A lightweight framework for prediction-based resource management in future wireless networks

The vast proliferation and widespread use of a variety of mobile devices in the heterogeneous networking environment necessitates the introduction of lightweight management mechanisms to ease the administration complexity and optimise the overall system performance. To this end, one key research problem is the design of novel functionalities in network nodes to enable their self-adaptation to varying operational conditions, e.g. their own resources saturation--and to the status of other neighbouring nodes, to assure stability and optimality in the resource management. In these terms, the introduction of advanced techniques for the load balancing of users' requests in order to avoid the resources saturation is a fundamental objective. The latter addresses both the local node level as well as the cluster level of neighbouring nodes. In this article, an appropriate model for the management of computational system resources is proposed, enhanced with prediction schemes. An algorithmic framework is introduced for the proactive load balancing of user decision-making requests, assuming reconfigurable and autonomous mobile devices. The latter is based on the proposed metric of user satisfaction; such metric is a function of the network response time for serving the decision-making requests. An analytical model has been proposed to compute the predicted values of the user satisfaction, extending the prediction models by Andreolini. Acting on top of the typical load-balancing actions for handling the current resources saturations, the goal of this framework is to avoid the full utilisation of system resources in the near future. Afterwards, the introduced prediction-based load-balancing framework has initially been evaluated in a test-single node system and then applied in a case study system. The obtained results show the gains of the presented framework in terms of the number of dropped user requests. The introduction of prediction schemes enables to minimise the number of dropped user requests for both classes of mobile devices. It should be noted that the prediction framework optimises the failure rates for the autonomous mobile devices. This outcome indicates that the introduction of intelligence in the mobile devices eases their proactive management.