Abstract
As modern software-intensive systems become larger, more complex, and more customizable, it is desirable to optimize their functionality by runtime adaptations. However, in most cases it is infeasible to fully model and predict their behavior in advance, which is a classical requirement of runtime self-adaptation. To address this problem, we propose their self-adaptation based on a sequence of online experiments carried out in a production environment. The key idea is to evaluate each experiment by data analysis and determine the next potential experiment via an optimization strategy. The feasibility of the approach is illustrated on a use case devoted to online self-adaptation of traffic navigation where Bayesian optimization, grid search, and local search are employed as the optimization strategies. Furthermore, the cost of the experiments is discussed and three key cost components are examined—time cost, adaptation cost, and endurability cost.
Highlights
Large software-intensive systems (LSIS) are becoming more dynamic, adaptive, and data-driven
Instead of creating such models, we propose to self-adapt LSIS to meet an optimization goal at runtime via automated online experiment-driven adaptation (AOEDA)
We identify three key components of experimentation cost: (i) Cost related to the time needed for an optimization round of the LSIS in question; (ii) cost of applying a new configuration to the LSIS; (iii) cost related to the user dissatisfaction potentially caused by a new configuration
Summary
Large software-intensive systems (LSIS) are becoming more dynamic, adaptive, and data-driven. Traffic density to adaptation actions such as setting dynamic speed limits or opening and closing extra lanes, would be needed This would be a coarse-grained, empirically constructed model. It is a challenge to tailor such a model to the specifics of a particular LSIS and keep it continuously updated in face of changes in the environment (consider, e.g., a highway close to a city busy with commuters and one with detours in a hilly remote area) Instead of creating such models, we propose to self-adapt LSIS to meet an optimization goal at runtime via automated online experiment-driven adaptation (AOEDA).
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