Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?

TL;DR

LiDOS introduces a lifelong dynamic optimization framework for self-adaptive systems, formulating planning as a multi-modal problem to effectively handle environment changes and outperform existing methods.

Contribution

The paper presents LiDOS, a novel lifelong optimization approach that explicitly incorporates environment dynamics into SAS planning, improving performance and speed.

Findings

LiDOS outperforms stationary planners with up to 10x improvement.

LiDOS achieves 1.4x to 10x faster plan generation.

Experimental validation on three real-world SASs confirms effectiveness.

Abstract

When faced with changing environment, highly configurable software systems need to dynamically search for promising adaptation plan that keeps the best possible performance, e.g., higher throughput or smaller latency -- a typical planning problem for self-adaptive systems (SASs). However, given the rugged and complex search landscape with multiple local optima, such a SAS planning is challenging especially in dynamic environments. In this paper, we propose LiDOS, a lifelong dynamic optimization framework for SAS planning. What makes LiDOS unique is that to handle the "dynamic", we formulate the SAS planning as a multi-modal optimization problem, aiming to preserve the useful information for better dealing with the local optima issue under dynamic environment changes. This differs from existing planners in that the "dynamic" is not explicitly handled during the search process in planning. As such, the search and planning in LiDOS run continuously over the lifetime of SAS, terminating only when it is taken offline or the search space has been covered under an environment. Experimental results on three real-world SASs show that the concept of explicitly handling dynamic as part of the search in the SAS planning is effective, as LiDOS outperforms its stationary counterpart overall with up to 10x improvement. It also achieves better results in general over state-of-the-art planners and with 1.4x to 10x speedup on generating promising adaptation plans.