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.
Keynote Talk on Recent Advances in Mobile Grid and Cloud Computing
1. About Me
ī¨ 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
ī¨ Education: PhD in Computer Science
Korea University 2012
ī¨ 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
ī¨ Background
ī¨ Mobile Grid and Cloud Computing
ī¤ Cloud Robotics
ī¤ Sensor Cloud
ī¨ Mobile Ad hoc Grid and Cloud Computing
ī¤ Opportunities
ī¤ Challenges
4. Cluster Grid Cloud
Distributed System
A collection of interconnected
computers cooperatively
work together as a single
integrated computing
resource
5. ī¨ 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
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
ī¨ 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
ī¨ Mobile devices are integrated with a grid computing
system through an infrastructure-based communication
network
15. Benefits
ī¨ Improved data storage capacity and processing power
ī¨ Users can execute computationally and data-intensive
applications on mobile devices
ī¨ Extended battery life
ī¨ Improved reliability
16. Cloud Robotics
ī¨ Robots rely on a cloud-computing infrastructure to
access vast amounts of processing power and data
ī¨ Execution of computationally
intensive tasks on cloud would
result in cheaper, lighter and
easy-to-maintain hardware
ī¨ Shared library of
ī¤ objects
ī¤ algorithms
ī¤ skills
17. Cloud Robotics Projects
ī¨ 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
īŽ 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
ī¨ At CNRS researcher are creating object databases for robots to
simplify the planning of manipulation tasks like opening a door
ī¨ The idea is to develop a software framework where objects come
with a âuser manualâ for the robot to manipulate them
19. Cloud Robotics Projects
ī¨ 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
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
26. ī¨ A group of miniature autonomous mobile robots are deployed
in urban environments to detect and monitor a range of
military and non-military threats
īŽ Use sophisticated image and video processing algorithms
īŽ Vision-based navigation algorithms to navigate in the environment
Autonomous Threat Detection in Urban Environments
27. ī¨ A set of miniature unmanned aerial vehicles or mobile robots
can be deployed in a targeted area
ī¤ Broadcast live video streams
ī¤ 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
ī¨ In warfare soldiers may experience
physical and mental problems
ī¨ In such situations, various biomedical
devices can be used to continuously
monitor the soldiers' psychophysiological
health
ī¤ Data from devices can be used to assess physical
and mental health
ī¤ Soldiers also need to rely on various sensing,
processing and communication systems in the
vicinity to achieve situational awareness and understanding
of the battlefield
ī¤ 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
ī¨ Mobile ad hoc computational grid is attractive even
when network infrastructure is available
ī¨ Short-range wireless communication consumes less
energy and provides faster connectivity
ī¤ 3G networks 14 Mbps
ī¤ 4G networks 100 Mbps
ī¤ Wi-Fi LAN 400Mbps
30. Research Challenges and Future Research Directions
ī¨ Compared to traditional parallel and distributed
computing systems such as grid and cloud mobile
ad hoc computational grid is characterized by
ī¤ Node mobility
ī¤ Limited battery power
ī¤ Low bandwidth and high latency
ī¤ Shared and unreliable communication medium
ī¤ Infrastructure-less network environment
31. Research Challenges and Future Research Directions
ī¨ Node Mobility
ī¤ Global Node Mobility
ī§ Task Failure
ī¤ Local Node Mobility
ī§ Increased data transfer times
ī¤ Mobility of an Intermediate Node
ī§ Increased data transfer times
ī§ May disconnect network
ī¤ Approaches:
ī§ Task migration
ī§ Task reallocation
ī§ In both cases delay due to
reallocation or migration of task
32. Research Challenges and Future Research Directions
ī¨ Node Mobility
ī¤ Makes it difficult to design an efficient and robust resource
discovery and monitoring system
ī§ After reporting status a node may move across the coverage area
ī§ Grid management system would assume that status is valid and
would make decisions accordingly
ī¤ To avoid this problem
īŽ Proactive approach
ī§ Resources can be monitored continuously or with minimum update
interval
ī§ In both cases there will be a communication overhead
īŽ Reactive approach
ī§ Reduces communication cost but introduces delay
33. Research Challenges and Future Research Directions
ī¨ Power Management
ī¤ Main sources of energy consumption are CPU processing,
memory, and data transmission in the network
ī¤ Key factors that contribute to transmission energy
consumption
īŽ transmission power required to transmit data and
īŽ communication cost induced by data transfers between tasks
ī¤ Most of the schemes are focused on the conservation of
processing energy
34. ī¨ Power Management
ī¤ Energy efficient resource allocation scheme
īŽ Aims to reduce transmission energy consumption and data
transfer cost
īŽ Basic idea is to allocate tasks to nodes that are accessible at
minimum transmission power
Research Challenges and Future Research Directions
35. ī¨ Constrained communication environment due shared and
unreliable communication medium and node mobility
ī¨ Suffers from high latency and unstable connectivity problems
ī¤ In such an environment data transfer cost is very critical for
application and system performance
ī¤ To reduce data transfer costs, directional antennas, efficient
medium access control, channel switching, and multiple radios
are a few promising approaches
ī¤ Parallel applications usually consist of a range of tasks with
varying bandwidth, processing, and deadline constraints
Research Challenges and Future Research Directions
36. ī¨ Dynamic network performance
ī¤ 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. ī¨ Task Migration
ī¤ To improve application performance and resource
utilization and to avoid task failure and load
imbalance
ī¤ Most common migration strategy is to estimate
migration cost and determine task completion time
before and after the migration of task
ī¤ Estimation of migration cost particularly of data
intensive task is not straightforward due to dynamic
communication environment
ī§ How to estimate data transfer time?
Research Challenges and Future Research Directions
38. ī¨ Parallel programming model
ī¤ Programming model provides an abstract view of
computing system
ī¤ The traditional parallel programming models do not
deal well with communication issues
īŽ Therefore are not suitable for mobile ad hoc environments
where communication latencies and link failure and
activation ratios are too high
ī¤ 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. ī¨ Security risks
ī¤ Mobile ad hoc computational Grid
īŽ may include heterogeneous devices owned by various
individuals and organizations
īŽ can be used in various scenarios such as military and
disaster relief where security is a primary concern
ī¤ Design of an efficient security system is a challenging
task due to
īŽ infrastructure-less network environment
īŽ shared communication medium
īŽ node mobility
Research Challenges and Future Research Directions
40. ī¨ Incentive mechanism
ī¤ Assume a scenario where an individual travelling with
strangers requires additional computing resources to
execute a computationally intensive task
īŽ The problem is how to or what will motivate an individual to share
her resources with a stranger?
ī¤ To address this problem, a few solutions have been
proposed in the literature where either battery power or
processing cycles are traded
īŽ Effective when both parties are in need of resources
Research Challenges and Future Research Directions
41. ī¨ Architecture for mobile ad hoc computational Grid
ī¤ Centralized
īŽ Single point of failure and scalability
ī¤ Decentralised
īŽ Group management
īŽ Ineffective resource allocation
ī¤ Distributed
īŽ Ineffective resource allocation
ī¤ Hybrid architecture
Research Challenges and Future Research Directions
42. ī¨ Failure management
ī¤ Migrate the task or restart the task on another node
ī¤ Estimation of task completion time with and without
migration cost?
ī¨ Quality of Service support
ī¤ Applicationâs demands such as energy, bandwidth
guarantees and real-time services
ī¨ Standards for heterogeneous environments
ī¨ Wireless Communication Technologies
Research Challenges and Future Research Directions
43. Conclusion
ī¨ 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
ī¨ Further investigation is required
ī¤ Resource Management
ī¤ Programming model
ī¤ Communication performance
ī¤ Mobility
ī¤ QoS support