Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies. (10th October 2020)

• Record Type: Journal Article
• Authors: Di Sanzo, Pierangelo; Avresky, Dimiter R.; Pellegrini, Alessandro
• Is Part Of: Software, practice & experience. Volume 51:Number 1(2021)
• Page Start: 46
• Page End: 71
• Publication Date: 2020-10-10
• Subjects: autonomic computing -- cloud computing -- hybrid cloud -- proactive management -- software rejuvenation; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2908
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it might be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the cause of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article, we propose an alternative approach to cope with the problem of software anomalies in cloud‐based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques, and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed to heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards its adoption, and encouraging further studies and practical experiences to evaluate and improve it.