Improving Application Management Services through Ticket Data Clustering

Improving Application Management Services through Ticket Data Clustering

Ying Li (IBM T. J. Watson Research Center, USA)
DOI: 10.4018/978-1-4666-8496-6.ch005
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Abstract

In the area of Application Management Services (AMS), good resource planning and effective cross-skill training are critical to success. Meeting these objectives would require systematic and repeatable approaches for determining the best way of forming resource pools and identifying who to train for what skills under a constrained budget. This chapter presents a methodology that aims to achieve above goals based on an optimal clustering of service request data (aka. ticket data). Specifically, tickets that require similar problem-solving skills are first clustered using a statistical clustering technique into groups, which are then used to assist resource pooling and cross-skill training plan generation. Preliminary results have shown that an average 40% of resource saving can be achieved in our simulation scenario, while maintaining the same Service-Level Agreements (SLA) with the customer. Encouraging feedback on the cross-skill training recommendation has also been received from several real AMS customers.
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Background

To our best knowledge, resource planning in AMS has not received much attention. This is most likely because that application management as a service is relatively new. As it matures as a service, a disciplined approach to managing it and the science required to do so will also emerge and evolve. Nevertheless, we will mention below a few of the scientific publications that appeared in the literature.

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