Data-intensive Service Mashup Based on Game Theory and Hybrid Fireworks Optimization Algorithm in the Cloud

被引：0
作者：
Yang, Wanchun [1 ,2 ]
Zhang, Chenxi [3 ]
Mu, Bin [3 ]
机构：
[1] Tongji Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China
[2] Shandong Jiaotong Univ, Sch Sci, Jinan 250357, Peoples R China
[3] Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China
来源：
INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS | 2015年 / 39卷 / 04期
关键词：
cloud computing; data-intensive; mashup; hybrid fireworks optimization algorithm; game theory; service correlation;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
End users can create kinds of mashups which combine various data-intensive services to form new services. The challenging issue of data-intensive service mashup is how to find service from a great deal of candidate services while satisfying SLAs. In this paper, Service-Level Agreement (SLA) consists of two parts, which are SLA-Q and SLA-T. SLA-Q (SLA-T) indicates the end-to-end QoS (transactional) requirements. SLA-aware service mashup problem is known as NP-hard, which takes a significant amount of time to find optimal solutions. The service correlation also exists in data-intensive service mashup problem. In this paper, the service correlation includes the functional correlation and QoS correlation. For efficiently solving the data-intensive service mashup problem with service correlation, we propose an approach GTHFOA-DSMSC (Data-intensive Service Mashup with Service Correlation based on Game Theory and Hybrid Fireworks Optimization Algorithm) which evolves a set of solutions to the Pareto optimal front. The experimental tests demonstrate the effectiveness of the algorithm.
引用
收藏
页码：421 / 429
页数：9
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