An Active Learning Approach to Build Adaptive Cost Models for Web Services

Delivering accurate estimates of query costs in web services is important in different contexts, e.g., to measure their Quality of Service. However, building a reliable cost model is difficult as (i) a web service is a black box often hiding a complex computation, (ii) a call to the same service can yield completely different costs by simply changing a parameter value, and (iii) execution costs can drift with time. In this paper we propose Tiresias, an approach that, given a web service exposing an interface with a fixed number of parameters, initializes and actively adapts a model to accurately predict query costs. The cost model is represented by a regression tree trained through two interleaved querying cycles: a passive one, where the costs measured for user-generated queries are used to update the tree, and an active one, where the service is probed through system-generated queries to cope with drifts in the cost function. Tiresias is finally evaluated in terms of effectiveness and efficiency through a set of experimental tests performed on both real and synthetic datasets.