A Generalized Nesterov-Accelerated Hessian-Vector-Based Latent Factor Analysis Model for QoS Prediction

Weiling Li, Xin Luo, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

User-side Quality-of-Service (QoS) data are vital for efficient cloud service selection, while available QoS data in real applications are commonly described by a high-dimensional and Sparse (HiDS) matrix due to a) increasing users and services, and b) the impossibility of observing the full invoking mapping among users and services. A latent factor analysis (LFA) model has proven to be efficient in performing representation learning on such an HiDS QoS matrix. However, existing LFA models commonly adopt a first-order optimizer that cannot makes it approach the second-order stationary point of the learning objective, thereby resulting in accuracy loss. Aiming at addressing this issue, this study proposes to incorporate a Generalized Nesterov's Acceleration (GNA) method in to a Hessian-vector algorithm for LFA, thereby establishing a GNA-incorporated Hessian-vector-based LFA (GNHL) model with two-fold ideas: A) adopting the principle of a Hessian-vector method to acquire a proper Newton step efficiently, and b) incorporating a GNA method into its linear search for accelerating its convergence rate. Experimental results on six real QoS datasets demonstrate that a GNHL model outperforms state-of-The-Art LFA models in generating highly accurate predictions for missing QoS data with low computational burden.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 14th International Conference on Cloud Computing, CLOUD 2021
EditorsClaudio Agostino Ardagna, Carl K. Chang, Ernesto Daminai, Rajiv Ranjan, Zhongjie Wang, Robert Ward, Jia Zhang, Wensheng Zhang
PublisherIEEE Computer Society
Pages200-205
Number of pages6
ISBN (Electronic)9781665400602
DOIs
StatePublished - Sep 2021
Event14th IEEE International Conference on Cloud Computing, CLOUD 2021 - Virtual, Online, United States
Duration: Sep 5 2021Sep 11 2021

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2021-September
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Conference

Conference14th IEEE International Conference on Cloud Computing, CLOUD 2021
Country/TerritoryUnited States
CityVirtual, Online
Period9/5/219/11/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

Keywords

  • Cloud Service
  • Gauss-Newton approximation
  • Generalized Nesterov's acceleration
  • Latent factor analysis
  • Machine learning
  • Quality-of-Service (QoS)

Fingerprint

Dive into the research topics of 'A Generalized Nesterov-Accelerated Hessian-Vector-Based Latent Factor Analysis Model for QoS Prediction'. Together they form a unique fingerprint.

Cite this