ONLINE PERSONALIZED QOS PREDICTION APPROACH FOR CLOUD SERVICES

被引：0
|
作者：
Xu, Jianlong [1 ,3 ]
Zheng, Zibin [2 ]
Fan, Zhun [1 ]
Liu, Wenhua [3 ]
机构：
[1] Shantou Univ, Dept Elect Engn, Guangdong Prov Key Lab Digital Signal & Image Pro, Shantou 515063, Peoples R China
[2] Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Guangdong, Peoples R China
[3] Shantou Univ, Coll Sci, Shantou 515063, Peoples R China
来源：
PROCEEDINGS OF 2016 4TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (IEEE CCIS 2016) | 2016年
关键词：
Cloud service; Online learning; QoS prediction; Matrix factorization; Cloud computing;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Personalized Quality-of-Service (QoS) prediction is an indispensable technique to select suitable services for service-based cloud applications. Considering the dynamic nature of services, efficiently and accurately predicting QoS value becomes an urgent and crucial research issue. In this paper, we propose an online personalized QoS prediction approach for cloud service, namely online learning based matrix factorization (OLMF). We build the objective function of online matrix factorization and use stochastic gradient descent algorithm to solve the function. Extensive experiments are conducted on real world public datasets, which verify the effectiveness and efficiency of our proposed approach.
引用
收藏
页码：32 / 37
页数：6
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