Results 191 to 200 of about 292,632 (247)

Web Service QoS Prediction via Collaborative Filtering: A Survey

IEEE Transactions on Services Computing, 2020
With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services.
Zibin Zheng, Liao Xiaoli, Mingdong Tang, Fenfang Xie, Michael R. Lyu   +4 more
semanticscholar   +1 more source

QoS measurement issues with DAML-QoS ontology

IEEE International Conference on e-Business Engineering (ICEBE'05), 2005
With the industry's efforts in promoting the use of Web services, a huge number of Web services are being developed for the Web. To improve the service provider's competition advantage, it is necessary to support QoS-aware service discovery mechanism as well as the corresponding measurement system.
C. Zhou, L.-T. Chia, B.-S. Lee
openaire   +1 more source

Predicting unknown QoS value with QoS-Prophet

Proceedings Demo & Poster Track of ACM/IFIP/USENIX International Middleware Conference, 2013
We have witnessed the widespread adoption of Collaborative Filtering (CF) in prediction of unknown QoS value. Existing approaches have neglected that CF idea was originated from the processing of subjective data, movies scores for example. However, QoS value is objective, therefore the existing CF-based prediction approaches are not applicable well for
You Ma, Shangguang Wang, Qibo Sun, Hua Zou, Fangchun Yang   +4 more
openaire   +1 more source

FM-QoS

Proceedings of the 1997 ACM/IEEE conference on Supercomputing (CDROM) - Supercomputing '97, 1997
FM-QoS employs a novel communication architecture based on network feedback to provide predictable communication performance (e.g. deterministic latencies and guaranteed bandwidths) for high speed cluster interconnects. Network feedback is combined with self-synchronizing communication schedules to achieve synchrony in the network interfaces (NIs ...
Kay Connelly, Andrew A. Chien
openaire   +1 more source

QoS Prediction for Service Recommendation With Features Learning in Mobile Edge Computing Environment

IEEE Transactions on Cognitive Communications and Networking, 2020
In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques.
Yuyu Yin, Zengxu Cao, Yueshen Xu, Honghao Gao, Rui Li, Zhida Mai   +5 more
semanticscholar   +1 more source

QoS Driven Task Offloading With Statistical Guarantee in Mobile Edge Computing

IEEE Transactions on Mobile Computing, 2020
In mobile edge computing, popular mobile applications, such as augmented reality, usually offload their tasks to resource-rich edge servers. The user experience can be considerably affected when many mobile users compete for the limited communication and
Qing Li, Shangguang Wang, Ao Zhou, Xiao Ma, Fangchun Yang, A. Liu   +5 more
semanticscholar   +1 more source

QoS-Predictions Service

2010
The success of emerging mobile services depends on the serviceability of the underlying wireless networks, expressed in terms of Quality of Service (QoS) provided by a network available to service user at a given geographical location and time. In general, this serviceability is a priori unknown.
Wac, Katarzyna, Konstantas, Dimitri, Bults, Richard, Van Beijnum, Bert-Jan, Hilario, Mélanie   +4 more
openaire   +3 more sources

FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

IEEE Internet of Things Journal, 2019
Recent advances in the areas of Internet of Things (IoT), big data, and machine learning have contributed to the rise of a growing number of complex applications.
Ashkan Yousefpour, A. Patil, Genya Ishigaki, Inwoong Kim, Xi Wang, H. Cankaya, Qiong Zhang, Weisheng Xie, J. Jue   +8 more
semanticscholar   +1 more source

e-QoS

Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks - QShine '06, 2006
In this paper we propose a novel energy-aware QoS model, e-QoS, for application sessions that might across multiple protocol domains. The model provides the QoS guarantee by dynamically selecting and adapting application protocols. To the best of our knowledge, our model is the first attempt to address QoS adaptation at the application session level by
Hanping Lufei, Weisong Shi
openaire   +1 more source

When UAV Swarm Meets Edge-Cloud Computing: The QoS Perspective

IEEE Network, 2019
In this article, we propose a hybrid computing model, UAV-Edge-Cloud, bringing edge/cloud computing and UAV swarm together to achieve high quality of service (QoS) guarantees.
Wuhui Chen, Baichuan Liu, Huawei Huang, Song Guo, Zibin Zheng   +4 more
semanticscholar   +1 more source