DocumentCode :
717099
Title :
Automated business application discovery
Author :
Nidd, Michael ; Kun Bai ; Jinho Hwang ; Vukovic, Maja ; Tacci, Michael
Author_Institution :
Zurich Lab., IBM Res., Zurich, Switzerland
fYear :
2015
fDate :
11-15 May 2015
Firstpage :
794
Lastpage :
797
Abstract :
When planning a data center migration it is critical to discover the client´s business applications and on which devices (server, storage and appliances) those applications are deployed in the infrastructure. It is also important to understand the dependencies the applications have on the infrastructure, on other applications, and in some cases on systems external to the client. Clients can only rarely provide that information in a complete and accurate manner. The usual approach then has been to obtain the information by asking the client´s application and platform owners a series of questions but in most cases clients do not have the tools or skills to acquire the requested information. The lack of accurate information leads to project delays, increased cost and higher levels of risk. In this paper we present an algorithm and tools for programmatically identifying and locating business application instances in an infrastructure, based on weighted similarity metric. We discuss results from our preliminary evaluation and the correctness of the algorithm. Such automated approach to application discovery significantly helps clients to achieve their project objectives and timeline without imposing additional work on the application and platform owners.
Keywords :
cloud computing; commerce; computer centres; planning; automated business application discovery; data center migration; planning; project delays; Business; Clustering algorithms; Interviews; Measurement; Planning; Servers; XML;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on
Conference_Location :
Ottawa, ON
Type :
conf
DOI :
10.1109/INM.2015.7140378
Filename :
7140378
Link To Document :
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