A Probabilistic Process Learning Approach for Service Composition in Cloud Networks

被引：0

作者：

Al Ridhawi, Ismaeel ^{[1
]}

Kotb, Yehia ^{[1
]}

Aloqaily, Moayad ^{[2
]}

Kantarci, Burak ^{[2
]}

机构：

[1] Amer Univ Middle East AUM, Coll Engn & Technol, Egaila, Kuwait

[2] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON, Canada

来源：

2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2017年

关键词：

Workflow-net; Petri-net; service composition; overlay networks; cloud networks;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We present a formal probabilistic framework for process learning to compose service specific overlays (SSO) in cloud networks. The approach provides a learning mechanism that relies on previous composition results to build service composition process models that can be adopted for future composition requests. The process is then translated into a workflow-net to provide guaranteed delivery of requested cloud media services to clients. A mathematical merge technique is also presented to converge multiple process threads into a single composed process. We provide simulation results to show that our approach can adequately establish sound composition paths in a timely manner.

引用

页数：6

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