Modern cloud-native applications are adopting the microservice architecture in which applications are deployed in lightweight containers that run inside a virtual machine (VM). Containers running different services are often co-located inside the same virtual machine. While this enables better resource optimization, it can cause interference among applications. This can lead to performance degradation. Detecting the cause of performance degradation at runtime is crucial to decide the correct remediation action such as, but not limited to, scaling or migrating. We propose a non-intrusive detection technique that differentiates between degradation caused by load and by interference. First, we define an operational zone for the application. Then we define a disambiguation method that uses models to classify interference and normal load. In contrast to previous work, our proposed detection technique does not require intrusive application instrumentation and incurs minimal performance overhead. We demonstrate how we can design effective Machine Learning models that can be generalized to detect interference from different types of applications. We evaluate our technique using realistic microservice benchmarks on AWS EC2. The results show that our approach outperforms existing interference detection techniques in F_1 score by at least 2.75% and at most 53.86%.

Cloud-native applications are often composed of lightweight containers and conform to the microservice architecture. Cloud providers offer platforms for container hosting and orchestration. These platforms reduce the level of support required from the application owner as operational tasks are delegated to the platform. Furthermore, containers belonging to different applications can be co-located on the same virtual machine to utilize resources more efficiently. Given that there are underlying shared resources and consequently potential performance interference, predicting the level of interference before deciding to share virtual machines can avoid undesirable performance deterioration. We propose a lightweight performance interference modelling technique for cloud-native microservices. The technique constructs ML models for response time prediction and can dynamically account for changing runtime conditions through the use of a sliding window method. We evaluate our technique against realistic microservices on AWS EC2. Our technique outperforms baseline and competing techniques in MAPE by at least 1.45% and at most 92.04%.

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.