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An Architectural View Type for Elasticity Modeling and Simulation -- The Slingshot Approach

Floriment Klinaku, Sarah Sophie Stieß, Alireza Hakamian, Steffen Becker

TL;DR

The paper tackles the difficulty of designing and evaluating elasticity policies at design time for cloud-native architectures. It introduces the Slingshot approach, comprising the SPD metamodel, a dedicated visual notation, and simulation semantics that integrate with Palladio to model and simulate elasticity through scalable policies. Empirical evaluation across two single-case experiments and a user study demonstrates that SPD-based simulations can achieve meaningful predictive power (best-case mean absolute error around a low 20s percent) and substantial efficiency gains over load testing, while highlighting remaining challenges in modeling productivity and accuracy for complex, large-scale systems. The work offers a practical, policy-driven framework for architecting elasticity that improves design-time analysis and decision making, with clear directions for extending support to learning-based policies and broader elasticity paradigms.

Abstract

The cloud computing model enables the on-demand provisioning of computing resources, reducing manual management, increasing efficiency, and improving environmental impact. Software architects now play a strategic role in designing and deploying elasticity policies for automated resource management. However, creating policies that meet performance and cost objectives is complex. Existing approaches, often relying on formal models like Queueing Theory, require advanced skills and lack specific methods for representing elasticity within architectural models. This paper introduces an architectural view type for modeling and simulating elasticity, supported by the Scaling Policy Definition (SPD) modeling language, a visual notation, and precise simulation semantics. The view type is integrated into the Palladio ecosystem, providing both conceptual and tool-based support. We evaluate the approach through two single-case experiments and a user study. In the first experiment, simulations of elasticity policies demonstrate sufficient accuracy when compared to load tests, showing the utility of simulations for evaluating elasticity. The second experiment confirms feasibility for larger applications, though with increased simulation times. The user study shows that participants completed 90% of tasks, rated the usability at 71%, and achieved an average score of 76% in nearly half the allocated time. However, the empirical evidence suggests that modeling with this architectural view requires more time than modeling control flow, resource environments, or usage profiles, despite its benefits for elasticity policy design and evaluation.

An Architectural View Type for Elasticity Modeling and Simulation -- The Slingshot Approach

TL;DR

The paper tackles the difficulty of designing and evaluating elasticity policies at design time for cloud-native architectures. It introduces the Slingshot approach, comprising the SPD metamodel, a dedicated visual notation, and simulation semantics that integrate with Palladio to model and simulate elasticity through scalable policies. Empirical evaluation across two single-case experiments and a user study demonstrates that SPD-based simulations can achieve meaningful predictive power (best-case mean absolute error around a low 20s percent) and substantial efficiency gains over load testing, while highlighting remaining challenges in modeling productivity and accuracy for complex, large-scale systems. The work offers a practical, policy-driven framework for architecting elasticity that improves design-time analysis and decision making, with clear directions for extending support to learning-based policies and broader elasticity paradigms.

Abstract

The cloud computing model enables the on-demand provisioning of computing resources, reducing manual management, increasing efficiency, and improving environmental impact. Software architects now play a strategic role in designing and deploying elasticity policies for automated resource management. However, creating policies that meet performance and cost objectives is complex. Existing approaches, often relying on formal models like Queueing Theory, require advanced skills and lack specific methods for representing elasticity within architectural models. This paper introduces an architectural view type for modeling and simulating elasticity, supported by the Scaling Policy Definition (SPD) modeling language, a visual notation, and precise simulation semantics. The view type is integrated into the Palladio ecosystem, providing both conceptual and tool-based support. We evaluate the approach through two single-case experiments and a user study. In the first experiment, simulations of elasticity policies demonstrate sufficient accuracy when compared to load tests, showing the utility of simulations for evaluating elasticity. The second experiment confirms feasibility for larger applications, though with increased simulation times. The user study shows that participants completed 90% of tasks, rated the usability at 71%, and achieved an average score of 76% in nearly half the allocated time. However, the empirical evidence suggests that modeling with this architectural view requires more time than modeling control flow, resource environments, or usage profiles, despite its benefits for elasticity policy design and evaluation.

Paper Structure

This paper contains 33 sections, 1 equation, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Running Example
  3. Elasticity Scenario
  4. Elasticity Concerns
  5. Foundations
  6. The Palladio Approach
  7. Elasticity Concerns
  8. Answering Elasticity Concerns with Palladio
  9. Methodology
  10. Problem Understanding and Design Goals
  11. Solution Design
  12. Evaluation Design
  13. The Elasticity Architectural View Type---The Slingshot Approach
  14. SPD Metamodel
  15. Target Groups.
  16. ...and 18 more sections

Figures (13)

  • Figure 1: Architecture of the Remote Measuring Use Case.
  • Figure 2: Send Data Elasticity Scenario.
  • Figure 3: Modeling Views in Palladio for the Running Example.
  • Figure 4: Conceptual architectural model for a cloud-native application based on Weyns conceptual model weyns2020introduction.
  • Figure 5: Simulation of an Elasticity Policy based on Palladio.
  • ...and 8 more figures

Theorems & Definitions (2)

  • Definition 1: Self-Adaptive System Reconfiguration Space
  • Definition 2: Self-Adaptive System State