CMonitor: A Monitoring and Alarming Platform for Container-Based Clouds

Ji, Shujian; Ye, Kejiang; Xu, Cheng‐Zhong

doi:10.1007/978-3-030-23502-4_23

Cited by 13 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CMonitor as a related tool has been developed to support similar goals of lightweight container profiling without setup of a full monitoring application [ 19 , 20 ]. CMonitor is installed and run on the host and is used to profile host metrics in addition to container metrics as the tool is not focused specifically on profiling a containerized task or pipeline.…”

Section: Resultsmentioning

confidence: 99%

Container Profiler: Profiling resource utilization of containerized big data pipelines

Hoang,

Hung,

Perez

et al. 2022

GigaScience

View full text Add to dashboard Cite

Background This article presents the Container Profiler, a software tool that measures and records the resource usage of any containerized task. Our tool profiles the CPU, memory, disk, and network utilization of containerized tasks collecting over 60 Linux operating system metrics at the virtual machine, container, and process levels. The Container Profiler supports performing time-series profiling at a configurable sampling interval to enable continuous monitoring of the resources consumed by containerized tasks and pipelines. Results To investigate the utility of the Container Profiler, we profile the resource utilization requirements of a multistage bioinformatics analytical pipeline (RNA sequencing using unique molecular identifiers). We examine profiling metrics to assess patterns of CPU, disk, and network resource utilization across the different stages of the pipeline. We also quantify the profiling overhead of our Container Profiler tool to assess the impact of profiling a running pipeline with different levels of profiling granularity, verifying that impacts are negligible. Conclusions The Container Profiler provides a useful tool that can be used to continuously monitor the resource consumption of long and complex containerized applications that run locally or on the cloud. This can help identify bottlenecks where more resources are needed to improve performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Container Profiler: Profiling resource utilization of containerized big data pipelines

Hoang,

Hung,

Perez

et al. 2022

GigaScience

View full text Add to dashboard Cite

show abstract

“…These solutions monitor containers by leveraging either resource usage or syscalls. Resource usage based solutions [13], [27], [45], [46] work under the assumption that anomalies usually trigger a spike in a container's resource usage (e.g., CPU, memory, etc). While these techniques can successfully detect attacks that demand an excess of resources (e.g., Cryptojacking [28]), they overlook those that do not require such high resource consumption.…”

Section: Ixrelated Workmentioning

confidence: 99%

ReplicaWatcher: Training-less Anomaly Detection in Containerized Microservices

El Khairi,

Caselli,

Peter

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

Despite its detection capabilities against previously unseen threats, anomaly detection suffers from critical limitations, which often prevent its deployment in real-world settings. In fact, anomaly-based intrusion detection systems rely on comprehensive pre-established baselines for effectively identifying suspicious activities. Unfortunately, prior research showed that these baselines age and gradually lose their effectiveness over time, especially in dynamic deployments such as microservices-based environments, where the concept of "normality" is frequently redefined due to shifting operational conditions. This scenario reinforces the need for periodic retraining to uphold optimal performance -a process that proves challenging, particularly in the context of security applications.We propose a novel, training-less approach to monitoring microservices-based environments. Our system, REPLI-CAWATCHER, observes the behavior of identical container instances (i.e., replicas) and detects anomalies without requiring prior training. Our key insight is that replicas, adopted for fault tolerance or scalability reasons, execute analogous tasks and exhibit similar behavioral patterns, which allow anomalous containers to stand out as a notable deviation from their corresponding replicas, thereby serving as a crucial indicator of security threats. The results of our experimental evaluation show that our approach is resilient against normality shifts and maintains its effectiveness without the necessity for retraining. Besides, despite not relying on a training phase, REPLICAWATCHER performs comparably to state-of-the-art, training-based solutions, achieving an average precision of 91.08% and recall of 98.35%.

show abstract

PLMSys: A Cloud Monitoring System Based on Cluster Performance and Container Logs

Sun

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

CMonitor: A Monitoring and Alarming Platform for Container-Based Clouds

Cited by 13 publications

References 8 publications

Container Profiler: Profiling resource utilization of containerized big data pipelines

Container Profiler: Profiling resource utilization of containerized big data pipelines

ReplicaWatcher: Training-less Anomaly Detection in Containerized Microservices

PLMSys: A Cloud Monitoring System Based on Cluster Performance and Container Logs

Contact Info

Product

Resources

About