Machine Learning for Achieving Self-* Properties and Seamless Execution of Applications in the Cloud

Software anomalies are recognized as a major problem affecting the performance and availability of many computer systems. Accumulation of anomalies of different nature, such as memory leaks and unterminated threads, may lead the system to both fail or work with suboptimal performance levels. This problem particularly affects web servers, where hosted applications are typically intended to continuously run, thus incrementing the probability, therefore the associated effects, of accumulation of anomalies. Given the unpredictability of occurrence of anomalies, continuous system monitoring would be required to detect possible system failures and/or excessive performance degradation in order to timely start some recovering procedure. In this paper, we present a Machine Learning-based framework for proactive management of client-server applications in the cloud. Through optimized Machine Learning models and continually measuring system features, the framework predicts the remaining time to the occurrence of some unexpected event (system failure, service level agreement violation, etc.) of a virtual machine hosting a server instance of the application. The framework is able to manage virtual machines in the presence of different types anomalies and with different anomaly occurrence patterns. We show the effectiveness of the proposed solution by presenting results of a set of experiments we carried out in the context of a real world-inspired scenario

Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework

Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning

In this paper, we present a novel framework for supporting the management and optimization of application subject to software anomalies and deployed on large scale cloud architectures, composed of different geographically distributed cloud regions. The framework uses machine learning models for predicting failures caused by accumulation of anomalies. It introduces a novel workload balancing approach and a proactive system scale up/scale down technique. We developed a prototype of the framework and present some experiments for validating the applicability of the proposed approache

Machine Learning for Achieving Self-* Properties and Seamless Execution of Applications in the Cloud

Software anomalies are recognized as a major problem affecting the performance and availability of many computer systems. Accumulation of anomalies of different nature, such as memory leaks and unterminated threads, may lead the system to both fail or work with suboptimal performance levels. This problem particularly affects web servers, where hosted applications are typically intended to continuously run, thus incrementing the probability, therefore the associated effects, of accumulation of anomalies. Given the unpredictability of occurrence of anomalies, continuous system monitoring would be required to detect possible system failures and/or excessive performance degradation in order to timely start some recovering procedure. In this paper, we present a Machine Learning-based framework for proactive management of client-server applications in the cloud. Through optimized Machine Learning models and continually measuring system features, the framework predicts the remaining time to the occurrence of some unexpected event (system failure, service level agreement violation, etc.) of a virtual machine hosting a server instance of the application. The framework is able to manage virtual machines in the presence of different types anomalies and with different anomaly occurrence patterns. We show the effectiveness of the proposed solution by presenting results of a set of experiments we carried out in the context of a real world-inspired scenario