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/

https://sites.google.com/site/chhattanshah/

https://sites.google.com/site/chhattanshah/


Outline

Background

Mobile Grid and Cloud Computing

Cloud Robotics

Mobile Ad hoc Computational Grid and Cloud

Opportunities

Research Challenges

Future Research Directions

Conclusion

Background


A collection of independent computers that ap-pear to the users of the system as a single com-puter

ATM Internet

Distributed System


Types of Distributed Systems

Cluster

Grid

Cloud


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


Cloud Computing

Everything — from computing power to com-puting infrastructure and applications are delivered as a service


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


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


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


Mobile Cloud Computing

Data processing and data storage happen outside of mobile devices


Mobile Grid Computing

Data processing and data storage happen outside of mobile devices


Mobile Grid and Cloud Computing

Enabling Factors

Wireless networks• 3G networks: 14.4 Mbps

• 4G networks: 100~128 Mbps


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


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


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


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?


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


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



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



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


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


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



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



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


Mobile Ad hoc Computational

Grid


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



A distributed computing infrastructure that

allows mobile nodes to share computing

resources in mobile ad hoc environments

Service Provider Node




Service Requesting Node

Service Requesting Node

Service Broker Node

Mobile Ad hoc Network



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


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


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


Contents

38


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


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

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


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



Node mobility

Node

RESOURCE AL-LOCATION

Node

Task

Grid Members

Task Queue

Task

NODE SE-LECTION

DISPATCHER



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



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



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



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



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



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



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



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



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



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



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



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



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


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


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


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


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


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


Cloud Robotics and Networked Robots


Cloud Robotics and Networked Robots

Peer-based Model

Proxy-based Model

Clone-based Model


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

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