Incorporating architecture-based self-adaptation into an adaptive industrial software system

Abstract

Complex software-intensive systems are increasingly relied upon for all kinds of activities in society, leading to the requirement that these systems should be resilient to changes that may occur to the system, its environment, or its goals.Traditionally, resilience has been achieved either through: (i) low-level mechanisms embedded in the implementation (e.g., exception handling, timeouts, redundancies), which are unable to detect subtle but important anomalies (e.g., progressive performance degradation); or (ii) human oversight, which is costly and unreliable. Architecture-based self-adaptation (ABSA) is regarded as a promising approach to improve the resilience and reduce the development/operation costs of such systems. Although researchers have illustrated the benefits of ABSA through a number of small-scale case studies, it remains to be seen whether ABSA is truly effective in handling changes at run-time in industrialscale systems. In this paper, we report on our experience applying an ABSA framework (Rainbow) to a large-scale commercial software system, called Data Acquisition and Control Service (DCAS), which is used to monitor and manage highly populated networks of devices in renewable energy production plants. In 4 Critical Software, Portugal. Preprint submitted to Journal of L A T E X Templates December 28, 2014Submitted for publication the approach followed, we have replaced some of the existing adaptive mechanisms embedded in DCAS by those advocated by ABSA proponents. This has allowed us to assess the development costs associated with the reengineering of adaptive mechanisms when using an ABSA solution, and to make effective comparisons, in terms of operational performance, between a baseline industrial system and one that uses ABSA. Our results show that using the ABSA concepts as embodied in Rainbow enabled an independent team of developers to: (i) effectively implement the adaptation behavior required from such industrial systems; and (ii) obtain important benefits in terms of maintainability and extensibility of adaptation mechanisms.

Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling

“…The Rainbow framework (Garlan et al 2004) is a reusable software architecture for engineering systems that are self-adaptive at runtime. It aims to minimize the design efforts by providing a customizable framework that can be employed in different domains (Cámara et al 2016).…”

“…The Rainbow framework employs the notation architectural style to distinguish system-specific knowledge from commonalities between systems. This facilitates customization by determining four customization points (Cámara et al 2016):…”

Adaptive and reconfigurable software systems and architectures

“…The work of Camara et al (2016) presents an experience feedback on the re-engineering of an industrial system so that it supports architecture-based self-adaptation. This system, called DCAS, is a cyber-physical system whose main functional goal is to monitor physical devices and manage data requested from these devices.…”

Reasoning about Human Participation in Self-Adaptive Systems

“…We assume a model of adaptation that represents adaptation knowledge using the following high-level concepts: 4 1) Tactic: is a primitive action that corresponds to a single step of adaptation. For instance, in DCAS we can specify pairs of tactics with opposing effects for (de)activating processor nodes, or scale up/down the number of DRPPs active in a given processor node.…”

“…In this paper, we refer to an automated version of scale out implemented on a Rainbow-based prototype of DCAS[4].…”