keynote talk on recent advances in mobile grid and cloud computing

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

Recent Advances in Mobile Grid and Cloud Computing

Sayed Chhattan Shah

Assistant Professor of Computer Science

Hankuk University of Foreign Studies Korea

[email protected]|https://sites.google.com/site/chhattanshah

Contents

Background

Mobile Grid and Cloud Computing

Cloud Robotics

Sensor Cloud

Mobile Ad hoc Grid and Cloud Computing

Opportunities

Challenges

Cluster Grid Cloud

Distributed System

A collection of interconnected

computers cooperatively

work together as a single

integrated computing

resource

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


Communications

Software

PC


Communications

Software

PC


Communications

Software

Sequential Applications

Parallel Applications





Geographically

distributed

resources are

connected through

a wide area

network

Grid Computing

Cloud Computing

Everything - from

computing power

to computing

infrastructure and

applications are

delivered as a

service

Grid and Cloud Computing

Computing resources should be available on

demand for a fee like electrical power grid

Basic idea


Grid and cloud computing systems have been extensively

deployed and widely used to solve large and complex

problems in science and engineering areas


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


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 devices are integrated with a cloud computing

system through an infrastructure-based communication

network such as cellular network


Mobile devices are integrated with a grid computing

system through an infrastructure-based communication

network

Enabling Factors

Benefits

Improved data storage capacity and processing power

Users can execute computationally and data-intensive

applications on mobile devices

Extended battery life

Improved reliability

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

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


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


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

Sensor Cloud

Sensors are integrated with cloud computing systems

Sensor Cloud Applications

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


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

Architecture

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

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

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


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

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


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


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


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

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


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


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


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?


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


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


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


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


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


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

keynote talk on recent advances in mobile grid and cloud computing

Education

cloud computing grid

cloud computing mobile

grid computing system

computing power

distributed computing

cloud robotics robo

mobile nodes

various mobile devices