
Dynamic Source Routing (DSR)
Node S on receiving RREP, caches the route included in the RREP
When node S sends a data packet to D, the entire route is included in the packet header hence the name source routing
Intermediate nodes use the source route included in a packet to determine to whom a packet should be forwarded
Wireless Networking 142
Data Delivery in DSR
B
A
S E
F
H
J
D
C
G
IK
Z
Y
M
N
L
DATA [S,E,F,J,D]
Packet header size grows with route length
Wireless Networking 143
DSR Optimization: Route Caching
Each node caches a new route it learns by any means Through Route Request (RREQ)
When node K receives RREQ [S,C,G] destined for node D, node K learns route [K,G,C,S] to node S
Through Route Reply (RREP) When node S finds RREP [S,E,F,J,D] to node D, node S also learns route [S,E,F] to
node F When node F forwards RREP [S,E,F,J,D], node F learns route [F,J,D] to node D
Through DATA packet’s source routes When node E forwards Data [S,E,F,J,D] it learns route [E,F,J,D] to node D A node may also learn a route when it overhears Data
Problem: Stale caches may increase overheads
Wireless Networking 144
Dynamic Source Routing: Advantages
Routes maintained only between nodes who need to communicate reduces overhead of route table maintenance
Routing cache can further reduce route discovery overhead
A single route discovery may yield many routes to the destination, due to intermediate nodes replying from local caches
Wireless Networking 145
Dynamic Source Routing: Disadvantages
Packet header size grows with route length due to source routing
Flooding of route requests may potentially reach all nodes in the network
Stale caches will lead to increased overhead Common problems for both AODV and DSR
Potential collisions between route requests propagated by neighboring nodes - insertion of random delays before forwarding RREQ
Increased contention if too many route replies come back due to nodes replying using their local cache - Route Reply Storm problem