Abstract
Detecting the location of performance anomalies in complex distributed systems is critical to ensuring the effective operation of a system, in particular, if short-lived container deployments are considered, adding challenges to anomaly detection and localization. In this paper, we present a framework for monitoring, detecting and localizing performance anomalies for container-based clusters using the hierarchical hidden Markov model (HHMM). The model aims at detecting and localizing the root cause of anomalies at runtime in order to maximize the system availability and performance. The model detects response time variations in containers and their hosting cluster nodes based on their resource utilization and tracks the root causes of variations. To evaluate the proposed framework, experiments were conducted for container orchestration, with different performance metrics being used. The results show that HHMMs are able to accurately detect and localize performance anomalies in a timely fashion.
Highlights
Many distributed software systems, such as clouds, allow applications to be deployed and managed through third-party organizations in order to provide shared, dynamically managed resources and services [1]
The results demonstrate that dynamic workload has a strong impact on the container metrics as the monitored container is unable to process more than those requests
The results show that the hierarchical hidden Markov model (HHMM) and the hierarchical temporal memory (HTM) model detect anomalous behaviour with good results compared to the dynamic bayesian network (DBN) on small and moderate datasets
Summary
Many distributed software systems, such as clouds, allow applications to be deployed and managed through third-party organizations in order to provide shared, dynamically managed resources and services [1]. Understanding the behaviour of such systems is difficult as it often requires to observe larger numbers of components such as nodes and containers over longer periods of time. Due to the distributed nature, heterogeneity, and scale of many container deployments, performance anomalies may be experienced leading to system performance degradation and potential application failures. A performance anomaly arises when a resource behaviour (e.g., CPU utilization, memory usage) deviates from its expectation. Such a performance anomaly is difficult to detect because normal performance behaviours are not always established. If an application attempts to automatically configure itself by allocating resources based on the total node resources available, it may over-allocate when running in a resource-constrained container
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