Monitoring Elastically Adaptive Multi-Cloud Services

Abstract

Automatic resource provisioning is a challenging and complex task. It requires for applications, services and underlying platforms to be continuously monitored at multiple levels and time intervals. The complex nature of this task lays in the ability of the monitoring system to automatically detect runtime configurations in a cloud service due to elasticity action enforcement. Moreover, with the adoption of open cloud standards and library stacks, cloud consumers are now able to migrate their applications or even distribute them across multiple cloud domains. However, current cloud monitoring tools are either bounded to specific cloud platforms or limit their portability to provide elasticity support. In this article, we describe the challenges when monitoring elastically adaptive multi-cloud services. We then introduce a novel automated, modular, multi-layer and portable cloud monitoring framework. Experiments on multiple clouds and real-life applications show that our framework is capable of automatically adapting when elasticity actions are enforced to either the cloud service or to the monitoring topology. Furthermore, it is recoverable from faults introduced in the monitoring configuration with proven scalability and low runtime footprint. Most importantly, our framework is able to reduce network traffic by 41 percent, and consequently the monitoring cost, which is both billable and noticeable in large-scale multi-cloud services.