Automating Mobile Task Offloading with Agent Based Auctions
Shah, Nidhi Niket
MetadataShow full item record
Mobile Cloud Computing has been introduced to be a potential technology for mobile services. It solves mobile computing’s fundamental problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device i.e. by offloading them onto the cloud servers. Cloud computing allows users to use infrastructure, platforms and software in an on-demand fashion. Thus, data storage and processing services in clouds, eliminates the need for mobile users to have a powerful device configuration (e.g. CPU speed, memory capacity etc.), as all resource-intensive computing can be performed on the cloud. The data center hardware and software is referred as the cloud and the cloud can be made available “pay-as-you-use” manner. This payment for services to cloud is based on services we require for cloud to process. These services are further differentiated according to types, availability and quality. For instance, to run any particular application, one cloud provider offers less price than other cloud provider but its quality is compromised. In some cases, single cloud is not enough to meet mobile user’s demands. Therefore, new scheme is needed in which the mobile users can utilize multiple clouds in a unified fashion and get opportunity to choose the best cloud provider to serve them. The present research addresses this issue and presents design scenario for the agent based auction model where mobile users get chance to receive best services from one of the cloud providers which offers minimum cost including price and quality of the service. This quality of service refers to energy and latency optimization. The agent based framework has been designed on JADE framework, to provide robustness to the model. Concept of auction market and auction manager has been introduced in this research that helps mobile users to get energy-latency optimization with optimal price to be paid to the cloud providers for accessing their resources. The proposed mechanism has been evaluated for different scenarios and it gives optimal solution for all the cases. Thus, proposed approach can be scaled in future for more features.