Dynamic Context Monitoring and Modeling for QoS-aware Applications

In the future, we are likely to see a tremendous need for context-aware applications which adapt to available context information such as physical surroundings, network or system conditions. The success of end-to-end adaptation relies on accurate and timely knowledge of the changing context information. This project aims to provide a fundamental support for context-aware applications - a context information collection service. This service delivers the right context information to the right user at the right time. The complexity of providing the context information service arises from (i) dynamically changing status of information sources; (ii) diverse user requirements in terms of Quality of Service (QoS: such as response timeliness or reliability etc.) and Quality of Data (QoD, such as data accuracy or freshness); and (iii) constantly changing system conditions.


In this project, we take into consideration the dynamic factors mentioned above and focus on addressing the tradeoffs between QoS, QoD and resource consumption by exploiting the tolerance of applications to quality violations. The objective is to ensure that applications receive the information at the desired levels of quality while ensuring effective utilization of underlying resources. We have focused on designing adaptive and cost-effective algorithms for the representation, collection and maintenance of the enormous amount of dynamic context information in heterogeneous distributed systems. These algorithms are tailored for multimedia services, mobile applications and real-time applications. In addition to these algorithmic efforts, we have designed a middleware framework supporting context awareness.




This project is supported in part by NSF grant CNS-0855060 and Office of Naval Research (ONR) as part of the DoD Multidisciplinary University Research Initiative (MURI) project CONTESSA.