Robust resource demand estimation using hierarchical Bayesian model in a distributed service system

Robust resource demand prediction is crucial for efficient allocation of resources to service requests in a distributed service delivery system. There are two problems in resource demand prediction: firstly to estimate the volume of service requests that come in at different time points and at different geo-locations, secondly to estimate the resource demand given the estimated volume of service requests. While a lot of literature exists to address the first problem, in this work, we have proposed a data-driven statistical method for robust resource demand prediction to address the second problem. The method automates the identification of various system operational characteristics and contributing factors that influence the system behavior to generate an adaptive low variance resource demand prediction model. Factors can be either continuous or categorical in nature. The method assumes that each service request resolution involves multiple tasks. Each task is composed of multiple activities. Each task belongs to a task type, based on the type of the resource it requires to resolve that task. Our method supports configurable tasks per service request, and configurable activities per task. The demand prediction model produces an aggregated resource demand required to resolve all the activities under a task by activity sequence modeling; and aggregated resource demand by resource type, required to resolve all the activities under a service request by task sequence modeling.