Case Study - API Capacity Management for Aviva Plc. (March – October 2018)

Summary	Capacity management / improvement for a key UK insurance industry API
Key Outcomes	Analytic understanding of system performance and demand, leading to first unified, predictive capacity model. Minimal issues through seasonal peak.
Key Challenges	Complex multi-party interactions. Conflicts with other major change programmes.
Key Technologies	Splunk, JBoss Fuse, Microsoft SQL/Server, Selenium WebDriver and LoadRunner, Jira/Confluence

Rating as a Service (RaaS) provides quotation services to the UK motor and home insurance industry. It runs complex multi-party processing at high volumes and subject to tight performance targets. There was general concern that the system might not cope with rising demand, but little formal analysis of the true demand expectations, or the system’s capacity to meet them. The impact of planned hardware upgrades and a cloud migration were not understood.

I addressed this by defining a formal set of non-functional requirements, analysing the system’s performance, and creating a detailed analytical model of the demand profile, incorporating seasonal and temporal patterns and the impact of planned business changes and legacy migrations. All these come together in a model which portrays on one page both the progression of projected system demand, and the concurrent system capacity.

Armed with this and other analytical tools I was able to quantify and escalate the business risks, identify demand management actions such as the removal of low-value, high-volume business streams, refine non-functional testing so that it provided an accurate indication of true system capacity, and highlight action areas for capacity improvement.

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