Enhancing availability of grid computational services to ubiquitous computing applications

The grid is an integrated infrastructure that can play the dual roles of a coordinated resource consumer as well as a donator in distributed computing environments. In a mobile grid environment, the grid acts as a resource hungry consumer whereas in a ubiquitous computing environment, it has the inherent potential to provide services to applications. The enormous growth in the use of mobile and embedded devices in ubiquitous computing environment and their interaction with human beings, produces a huge amount of data that need to be processed efficiently anytime anywhere. However, such devices often have limited resources in terms of CPU, storage, battery power and communication bandwidth. Thus, there is a need to transfer ubiquitous computing application services to more powerful computational resources. In this paper, we investigate the use of the grid as a candidate for provisioning computational services to applications in ubiquitous computing environments. In particular, we present a competitive model that describes the possible interaction between the competing resources in the grid infrastructure as service providers and ubiquitous applications as subscribers. The competition takes place in terms of quality of service (QoS) and cost offered by different grid service providers. The ubiquitous users' resource demands depend not only on the QoS parameters (e.g., response time and loss probability, i.e., loss of ubiquitous users experienced by a grid service provider in our context) offered by that service provider but also upon those of its competitors. We develop a stochastic equilibrium model for QoS assurance. Based upon those QoS parameters, we propose two types of demand models whose distribution functions are closely correlated with service level QoS parameters. We also present the equilibrium behavior of our model facing global competition under stochastic demand and estimate guaranteed QoS assurance level by efficiently satisfying the requirement of ubiquitous application. Simulation results show that our proposed framework maximizes the provider's expected revenue at the optimal point of different QoS parameters.