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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Branch, J.W. Yixin Diao Shwartz, L. |
| Copyright Year | 2014 |
| Description | Author affiliation: IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA (Branch, J.W.; Yixin Diao; Shwartz, L.) |
| Abstract | Predicting IT infrastructure performance under varying conditions, e.g., the addition of a new server or increased transaction loads, has become a typical IT management exercise. However, within a service delivery context, enterprise clients are demanding predictive analytics that outline future “costs” associated with changing conditions. The service delivery staffing costs incurred in addressing problems and requests (arriving in the form of incident and other problem tickets) in the managed environment is especially of high importance. This paper describes an analytical study addressing such cost prediction. Specifically, a novel approach is described in which support vector regression is used to predict service delivery workloads (measured by ticket volumes) based on managed server characteristics Additionally, a proposed framework combining various analytical models is proposed to predict service delivery staffing requirements under changing IT infrastructure characteristics and conditions. Detailed descriptions of the workload prediction techniques, as well as an evaluation using data from an actual large service delivery engagement, are presented. |
| Sponsorship | IEEE Commun. Soc. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 323081 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479909131 |
| DOI | 10.1109/NOMS.2014.6838266 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-05-05 |
| Publisher Place | Poland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Servers Support vector machines Analytical models Predictive models Complexity theory Monitoring Load modeling performance analysis machine learning predictive models |
| Content Type | Text |
| Resource Type | Article |
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