[Paper Review] ActivFORMS: A Model-Based Approach to Engineer Self-Adaptive Systems

Danny Weyns, M. Usman Iftikhar|arXiv (Cornell University)|Aug 29, 2019

Advanced Software Engineering Methodologies70 references17 citations

TL;DR

ActivFORMS is a model-based approach that ensures correct runtime behavior of feedback loops in self-adaptive systems through formally verified models, uses statistical model checking for efficient adaptation option selection, and supports runtime updates to adaptation goals and models. It enables reliable, efficient, and dynamic self-adaptation in uncertain environments, validated in an IoT-based building security monitoring system in Leuven.

ABSTRACT

Handling change is an increasingly important challenge for software engineers. Our focus is on changes caused by uncertainties in the operating conditions of a system, such as changes in the availability of resources in a highly dynamic environment. To deal with such uncertainties, an external feedback loop system can be added to the system that collects additional data during operation to resolve the uncertainties and adapt the system to achieve particular quality requirements (i.e., adaptation goals); this approach is commonly referred to as self-adaptation. To ensure that the system complies with the adaptation goals, recent research suggests the use of formal techniques at runtime. Existing approaches have three shortcomings that limit their practical applicability: (i) they ignore correctness of the behavior of the feedback loop, (ii) they apply exhaustive verification at runtime to select adaptation options to realize the adaptation goals, which is very resource demanding, and (iii) they provide limited or no support for changing adaptation goals at runtime. To tackle these shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation). ActivFORMS: (i) provides guarantees for the correct behavior of the feedback loop with respect to a set of correctness properties at design time and preserves the guarantees at runtime by directly executing the verified models of the feedback loop, (ii) guides the adaptation of the system by selecting adaptation options that realize the adaptation goals in an efficient manner using statistical model checking at runtime, and (iii) offers basic support for changing adaptation goals and updating verified models of the feedback loop on-the-fly to meet the new goals. To validate ActivFORMS, we present a tool-supported instance of the approach that we apply to an IoT application for building security monitoring deployed in Leuven.

Motivation & Objective

• To address the limitations of existing self-adaptation approaches that lack correctness guarantees for feedback loops, incur high runtime verification costs, and offer minimal support for runtime goal changes.
• To ensure the correctness of feedback loop behavior through formal verification at design time and runtime execution of verified models.
• To reduce the computational cost of adaptation by replacing exhaustive verification with statistical model checking for selecting adaptation options.
• To enable dynamic updates to adaptation goals and verified feedback loop models at runtime to support evolving system requirements.
• To validate the approach in a real-world IoT deployment for building security monitoring in Leuven.

Proposed method

• Formal models of feedback loops are specified and verified at design time using formal techniques to guarantee correctness properties.
• Verified models are directly executed at runtime to ensure behavioral correctness of the feedback loop.
• Statistical model checking is applied at runtime to efficiently evaluate and select adaptation options that meet adaptation goals.
• The approach supports runtime modification of adaptation goals and incremental updates to verified feedback loop models to reflect new objectives.
• A tool-supported instance of ActivFORMS is implemented and evaluated on an IoT-based building security monitoring system in Leuven.

Experimental results

Research questions

• RQ1 How can feedback loop behavior in self-adaptive systems be guaranteed correct at both design time and runtime?
• RQ2 Can statistical model checking replace exhaustive verification to reduce runtime adaptation costs while ensuring adaptation goal compliance?
• RQ3 To what extent can adaptation goals and feedback loop models be updated dynamically at runtime without compromising correctness?
• RQ4 How effective is ActivFORMS in enabling efficient and correct self-adaptation in a real-world IoT deployment?

Key findings

• ActivFORMS ensures the correctness of feedback loop behavior through formal verification at design time and runtime execution of verified models.
• Statistical model checking enables efficient selection of adaptation options, significantly reducing the computational overhead compared to exhaustive verification.
• The approach supports runtime updates to adaptation goals and verified feedback loop models, enabling dynamic adaptation to changing requirements.
• The tool-supported implementation of ActivFORMS was successfully applied to an IoT-based building security monitoring system in Leuven, demonstrating practical applicability.