Name
Abstract | ||
|---|---|---|
Discovering application configurations and dependencies in the existing runtime environment is a critical prerequisite to the success of cloud migration, which attracts many attentions from both researchers and commercial vendors. However, the high complexity and diversity of enterprise applications as well as their runtime environment challenge the existing approaches which generally depend on the pre-built domain specific knowledge. In this paper, we propose a generic framework for application configuration discovery which can be applied even when the domain knowledge is missing or incomplete. We design a generic approach to significantly narrow down the configuration discovery scale based on the iterative comparison and enable users to manually identify configurations from reasonable scaled file sets with semantic tags. To maximize automation, we further design an easy extensible and pluggable knowledge base to assist configuration discovery. Through extensive case study, the capability and efficiency of our framework have been demonstrated. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CLOUD.2013.16 | IEEE CLOUD |
Keywords | Field | DocType |
application configuration,discovering application configuration,pluggable knowledge base,application configuration discovery,configuration discovery,existing approach,scaled file sets,specific knowledge,domain specific knowledge,existing runtime environment,semantic tags,cloud migration,configuration discovery scale,knowledge,enterprise applications,cloud computing,enterprise application,generic framework,discovery,domain knowledge,pluggable knowledge | Domain knowledge,Computer science,Automation,Semantic HTML,Knowledge base,Extensibility,Distributed computing,Cloud computing | Conference |
ISSN | ISBN | Citations |
2159-6182 | 978-0-7695-5028-2 | 4 |
PageRank | References | Authors |
0.46 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Jing Meng | 1 | 25 | 5.02 |
| Xuejun Zhuo | 2 | 256 | 13.91 |
| Bo Yang | 3 | 7 | 0.92 |
| Jing Min Xu | 4 | 67 | 10.98 |
| Pu Jin | 5 | 7 | 0.92 |
| Ajay Apte | 6 | 84 | 12.07 |
| Joe Wigglesworth | 7 | 4 | 1.14 |