Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models

Papadopoulos

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

et al. 2017

Self Cite

Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.

Section: Multi-objective Dynamic Bindingmentioning

confidence: 99%

Automated control of multiple software goals using multiple actuators

Papadopoulos

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

et al. 2017

Self Cite

“…DTMCs describe the probability of moving between system states and the model itself does not include any controllable actions. There are several methods employing DTMCs in the context of controlling software [10,22,24,26,27]. A typical scenario includes verifying a DTMC model of a system against a property.…”

Section: Devise the Modelmentioning

confidence: 99%

Software Engineering Meets Control Theory

2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Angelopoulos

et al. 2015

Self Cite

Abstract-The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller.This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.

“…Furthermore, parametric models are usually easy to keep updated with online tracking mechanisms such as Bayesian estimation [52], Kalman filtering [51], or other techniques for statistical learning [59]. This has a twofold benefit: (1) the model tracks changes in the system and (2) the quality of the estimates is continuously improved while the system is running, overcoming possible inaccuracies in the information collected during an initial learning phase.…”

Section: Learning and Runtime Adaptationmentioning

confidence: 99%

“…Furthermore, given the variance of the parameters is estimated too, a simple change point detection mechanism can be implemented based on the frequency of outliers in the new samples (e.g., those lying further than n times the standard deviation away from the mean). When such frequency gets too high, it is possible that the system undergone an abrupt change which invalidates the current model and requires a new learning phase [7,59].…”

Section: Multi-objective Service Dynamic Bindingmentioning

confidence: 99%

Automated multi-objective control for self-adaptive software design

Hoffmann

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

2015

Self Cite

While software is becoming more complex everyday, the requirements on its behavior are not getting any easier to satisfy. An application should offer a certain quality of service, adapt to the current environmental conditions and withstand runtime variations that were simply unpredictable during the design phase. To tackle this complexity, control theory has been proposed as a technique for managing software's dynamic behavior, obviating the need for human intervention. Control-theoretical solutions, however, are either tailored for the specific application or do not handle the complexity of multiple interacting components and multiple goals. In this paper, we develop an automated control synthesis methodology that takes, as input, the configurable software components (or knobs) and the goals to be achieved. Our approach automatically constructs a control system that manages the specified knobs and guarantees the goals are met. These claims are backed up by experimental studies on three different software applications, where we show how the proposed automated approach handles the complexity of multiple knobs and objectives.