An Integrated Platform for Distributed Resources Monitoring and Software Aging Mitigation in Private Clouds

Araújo, Jean; Braga, Céfanys; Neto, José Belmiro; Costa, Adriano Carneiro da; Matos, Rubens; Maciel, Paulo

doi:10.17706/jsw.11.10.976-993

Cited by 2 publications

(2 citation statements)

References 42 publications

(25 reference statements)

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“…Autonomicity, self-* properties, and real-time detection are also approaches which are well studied in the literature, [48][49][50][51][52] also with a special focus on resource monitoring. 53,54 Capacity allocation has also been dealt with, 55 although from the perspective of economic savings by choosing the best-suited instances. Our proposal retains all the capabilities of the aforementioned works in the literature, although we propose a general software architecture which embodies all aspects of the applications, independently of their deployments.…”

Section: Related Workmentioning

confidence: 99%

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Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Sanzo

Avresky²,

Pellegrini

2020

Softw Pract Exp

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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.

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Section: Related Workmentioning

confidence: 99%

“…Autonomicity, self‐* properties, and real‐time detection are also approaches which are well studied in the literature, 48‐52 also with a special focus on resource monitoring 53,54 . Capacity allocation has also been dealt with, 55 although from the perspective of economic savings by choosing the best‐suited instances.…”

Section: Related Workmentioning

confidence: 99%