The remote execution of an application inside the cloud is known as offloading. Offloading can be full and partial. If an application is executed fully inside the cloud at remote server side, this is called full offloading. Else if an application is executed partially inside the cloud, this is known as partial offloading. In this case, rest of the part is executed inside the mobile device. Offloading refers to the data transfer from a digital device to another digital device. It is a solution where computations are migrated to the resourceful computers in order to increase the capabilities of mobile devices. This method is different from the conventional client-server architecture. Client can be of two types: thin client and fat client. In case of thin client, data processing and transformation are performed at cloudlet; the client i.e. the mobile device is responsible only for retrieving and returning the data when it is being asked without any considerable processing. Applications which contains huge algorithm are unable to get processed only by the mobile device. Such applications are required to be executed inside the cloudlet for fast processing. Face recognition and voice to text translation are examples of such application. In case of fat client, the server is responsible for managing the data access only where most of the processing and transformation take place inside the client i.e. the mobile device. The mobile devices which are able to process resource rich applications are called fat clients. Instagram is a popular application which can be installed inside a mobile device. Using this application user can take a photo or video, and then can transform it and post on social networks. In such a scenario small processing takes place inside the cloudlet. This is an example where mobile devices serve as fat clients. For mobile devices cyber foraging is proposed. It is described as a mechanism to augment the computational and storage capabilities of mobile devices through task distribution.
Published in Chapter:
Natural Computing in Mobile Network Optimization
Anwesha Mukherjee (West Bengal University of Technology, India), Priti Deb (West Bengal University of Technology, India), and Debashis De (West Bengal University of Technology, India)
Copyright: © 2016|Pages: 27
DOI: 10.4018/978-1-5225-0058-2.ch017
Abstract
Nature inspired computing has been widely used to solve various research challenges of mobile network. Mobile network refers to mobile network, sensor network and ad hoc network. This chapter has focused on the application of nature inspired computing in mobile network. In this chapter, the bio-inspired techniques for wireless sensor network, mobile ad hoc network and mobile cloud computing are discussed. Ant colony optimization is used in sensor network and mobile cloud computing for efficient routing and scheduling respectively. Bee swarm intelligence is used to develop routing schemes for mobile ad hoc network. Bird flocking behavior is used for congestion control in wireless sensor network. The research challenges of bio-inspired mobile network are also illustrated.
