3 research outputs found
ServiceNet: A P2P Service Network
Given a large number of online services on the Internet, from time to time,
people are still struggling to find out the services that they need. On the
other hand, when there are considerable research and development on service
discovery and service recommendation, most of the related work are centralized
and thus suffers inherent shortages of the centralized systems, e.g.,
adv-driven, lack at trust, transparence and fairness. In this paper, we propose
a ServiceNet - a peer-to-peer (P2P) service network for service discovery and
service recommendation. ServiceNet is inspired by blockchain technology and
aims at providing an open, transparent and self-growth, and self-management
service ecosystem. The paper will present the basic idea, an architecture
design of the prototype, and an initial implementation and performance
evaluation the prototype design.Comment: 15 pages,7 figure
FES: A Fast Efficient Scalable QoS Prediction Framework
Quality-of-Service prediction of web service is an integral part of services
computing due to its diverse applications in the various facets of a service
life cycle, such as service composition, service selection, service
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 has to be faster in terms of prediction time so that it can be
integrated into a real-time recommendation or composition system. The other
important factor to consider while designing the prediction algorithm is
scalability to ensure that the prediction algorithm can tackle large-scale
datasets. The existing algorithms on QoS prediction often compromise on one
goal while ensuring the others. In this paper, we propose a semi-offline QoS
prediction model to achieve three important goals simultaneously: higher
accuracy, faster prediction time, scalability. Here, we aim to predict the QoS
value of service that varies across users. Our framework consists of
multi-phase prediction algorithms: preprocessing-phase prediction, online
prediction, and prediction using the 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 of the given QoS invocation log
matrix. 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 four publicly available WS-DREAM
datasets show the efficiency in terms of accuracy, scalability, fast
responsiveness of our framework as compared to the state-of-the-art methods.Comment: 13 pages, 15 figure
CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering
With the proliferation of Internet-of-Things and continuous growth in the
number of web services at the Internet-scale, the service recommendation is
becoming a challenge nowadays. One of the prime aspects influencing the service
recommendation is the Quality-of-Service (QoS) parameter, which depicts the
performance of a web service. In general, the service provider furnishes the
value of the QoS parameters during service deployment. However, in reality, the
QoS values of service vary across different users, time, locations, etc.
Therefore, estimating the QoS value of service before its execution is an
important task, and thus the QoS prediction has gained significant research
attention. Multiple approaches are available in the literature for predicting
service QoS. However, these approaches are yet to reach the desired accuracy
level. In this paper, we study the QoS prediction problem across different
users, and propose a novel solution by taking into account the contextual
information of both services and users. Our proposal includes two key steps:
(a) hybrid filtering and (b) hierarchical prediction mechanism. On the one
hand, the hybrid filtering method aims to obtain a set of similar users and
services, given a target user and a service. On the other hand, the goal of the
hierarchical prediction mechanism is to estimate the QoS value accurately by
leveraging hierarchical neural-regression. We evaluate our framework on the
publicly available WS-DREAM datasets. The experimental results show the
outperformance of our framework over the major state-of-the-art approaches