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Keynote on Mobile Grid and Cloud Computing
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Transcript of Keynote on Mobile Grid and Cloud Computing
한국해양과학기술진흥원
Mobile Grid and Cloud Comput-
ing Opportunities and Chal-
lenges
2013.9.22
Sayed Chhattan Shah, PhD
Senior Researcher
Electronics and Telecommunications Research Institute, Korea
etri.re.kr | https://sites.google.com/site/chhattanshah/
한국해양과학기술진흥원
Outline
Background
Mobile Grid and Cloud Computing
Cloud Robotics
Mobile Ad hoc Computational Grid and Cloud
Opportunities
Research Challenges
Future Research Directions
Conclusion
Background
한국해양과학기술진흥원
A collection of independent computers that ap-pear to the users of the system as a single com-puter
ATM Internet
Distributed System
한국해양과학기술진흥원
Types of Distributed Systems
Cluster
Grid
Cloud
한국해양과학기술진흥원
Overview: Clusters x GridsCluster - How can we use local net-worked resources to achieve better per-formance for large scale applications? High-speed LAN
Centralized resource and task manage-ment
How can we put together geographically distributed resources to achieve better performance? WAN
Distributed resource and task management
Cluster and Grid Computing
InformationGenerators
Information DistributedOver the Grid
CustomerAccess to Information
Grid
Computing power should be available on demand, for a fee
Just like the electrical power grid
Basic Idea
한국해양과학기술진흥원
Cloud Computing
Everything — from computing power to com-puting infrastructure and applications are delivered as a service
한국해양과학기술진흥원
Grid Computing
Computational Grids and Clusters have been ex-tensively deployed and widely used to solve com-plex and challenging problems in science and en-gineering areas such as drug design, earthquake simulation, and climate modeling
한국해양과학기술진흥원
Grid Computing
Due to recent advances in mobile comput-
ing and communication technologies, it has
become feasible to use mobile nodes as a
contributing entity to Grids and Clouds
한국해양과학기술진흥원
Grid Computing
Several approaches have been proposed to
integrate mobile nodes with Grid and Cloud
computing systems
Mobile Grid and Cloud Computing Mobile Ad hoc Grid and Cloud
Computing
Mobile Ad hoc Network
Mobile Grid and Cloud Comput-
ing
한국해양과학기술진흥원
Mobile Cloud Computing
Data processing and data storage happen outside of mobile devices
한국해양과학기술진흥원
Mobile Grid Computing
Data processing and data storage happen outside of mobile devices
한국해양과학기술진흥원
Mobile Grid and Cloud Computing
Enabling Factors
Wireless networks• 3G networks: 14.4 Mbps
• 4G networks: 100~128 Mbps
한국해양과학기술진흥원
Benefits
Improved data storage capacity and processing power
Apple’s iCloud enables users to store and synchronize data in the cloud
Users can execute computationally and data-intensive ap-plications on mobile devices
Image processing
Natural language processing
Video processing
Extended battery life
Improved reliability Data and application are stored and backed up on a number of computers
Cloud Robotics
한국해양과학기술진흥원
Cloud Robotics
Robots rely on a cloud-computing infrastructure to access vast amounts of processing power and data
Robots can offload heavy tasks Image processing
Voice recognition
한국해양과학기술진흥원
Benefits
Provides a shared knowledge database
Organizes and unifies information about the world in a for-mat usable by robots
Robot Goggles
Upload images -> Download Semantic• Object name • 3D model, mass, materials, friction properties• Usage instructions - function, how to grasp, operate• Context and Domain knowledge
한국해양과학기술진흥원
Benefits
Skill / Behavior Database Reusable library of “skills” or behaviors that map to per-
ceived task requirements / complex situations
Matrix Movie Scene
For humans, still science fiction
For robots?
한국해양과학기술진흥원
Benefits
Offloads heavy computing tasks to the cloud
Cheaper, lighter, easier-to-maintain hardware
Longer battery life
Less need for software pushes/updates
CPU hardware upgrades are invisible & hassle-free
한국해양과학기술진흥원
Cloud Robotics Projects
Researchers at Social Robotics Lab have built a cloud computing infrastructure to generate 3-D models of environments
Allowing robots to perform simultaneous localization and mapping much faster than by relying on their on-board computers
• SLAM refers to a technique for a robot to build a map of the environ-ment without a priori knowledge, and to simultaneously localize itself in the unknown environment
한국해양과학기술진흥원
Cloud Robotics Projects
At CNRS, researcher are creating object data-bases for robots to simplify the planning of ma-nipulation tasks like opening a door
The idea is to develop a software framework where objects come with a "user manual" for the robot to manipulate them
한국해양과학기술진흥원
Cloud Robotics Projects
Gostai, a French robotics firm, has built a cloud robotics in-frastructure called GostaiNet, which allows a robot to per-form speech recognition, face detection, and other tasks remotely
Jazz telepresence robot uses the cloud for video recording and voice synthesis
한국해양과학기술진흥원
Cloud Robotics
Same as:
Remote computing?
Mobile cloud computing?
Mobile Grid Computing?
Computation Offloading
Migrating computation to more resourceful com-puters
Computation offloading = Surrogate computing = Remote execution
한국해양과학기술진흥원
Offloading decisions are usually made by analyzing several parameters including
Bandwidths
Server speeds
Available memory
Server loads
Amounts of data exchanged between servers and mobile systems
Computation Offloading
한국해양과학기술진흥원
Offloading approaches are classified based on various factors including
Why to offload • Improve performance or save energy
What mobile systems use offloading • Smart phones, robots, sensors
Infrastructures for offloading • Cluster, Grid, Cloud
Types of applications • Multimedia, gaming, calculators, text editors
Computation Offloading
한국해양과학기술진흥원
Application partitioning• Static vs. dynamic
When to decide offloading • Static vs. dynamic
Offloading data-intensive interdependent tasks
Offloading small tasks• May not improve performance or reduce energy consumption
Computation Offloading
한국해양과학기술진흥원
Computation Offloading
Mobile Ad hoc Computational
Grid
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
The mobile Grid and Cloud computing systems
are restricted to infrastructure-based communi-
cation systems such as cellular network, and
therefore cannot be used in mobile ad hoc envi-
ronments
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
A distributed computing infrastructure that
allows mobile nodes to share computing
resources in mobile ad hoc environments
Service Provider Node
Service Provider Node
Service Provider Node
Service Provider Node
Service Requesting Node
Service Requesting Node
Service Broker Node
Mobile Ad hoc Network
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
Computational Grid allows distributed computing devices to share computing re-
sources to solve computationally-intensive problems
Mobile ad hoc network a wireless network of mobile devices that communicate with
each other without pre-existing network infrastructure
COMPUTATIONAL GRID
MOBILE AD HOC NETWORK
MOBILE AD HOC COMPUTATIONAL GRID
APPLICATIONS
MOBILE NODES
Applications
한국해양과학기술진흥원
Autonomous Threat Detection in Urban Environments
A group of miniature autonomous mobile robots are deployed in urban environments to detect and monitor a range of military and non-military threats
Use sophisticated image and video processing algo-rithms
Vision-based navigation algorithms to navigate in the environment
Beyond capabilities of single miniature mobile node
한국해양과학기술진흥원
Construction of 3D-Map and Identification of Targets within Map
A set of miniature unmanned aerial vehicles or mobile robots can be de-ployed in a targeted area Broadcast live video streams
Processed to construct map and indentify sta-tionary and mobile targets
Requires huge processing power
한국해양과학기술진흥원
Contents
38
한국해양과학기술진흥원
Video Data Mining
Fighting units need to know activities of target in the last 60 minutes from archived video content which requires storing live video content
To store content, a large amount of storage space is required
Processing of stored video content according to user demand also requires large amounts of processing power
Nodes owned by soldiers or fighting units can form an ad hoc data and computational Grid
한국해양과학기술진흥원
Future Soldier
In warfare soldiers may experience physical and mental problems
In such situations, various biomedical devices can be used to continuously monitor the soldiers' psychophysiological health
Data from devices can be used to assess physical and mental health
Soldiers also need to rely on various sensing, processing and communication systems in the vicinity to achieve situational awareness and understanding of the battlefield
Simultaneously executing computationally-intensive models for deriving physiological parameters and for acquiring battlefield awareness in real time requires computing capabilities that go beyond those of an individual sensing and processing devices
한국해양과학기술진흥원
Mobile Ad hoc Computational Grid
Mobile ad hoc computational Grid is attractive
even when network infrastructure is available
Short-range wireless communication consumes
less energy and provides faster connectivity
3G networks: 2~14.4 Mbps
4G networks: 100~128 Mbps
Wi-Fi LAN 400Mbps
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Compared to traditional parallel and distributed computing systems such as Grid and Cloud mo-bile ad hoc computational Grid is characterized by
Node mobility
Limited battery power
Low bandwidth and high latency
Shared and unreliable communication medium
Infrastructure-less network environment
• No one is in charge
• No one to provide standard service
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
Node
RESOURCE AL-LOCATION
Node
Task
Grid Members
Task Queue
Task
NODE SE-LECTION
DISPATCHER
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
Global Node Mobility
Task Failure
Local Node Mobility
Increased data transfer times
Mobility of an Intermediate Node
Increased data transfer times and may disconnect network
Approaches:
Task migration
Task reallocation
In both cases, delay due to reallocation or migration of task
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
To improve performance and avoid task failure or mi-gration, nodes with long-term connectivity are required for the allocation of tasks
An effective and robust two-phase resource allocation scheme
Exploit the history of user’s mobility patterns in order to se-lect nodes that provide long-term connectivity
Location prediction schemes
Use node’s direction and speed to predict future connectiv-ity
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
Makes it difficult to design an efficient and robust re-source discovery and monitoring system
After reporting status a node may move across the coverage area
Grid management system would assume that status is valid and would make deci-sions accordingly
To avoid this problem
• Proactive approach
Resources can be monitored continuously or with minimum update in-terval
In both cases, there will be a communication overhead
• Use reactive approach
Reduces communication cost but introduces delay
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Power management
Main sources of energy consumption are CPU process-ing, memory, and data transmission in the network
Key factors that contribute to transmission energy con-sumption
•transmission power required to transmit data and
•communication cost induced by data transfers between tasks
Most of the schemes are focused on the conservation of processing energy
Saving energy in data transfers between tasks remains an open problem
• becomes even more critical for data-intensive parallel appli-cations
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Power management
Energy efficient resource allocation scheme
• Aims to reduce transmission energy consumption and data transfer cost
• Basic idea is to allocate tasks to nodes that are accessible at minimum transmission power
1TPL 3TPL 4TPL2TPL
X
1TPL 3TPL 4TPL 2TPL
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Constrained communication environment due limited power, shared medium and node mobility
Suffers from low bandwidth, high latency and unsta-ble connectivity problems
In such an environment, data transfer cost is very critical for application and system performance
To reduce data transfer costs, directional antennas, efficient medium access control, channel switching, and multiple ra-dios are a few promising approaches
Parallel applications usually consist of a range of tasks with varying bandwidth, processing, and deadline constraints
Work is needed to develop a Grid management system that should exploit a diverse range of links, node capabilities, and application’s characteristics
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Dynamic network performance
Bandwidth at different network portions varies over the time and different nodes often experience differ-ent connection quality at the same time due to the traffic load and communication constraints
Grid management system that should consider net-work dynamics particularly when data-intensive inter-dependent tasks need to be allocated
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Task Migration
To improve application performance and resource util-ization, and to avoid task failure and load imbalance
Most common migration strategy is to estimate migra-tion cost and determine task completion time before and after the migration of task
However, estimation of migration cost particularly of data intensive task is not straightforward due to dy-namic communication environment
How to estimate data transfer time?
In addition, this strategy works well when amount of data transmitted or processed by a task is known in advance
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Parallel programming model
Programming model provides an abstract view of computing system
The traditional parallel programming models do not deal well with communication issues
• Therefore are not suitable for mobile ad hoc environments where communication latencies and link failure and activa-tion ratios are too high
Actor-based programming model could be the possible candidate because it deals quite well with high laten-cies, offers lightweight migration and can be easily adopted to deal with node mobility
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Security risks
Mobile ad hoc computational Grid may include hetero-geneous devices owned by various individuals, organi-zations and groups
can be used in various scenarios such as military, dis-aster relief and urban surveillance where security is a primary concern
Compared to traditional wired and wireless networks, design of an efficient security system for mobile ad hoc computational Grid is a challenging task
• due infrastructure-less network environment, shared communication medium, and node mobility
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Incentive mechanism Assume a scenario where an individual travelling with
strangers requires additional computing resources to perform a computationally intensive task
• The problem is how to or what will motivate an individual to share her resources with a stranger?
To address this problem, a few solutions have been proposed in the literature where either battery power or processing cycles are traded
• Effective when both parties are in need of resources from each other
The design of an incentive mechanism for mobile ad hoc computational Grids is difficult due to lack of cen-tral authority and ad hoc system architecture
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Architecture for mobile ad hoc computa-tional Grid
Centralized
• Single point of failure and scalability
Decentralised
• Group management
• Ineffective resource allocation
Distributed
• Ineffective resource allocation
Hybrid architecture
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Failure management Migrate the task or restart the task on another node
estimation of task completion time with and without migration cost?
Quality of Service support
application’s demands such as energy, bandwidth guarantees and real-time services
Standards for heterogeneous environments
Wireless Communication Technologies
한국해양과학기술진흥원
FARE-SHARE Project
Aims to exploit collective capabilities of nearby devices
To execute compute-intensive models for deriving physiological pa-rameters and for acquiring context awareness in real time
한국해양과학기술진흥원
Aims to develop a system for aerial surveillance to assist a rescue team in case of a disaster situation
Video data is submitted to an evaluation system via a high per-formance communication network where a 3D virtual world is created in quasi real time
Collaborative Drones
한국해양과학기술진흥원
Aims to develop a system for aerial surveillance to assist a rescue team in case of a disaster situation
Master-Slave Collaborative UAV Surveillance System Architecture
한국해양과학기술진흥원
Troops frequently have to wait until they’re back at camp to download latest up-dates
Mission opportunities may erode because the information needed at the tactical edge isn’t im-mediately available
CBMEN program aims to rapidly share up-to-date imagery, maps and other vi-tal information directly among front-line units
Each squad member’s mobile device function as a server, so content is gen-erated, distributed and maintained at the tactical edge where it’s needed
A key factor that enables CBMEN is the tremendous computing power avail-able in current mobile devices
64 gigabytes of storage in a single smartphone
A squad of nine troops could have more than half a terabyte (500 GB) of cloud storage
Content-Based Mobile Edge Networking Program
한국해양과학기술진흥원
Conclusion
Due to recent advances in mobile computing and communication technologies it has become fea-sible to design and develop next generation of distributed applications through sharing of com-puting resources in mobile and ad hoc environ-ments
Further investigation is required
Resource Management
Programming model
Communication performance
Mobility
QoS support
Backup
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
Peer-based Model
Proxy-based Model
Clone-based Model
한국해양과학기술진흥원
Vision Understanding
Attention Detection Body pose recognition Face detection Face pose recognition Eye detection
Lip Motion Detection
Face & eye tracking Mouth location & tracking Speaking recognition (spatial-temporal analysis)
Facial Expression and Emotion Local feature analysis Global face pattern analysis
Online face learning and recognition