Due to recent advances in mobile computing and networking technologies it has become feasible to integrate various mobile devices such as robots, aerial vehicles, sensors, and smart phones with grid and cloud computing systems. This integration enables design and development of next generation of applications through sharing of computing resources in mobile environments and also introduces several challenges due to dynamic and unpredictable network. In this talk, we will discuss applications and challenges involved in design and development of mobile grid and cloud computing systems, cloud robots, and innovative architectures for creating energy efficient and robust mobile cloud.

1. About Me
 Name: Sayed Chhattan Shah
Assistant Professor of Computer Science at HUFS Korea
Visiting Faculty at Seoul National University of Science & Technology
Visiting Researcher at Internet Computing Lab of Korea University
 Education: PhD in Computer Science
Korea University 2012
 Experience: Senior Researcher
Electronics and Telecommunications Research Institute Korea
Visiting Faculty at Dongguk University
Assistant Manager NESCOM
Lecturer Hamdard University
Technical Consultant Hamdard Information Technology Services

2. Recent Advances in Mobile Grid and Cloud Computing
Sayed Chhattan Shah
Assistant Professor of Computer Science
Hankuk University of Foreign Studies Korea
shah@hufs.ac.kr|https://sites.google.com/site/chhattanshah

3. Contents
 Background
 Mobile Grid and Cloud Computing
 Cloud Robotics
 Sensor Cloud
 Mobile Ad hoc Grid and Cloud Computing
 Opportunities
 Challenges

4. Cluster Grid Cloud
Distributed System
A collection of interconnected
computers cooperatively
work together as a single
integrated computing
resource

5.  Distributed computing devices are connected
through a local area network to achieve better
performance for large scale applications
Cluster Computing
Parallel Programming Environment
Cluster Middleware
(Single System Image and Availability Infrastructure)
Cluster Interconnection Network
PC
Network Interface Hardware
Communications
Software
PC
Network Interface Hardware
Communications
Software
PC
Network Interface Hardware
Communications
Software
PC
Network Interface Hardware
Communications
Software
Sequential Applications
Parallel Applications
Parallel Applications
Parallel Applications
Sequential Applications
Sequential Applications

6. Geographically
distributed
resources are
connected through
a wide area
network
Grid Computing

7. Cloud Computing
Everything - from
computing power
to computing
infrastructure and
applications are
delivered as a
service

8. Grid and Cloud Computing
Computing resources should be available on
demand for a fee like electrical power grid
Basic idea

9. Grid and Cloud Computing
Grid and cloud computing systems have been extensively
deployed and widely used to solve large and complex
problems in science and engineering areas

10. Grid and Cloud Computing
Due to recent advances it has become feasible to integrate
various mobile devices such as robots aerial vehicles sensors
and smart phones with grid and cloud computing systems

11. Mobile Grid and Cloud 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

12. Mobile Grid and Cloud Computing
 Mobile devices are integrated with a cloud computing
system through an infrastructure-based communication
network such as cellular network

13. Mobile Grid and Cloud Computing
 Mobile devices are integrated with a grid computing
system through an infrastructure-based communication
network

14. Enabling Factors

15. Benefits
 Improved data storage capacity and processing power
 Users can execute computationally and data-intensive
applications on mobile devices
 Extended battery life
 Improved reliability

16. Cloud Robotics
 Robots rely on a cloud-computing infrastructure to
access vast amounts of processing power and data
 Execution of computationally
intensive tasks on cloud would
result in cheaper, lighter and
easy-to-maintain hardware
 Shared library of
 objects
 algorithms
 skills

17. 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 onboard
computers
 SLAM refers to a technique for a robot to build a map of the environment without a priori
knowledge and to simultaneously localize itself in the unknown environment

18. Cloud Robotics Projects
 At CNRS researcher are creating object databases for robots to
simplify the planning of manipulation 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

19. Cloud Robotics Projects
 A French robotics firm has built a cloud robotics infrastructure called
GostaiNet which allows a robot to perform speech recognition, face
detection, and other tasks remotely on a cloud

20. Sensor Cloud
 Sensors are integrated with cloud computing systems

21. Sensor Cloud Applications

22. Sensor Cloud Applications

23. Mobile Ad hoc Computational Grid
The mobile grid and cloud computing systems are restricted to
infrastructure-based communication systems such as cellular
network. Therefore cannot be used in mobile ad hoc
environments

24. Mobile Ad hoc Computational Grid
Mobile ad hoc
computational grid
or cloud is a
distributed
computing
infrastructure that
allows mobile nodes
to share computing
resources in mobile
ad hoc environments

25. Architecture

26.  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 algorithms
 Vision-based navigation algorithms to navigate in the environment
Autonomous Threat Detection in Urban Environments

27.  A set of miniature unmanned aerial vehicles or mobile robots
can be deployed in a targeted area
 Broadcast live video streams
 Processed to construct map and identify mobile targets
Construction of 3D-Map and Identification of
Static and Mobile Targets within a Map

28. 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

29. 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 14 Mbps
 4G networks 100 Mbps
 Wi-Fi LAN 400Mbps

30. Research Challenges and Future Research Directions
 Compared to traditional parallel and distributed
computing systems such as grid and cloud mobile
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

31. 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
 May disconnect network
 Approaches:
 Task migration
 Task reallocation
 In both cases delay due to
reallocation or migration of task

32. Research Challenges and Future Research Directions
 Node Mobility
 Makes it difficult to design an efficient and robust resource
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 decisions accordingly
 To avoid this problem
 Proactive approach
 Resources can be monitored continuously or with minimum update
interval
 In both cases there will be a communication overhead
 Reactive approach
 Reduces communication cost but introduces delay

33. Research Challenges and Future Research Directions
 Power Management
 Main sources of energy consumption are CPU processing,
memory, and data transmission in the network
 Key factors that contribute to transmission energy
consumption
 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

34.  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
Research Challenges and Future Research Directions

35.  Constrained communication environment due shared and
unreliable communication medium and node mobility
 Suffers from high latency and unstable 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 radios
are a few promising approaches
 Parallel applications usually consist of a range of tasks with
varying bandwidth, processing, and deadline constraints
Research Challenges and Future Research Directions

36.  Dynamic network performance
 Bandwidth at different network portions varies over the
time and different nodes often experience different
connection quality at the same time due to the traffic load
and communication constraints
Research Challenges and Future Research Directions

37.  Task Migration
 To improve application performance and resource
utilization and to avoid task failure and load
imbalance
 Most common migration strategy is to estimate
migration cost and determine task completion time
before and after the migration of task
 Estimation of migration cost particularly of data
intensive task is not straightforward due to dynamic
communication environment
 How to estimate data transfer time?
Research Challenges and Future Research Directions

38.  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
activation ratios are too high
 Actor-based programming model could be the
possible candidate because it deals quite well with
high latencies, offers lightweight migration and can
be easily adopted to deal with node mobility
Research Challenges and Future Research Directions

39.  Security risks
 Mobile ad hoc computational Grid
 may include heterogeneous devices owned by various
individuals and organizations
 can be used in various scenarios such as military and
disaster relief where security is a primary concern
 Design of an efficient security system is a challenging
task due to
 infrastructure-less network environment
 shared communication medium
 node mobility
Research Challenges and Future Research Directions

40.  Incentive mechanism
 Assume a scenario where an individual travelling with
strangers requires additional computing resources to
execute 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
Research Challenges and Future Research Directions

41.  Architecture for mobile ad hoc computational Grid
 Centralized
 Single point of failure and scalability
 Decentralised
 Group management
 Ineffective resource allocation
 Distributed
 Ineffective resource allocation
 Hybrid architecture
Research Challenges and Future Research Directions

42.  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
Research Challenges and Future Research Directions

43. Conclusion
 Due to recent advances it has become feasible to
design and develop next generation of distributed
applications through sharing of computing resources
in mobile environments
 Further investigation is required
 Resource Management
 Programming model
 Communication performance
 Mobility
 QoS support