Abstract

From self-driving cars to self-adaptive websites, the world is increasingly becoming more reliant on autonomous systems. Similar to many other domains, the system's behavior is often determined by its requirements. For example, a self-adaptive web service is likely to have some maximum value that response time should not surpass. To maintain this requirement, the system uses tactics, which may include activating additional computing resources. In real-world environments, tactics will frequently experience volatility, known as tactic volatility. This can include unstable time required to execute the tactic or frequent fluctuations in the cost to execute the tactic. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics have static attributes.

To address the limitations in current processes, we propose a Tactic Volatility Aware (TVA) solution. Our approach focuses on providing a volatility aware solution that enables the system to properly maintain requirements. Specifically, TVA utilizes a Autoregressive Integrated Moving Average Model (ARIMA) to estimate potential future values for requirements, while also using a Multiple Regression Analysis (MRA) model to make predictions of tactic latency and tactic cost at runtime. This enables the system to both better estimate the true behavior of its tactics and it allows the system to properly maintain its requirements. Using data containing real-world volatility, we demonstrate the effectiveness of using TVA with both statistical analysis methods and self-adaptive experiments. In this work, we demonstrate (I) The negative impact of not accounting for tactic volatility (II) The benefits of a ARIMA-modeling approach in monitoring system requirements (III) The effectiveness of MRA in predicting tactic volatility (IV) The overall benefits of TVA to the self-adaptive process. This work also presents the first known publicly available dataset of real-world tactic volatility in terms of both cost and latency.

Library of Congress Subject Headings

Self-adaptive software; Machine learning

Publication Date

4-2019

Document Type

Thesis

Student Type

Graduate

Degree Name

Software Engineering (MS)

Department, Program, or Center

Software Engineering (GCCIS)

Advisor

Daniel Krutz

Advisor/Committee Member

Qi Yu

Advisor/Committee Member

Travis Desell

Campus

RIT – Main Campus

Plan Codes

SOFTENG-MS

Download

Share

COinS