Emergent Web Server:An Exemplar to Explore Online Learning in Compositional Self-Adaptive Systems

Rodrigues Filho, Roberto and Alberts, Elvin and Gerostathopoulos, Ilias and Porter, Barry and Costa, Fábio (2022) Emergent Web Server:An Exemplar to Explore Online Learning in Compositional Self-Adaptive Systems. In: SEAMS '22. ACM, New York, pp. 36-42. ISBN 9781450393058

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Abstract

Contemporary deployment environments are volatile, with conditions that are often hard to predict in advance, demanding solutions that are able to learn how best to design a system at runtime from a set of available alternatives. While the self-adaptive systems community has devoted significant attention to online learning, there is less research specifically directed towards learning for open-ended architectural adaptation - where individual components represent alternatives that can be added and removed dynamically. In this paper we present the Emergent Web Server (EWS), an architecture-based adaptive web server with 42 unique compositions of alternative components that present different utility when subjected to different workload patterns. This artefact allows the exploration of online learning techniques that are specifically able to consider the composition of logic that comprises a given system, and how each piece of logic contributes to overall utility. It also allows the user to add new components at runtime (and so produce new composition options), and to remove existing components; both are likely to occur in systems where developers (or automated code generators) deploy new code on a continuous basis and identify code which has never performed well. Our exemplar bundles together a fully-functional web server, a number of pre-packaged online learning approaches, and utilities to integrate, evaluate, and compare new online learning approaches.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SEAMS '22: Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems http://doi.acm.org/10.1145/3524844.3528079
ID Code:
167214
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Deposited On:
01 Nov 2022 16:20
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Nov 2022 17:51