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.

中文翻译：

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云应用的自主复兴作为软件异常的对策

计算机系统中的故障通常可以追溯到各种软件异常。在许多情况下，执行广泛的调试活动以发现异常的原因并消除它们可能是困难的、不可行的或无利可图的。在其他情况下，采取纠正措施可能会导致意外的服务停机。在本文中，我们提出了一种替代方法来处理基于云的应用程序中的软件异常问题，并提出了实现我们方法的分布式自治框架的设计。它利用云基础设施的弹性能力，并依赖于机器学习模型、主动恢复技术和新的负载平衡方法。通过将所有这些元素放在一起，我们表明，可以提高部署到异构云区域并遭受频繁故障的应用程序的可用性和性能。总的来说，我们的研究证明了我们方法的可行性，从而为采用它开辟了道路，并鼓励进一步的研究和实践经验来评估和改进它。