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Adaptive Quality-of-Service management is critical for enabling effective collaboration between distributed clients in a heterogeneous (wired and wireless) environment. This is because both client profiles (capabilities, interests and resources) and system resources can be significantly different and highly dynamic. This paper presents the design and prototype implementation of an adaptive QoS management framework for collaborative multimedia applications in distributed, heterogeneous environments. The overall goal of the framework is to locally adapt the shared information to meet the capabilities, interests and current state of each collaborating client while preserving the semantic content of the information to maintain effective sharing. Transformations investigated in this paper include gradual gradations and modality transformations. The framework builds on a publisher-subscriber messaging substrate that uses semantic profiles and provides each client with direct and immediate access to all information defined by its needs and capabilities, without having to maintain and update global rosters. It interfaces with the Simple Network Management Protocol (SNMP) to determine the state of the network by directly querying network elements. An experimental evaluation of the framework for wired and wireless clients is also presented.

Quality-of-Service (QoS) prediction of web service is an integral part of services computing due to its diverse applications in service composition/selection/recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm should be faster in terms of prediction time to be compatible with a real-time system. The other important factor to consider is scalability to tackle large-scale datasets. The existing QoS prediction algorithms often satisfy one goal while compromising the others. In this paper, we propose a semi-offline QoS prediction framework to achieve three important goals together: higher accuracy, faster prediction time, and scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework (FES) consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the proposed pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on WS-DREAM datasets show the efficiency (in terms of accuracy), scalability, and fast responsiveness (in terms of prediction time) of FES as compared to the state-of-the-art methods.

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