CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

TL;DR

CloudHealth introduces a model-driven approach for monitoring cloud service health, enabling operators to relate KPIs to high-level goals, derive metrics, and visualize system health effectively.

Contribution

It presents a novel model-based framework for operationalizing cloud health monitoring goals through goal decomposition, metric derivation, and dynamic data visualization.

Findings

Effective relation of KPIs to high-level health goals

Dynamic collection and visualization of cloud health metrics

Early results demonstrate improved monitoring capabilities

Abstract

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.