DocumentCode :
633091
Title :
Storage Mining: Where IT Management Meets Big Data Analytics
Author :
Yang Song ; Alatorre, Gabriel ; Mandagere, Nagapramod ; Singh, Ashutosh
Author_Institution :
IBM Almaden Res. Center, San Jose, CA, USA
fYear :
2013
fDate :
June 27 2013-July 2 2013
Firstpage :
421
Lastpage :
422
Abstract :
The emerging paradigm shift to cloud based data center infrastructures imposes remarkable challenges to IT management operations, e.g., due to virtualization techniques and more stringent requirements for cost and efficiency. On one hand, the voluminous data generated by daily IT operations such as logs and performance measurements contain abundant information and insights which can be leveraged to assist the IT management. On the other hand, traditional IT management solutions cannot consume and exploit the rich information contained in the data due to the daunting volume, velocity, variety, as well as the lack of scalable data mining and machine learning frameworks to extract insights from such raw data. In this paper, we present our on-going research thrust of designing novel IT management solutions by leveraging big data analytics frameworks. As an example, we introduce our project of Storage Mining, which exploits big data analytics techniques to facilitate storage cloud management. The challenges are discussed and our proof-of-concept big data analytics framework is presented.
Keywords :
cloud computing; computer centres; data analysis; data mining; storage management; IT management operations; IT management solutions; big data analytics framework; cloud based data center infrastructures; storage cloud management; storage mining; Analytical models; Data handling; Data mining; Data storage systems; Information management; Predictive models; Training; Big Data Analytics; IT Management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
Type :
conf
DOI :
10.1109/BigData.Congress.2013.66
Filename :
6597170
Link To Document :
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