Esadsa: Enhanced self-adaptive dynamic software architecture

Abstract

With the advent of new technologies and the trend in integration of related business, the software development has become very complex. However, complex systems are realized due to distributed computing technologies like Web services. With machine-to-machine (M2 M) interaction, human intervention is greatly reduced in distributed applications. Nevertheless, there is need for continuous changes in complex software systems. Manual incorporation of changes is both time consuming and tedious task. The self-adaptive features of software can cater to the needs of ad hoc demands pertaining to changes. Therefore, it is desirable to have a self-adaptive software architecture for distributed systems to adapt to changes automatically without traditional reengineering process involved in software update. The existing solutions do have limitations in self-adaptation and need human intervention. Rainbow is one of the examples for self-adaptive dynamic software architecture. However, it does not have knowledge mining and quality of software analysis for further improvements. It is essential to have such enhancements in the wake of self-adaptive systems of enterprises producing huge amount of data related to operations, service quality and other information required for analysing the architecture. We proposed a self-adaptive dynamic software architecture named enhanced self-adaptive dynamic software architecture (ESADSA) which is influenced by Rainbow. It incorporates modules such as QoS analyser and knowledge miner with two data mining algorithms for enhancing capabilities of the architecture. ESADSA decouples self-adaptation from target system by preserving cohesion of target system with loosely coupled interaction. A real-time case study is considered for proof of the concept. The experimental results revealed significant improvements in dynamic self-adaptation of the proposed architecture.