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Stojmenovic@gmail.com www.site.uottawa.ca/~ivan
Mobile Cloud, Crowd & Fog Computing,
Communications and
Sensing
Ivan Stojmenovic
Contents
1 Mobile Cloud Computing
2 Applications
3 Crowd & Fog sensing & computing
4 Vehicular cloud/crowd
5 Green Computing
0 Wireless and Cloud
2/24/2010 3
Mobile phones replacing desktop
computers for cloud access
Screen? Wireless? Computing? Sensing?
1926 Nikola Tesla: Teleautomation ‘When wireless is perfectly applied,
the whole Earth will be converted into a huge brain,
4
and the instruments
through which we shall be
able to do this will be
amazingly simple
compared with our
present telephone. A men
will be able to carry one in
his vest pocket.’ =
smartphone
handle e-mail, notepad items, contact book, photos and
documents,
automatically synchronized to iMac, iPod, iPhone and other
Apple’s terminal devices.
7
iCloud: Cloud Storage and Cloud Computing.
Mobile Cloud Computing Technologies
• Model the dependencies between
application modules, and optimize the
partitions
• Automatically allocate applications from
different levels to mobile devices and
cloud servers
• Provide solution for situations where the
latency is too high for distant cloud
resource to kick in
• Construct a mobile cloud computing
platform using cell phones
• enables smartphone applications with
distributed data and computation 10
Cloudlet CMU: Mahadev Satyanarayanan, where the latency is
too high for distant cloud resource to kick in directly.
‘data center in a box’
Ex: language translation app on the local cloudlet
Fog computing
Hyrax CMU Eugene E. Marinelli
Example: Hyrax multimedia search and sharing application,
HyraxTube, allows users to browse videos and images stored on a
network of phones and search by time, location, and quality.
Crowd Computing
Murray, Yoneki, Crowcroft, Hand, MobiHeld 2010
Combining mobile devices and social interactions to achieve large-scale distributed computation
analyze two encounter traces to place upper bound on the amount of useful computation on other devices that is possible
Task Farming
Single master process manages a queue of tasks, and distributes to ensemble of workers
Computation at node 0 is helped by nodes 1-4
Arrow: encounter
Useful computation
Wasted computation
Results need to be returned by deadline to be useful
Social Aware Task Farming
Exploit the social network formed by human interaction
master should meet a large number of other devices
Community structure: devices partitioned into groups: highly connected within, but few connections between
assign one master in each community?
accept only tasks from master in own community?
Opportunistic forwarding of results in addition to direct
Task dependencies and scheduling
Power consumption, task replication issues
COMMUNITY STRUCTURE Li,Wang,Yang,Jiang,Stojmenovic,IEEE INFOCOM 2014
Physical Proximity Community (PP Community)
Access Point Community (AP Community)
Space Crossing Community (SC Community)
Improving data forwarding (application)
17
SDN: Software Defined Networks
Emergent computing and networking paradigm
Separate control and data communication layers
Control is done at ‘centralized server’
Nodes follow communication path decided by the server
‘Centralized server’ may need distributed implementation
Biometric applications: verification and identification
e.g., find name of person
Real time forensic applications by experts at the scene
Socialize spontaneously with mobile applications (Liu, Feng, Li INFOCOM 2012)
achieve spontaneous social interaction
with other users in the same mobile application,
be they in the same living room or around the world.
eSmall talker
Champion, Yang, Zhang, Dai, Xuan, Li, TPDS 2012
Helps strangers in physical proximity to find potential small talk opportunities
each device creates a Bloom filter based on the small talk topics, e.g., hobbies
this filter will be advertised through Bluetooth’ service discovery protocol (SDP)
Multiple round Bloom filter advertising
Encoded common topic candidates
Each topic hashed into k bits of a common vector
Topic is candidate if vector from neighbors covers corresponding k bits, but some bits might be covered by union of other topics, eliminated for the next round
From Cloud to Crowd Computing
Remove cloud: computing in mobile phones
Spontaneous wireless ad hoc networks
Creation: Lacuesta, Lloret, Garcia, Penalver IEEE TPDS 2012
Authentication issues: AES symmetric encryption or Diffie-Hellman public keys
Trust issues: adding 0/1 trust value to connections
Applications: content delivery, games…
Mobile Crowdsensing
Ganti, Ye, Lei, 2011
ECG enabled mobile phone
Bluetooth to
mobile phone
iPhone 4: Camera,audio,
GPS, Accelerometer,
Gyroscope,
Compass,Proximity,
ambient light
Intel’s sensor
air quality
People-centric sensing Campbell et all 2008
Personal sensing
socialize
Public sensing
Smart city Social sensing
Best restaurant?
Crowd-Sourced Sensing and Collaboration using Twitter
Demirbas, Bayir, Akcora, Yilmaz WoWMoM 2010
Tweet: 20 char username + 140 char post field
News, alert systems (e.g. connect city residents)
Twitter can provide an ‘open’ publish-subscribe infrastructure for sensors and smart phones, allowing for data mining
Participatory sensing by volunteering smart phones
E.g. noise level mapping (with GPS) and querying
Crowd-sourcing (distributing a query to several Twitter users)
E.g. weather radar, polling for best restaurant
Social collaboration (back-and-forth interaction): e.g,. Arrange ride sharing, support group for addicts, social events…
Research Challenges
Localized analytics
Data mediation (e.g. noise elimination),
context inference (in a bus? Walking? Watching TV?)
Resource limitations
Energy, bandwidth, computation
Privacy, security, data integrity
Data perturbation (adding random noise)
Aggregate analytics
Data mining
Architecture
Unify for different applications, cooperation in sensing…
VC – Vehicular Cloud
A group of vehicles whose corporate
Computing, sensing, communication and physical resources can be coordinated and dynamically allocated to authorized users
How are VCs different from the classic clouds?
Mobility: close proximity to an event is often un-planned
pooling of the resources in support of mitigating the event must
occur spontaneously
Autonomy: for the decision of each vehicle to participate in the VC
Agility: ability of VCs to tailor the amount of shared resources to the actual needs of the situation in support of which the VC was constituted
A cloud in your parking lot
44 9/17/2014
• parking lot of a typical enterprise on a typical workday
• hundreds/thousands cars go unused for hours on end
• Why rent computational/storage resources elsewhere?
• you have them in your own backyard; they are yours to waste!
Data center at the shopping mall
45 9/17/2014
If drivers just attach to the internet by cable then malls can
• provide real data center computing services • by using the resources of the parked cars
• The shoppers cars get free parking + other perks in return
Dynamically rescheduled traffic lights
46 9/17/2014
• Reschedule traffic lights to help mitigate
congestion
• The municipality has the authority and
the code but does not have the hardware
• The cars have the
computational power but lack the authority and the code
Dynamic HoV lane designation (contraflow)
48 9/17/2014
• schedule HoV lanes in real time as required by traffic flow vehicular clouds to the rescue!
Planned evacuations
49 9/17/2014
• several inter-operating VC of vehicles involved in evacuation coordinated the emergency management center
• the emergency managers learn and upload real-time information about open gas stations, shelters, open medical
facilities etc
Network as a Service – Naas
Sending adds to the traveling public
People can subscribe to email, Internet access or location specific services in a pay-as-you-go fashion
Sharing Network Resources between Cars
Vehicles with Internet access
can be used as a network
cloud to reach thousands of
customers on the move
53
Transmission
Network interface
Computation
CPU
Memory
Sensing
GPS
Camera
Energy Consumption of Mobiles: User Side of Green MCC
Green Mobile Cloud Computing -Transmission
Significant energy cost on mobile device WiFi radio
Cellular network
Challenges Unstable wireless quality
• Various energy consumption status
Heterogeneous interfaces • Various transmission modes (PSM/CAM of wifi)
Different traffic demands • Real-time/delay-tolerant applications
Solutions Sleep during idle time by using PSM mode
Predict signal strength & traffic pattern to avoid rush hour
Send in a burst by traffic shaping
54
Green Mobile Cloud Computing - Computation
Challenges
Limited resources
• computational capability
• memory
Rely on a finite energy source
Solutions
Task out-sourcing schedule & cloud-assisted
CPU optimization
55
Task Outsourcing to The Cloud
Which can be offloaded?
High computation cost & low transfer cost
How to profile applications based on energy?
Energy state prediction
Power modeling
56
Energy Consumption Pattern on Modern Smart Phone
Tail power states NICs, sdcard and
GPS Stay at high power
state after I/O activities
Non-utilization system calls slowly change power state
Several components do not have quantitative utilization
57
Non-Utilization based Power Modeling
Tracing system calls of the applications
Accurate fine-grained energy estimation
Per-subroutine & per-thread & per process
58
Green Mobile Cloud Computing -Sensing
Challenges
High energy consumption of specific sensors
• GPS used for location-based service
59
Energy Saving of Location-based Service
Shortcomings of existing smart phones
Static use of location sensing mechanisms
Absence of use of power-efficient sensors
Lack of cooperation among multiple LBAs
60
Energy Saving of Location-based Service
Solutions
Substitution
• To make use of alternative location-sensing mechanism (e.g.,
cell-based location tracking, interpolation according to history..)
Suppression
• Use less power-intensive sensors to suppress unnecessary
GPS sensing (accelerometer, wireless data)
Piggybacking
• Synchronizes the location sensing requests from multiple
running LBAs (location based applications) e.g.,
– New LBA may delay GPS registration until existing LBA does it
61
Conclusion
We expect mobile cloud computing to see a phenomenal adoption rate and penetration of the IT market
Cloud computing will be extended to
Vehicular assets from individual vehicles to
entire fleets
Cell phones and other commodity consumer
products
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