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
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.
Matteo Golfarelli, Simone Graziani, Stefano Rizzi
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/676177
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact