Name
Abstract | ||
|---|---|---|
In order to accurately forecast Quality of Service (QoS) of different Web Services, this paper proposes a novel QoS forecasting approach called MulA-LMRBF (Multi-step fore-casting with Advertisement and Levenberg-Marquardt improved Radial Basis Function) based on multivariate time series. Considering the correlation among different QoS attributes, we use phase-space reconstruction to map historical multivariate QoS data into a dynamic system, use Average Dimension (AD) to estimate the embedding dimension and delay time of reconstructed phase space. We also add the short-term QoS advertisement data of service provider to form a more comprehensive data set. Then, RBF (Radial Basis Function) neural network improved by the Levenberg-Marquardt (LM) algorithm is used to update the weight of the neural network dynamically, which improves the forecasting accuracy and realizes the dynamic multiple-step forecasting. The experimental results demonstrate that MulA-LMRBF is better than previous approaches in term of precision and is more suitable for multi-step forecasting. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICWS.2017.27 | 2017 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Quality of Service,Multivariate time series,Phase-space reconstruction,LM algorithm,RBF neural network,Dynamic multiple step forecasting | Data mining,Time series,Embedding,Radial basis function,Computer science,Multivariate statistics,Quality of service,Service provider,Artificial intelligence,Web service,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-0753-4 | 3 | 0.39 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengcheng Zhang | 1 | 24 | 8.52 |
| Liyan Wang | 2 | 10 | 0.81 |
| Wenrui Li | 3 | 58 | 8.98 |
| Hareton Leung | 4 | 982 | 65.63 |
| Wei Song | 5 | 51 | 9.86 |