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.

An Active Learning Approach to Build Adaptive Cost Models for Web Services / Matteo Golfarelli, Simone Graziani, Stefano Rizzi. - In: DATA & KNOWLEDGE ENGINEERING. - ISSN 0169-023X. - STAMPA. - 119:(2019), pp. 89-104. [10.1016/j.datak.2019.01.001]

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

Matteo Golfarelli;Simone Graziani;Stefano Rizzi
2019

Abstract

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.
2019
An Active Learning Approach to Build Adaptive Cost Models for Web Services / Matteo Golfarelli, Simone Graziani, Stefano Rizzi. - In: DATA & KNOWLEDGE ENGINEERING. - ISSN 0169-023X. - STAMPA. - 119:(2019), pp. 89-104. [10.1016/j.datak.2019.01.001]
Matteo Golfarelli, Simone Graziani, Stefano Rizzi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/676177
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