RAD: Detecting Performance Anomalies in Cloud-based Web Services

Web services hosted on public cloud platforms are often subjected to performance anomalies. Runtime detection of such anomalies is crucial for operations in cloud data centers. With ever-increasing data center size, complexities in software applications and dynamic traffic workload patterns, automatically detecting performance anomalies is a challenging task. In this paper, we propose RAD, a lightweight runtime anomaly detection technique that does not require application level instrumentation and can be easily implemented for detecting anomalies in multi-tier cloud-based Web services. In particular, we focus on anomalies that are difficult to detect by simply monitoring system level metrics alone, such as anomalies that are caused by contention from within a service and also those caused by shared resource contention by other services running on the cloud. RAD continuously monitors service resource metrics and uses a queuing network model to detect performance anomalies at runtime. Additionally, RAD uses historical data and implements a statistical methodology to diagnose the root cause of an anomaly. We evaluate RAD on a private cloud and also on the EC2 public cloud platform to show that RAD incurs extremely low levels of performance overhead on the service and is effective for detecting anomalies in both multi-tier monolithic services and microservices.