A Holistic Framework for Automated Configuration Recommendation for Cloud Service Monitoring
- URL: http://arxiv.org/abs/2603.12268v1
- Date: Sat, 07 Feb 2026 07:54:42 GMT
- Title: A Holistic Framework for Automated Configuration Recommendation for Cloud Service Monitoring
- Abstract summary: Reliability of large-scale cloud services is critical for user satisfaction and business continuity.<n>In large cloud companies, multiple services are deployed across regions necessitating robust health monitoring systems.<n>The current monitor configuration process is manual, largely reactive and ad hoc, resulting in gaps in coverage and redundant alerts.
- Score: 10.02304021245375
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Reliability of large-scale cloud services is critical for user satisfaction and business continuity. Despite significant investments in reliability engineering, production incidents remain inevitable, often leading to customer impact and operational overhead. In large cloud companies, multiple services are deployed across regions necessitating robust health monitoring systems. However, the current monitor configuration process is manual, largely reactive and ad hoc, resulting in gaps in coverage and redundant alerts. In this paper, we present a comprehensive study of monitor creation in Microsoft, identifying key components in the existing process. We further design a modular recommendation framework that processes the graph structured service entities to suggest optimal monitor configurations. Through extensive experimentation on historical data and user study of recommendations for production services at Microsoft, we demonstrate the efficacy of our approach in providing relevant recommendations for monitor configurations.
Related papers
- MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices [53.94035827454474]
We propose a lightweight Multi-scale Transformer designed as a unified AI service model for reliable industrial predictive maintenance.<n>MsFormer incorporates a Multi-scale Sampling (MS) module and a tailored position encoding mechanism to capture sequential correlations.<n>Experiments on real-world datasets demonstrate that the proposed framework achieves significant performance improvements over state-of-the-art methods.
arXiv Detail & Related papers (2026-03-24T11:12:50Z) - Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems [20.57785917249615]
We present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft.<n>We provide insights on the usefulness of this feature as perceived by the cloud service owners and lessons learned from deployment.
arXiv Detail & Related papers (2025-10-23T15:14:09Z) - Tracing and Metrics Design Patterns for Monitoring Cloud-native Applications [0.0]
This paper builds upon our previous work and introduces three design patterns that address key challenges in monitoring cloud-native applications.<n> Distributed Tracing improves visibility into request flows across services, aiding in latency analysis and root cause detection, Application Metrics provides a structured approach to instrumenting applications with meaningful performance indicators, and Infrastructure Metrics focuses on monitoring the environment in which the system is operated.
arXiv Detail & Related papers (2025-10-03T13:32:15Z) - A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments [40.96858640950632]
We propose DEMon, a decentralized self-adaptive monitoring system for edge.
We implement the proposed system as a lightweight and portable container-based system and evaluate it through experiments.
The results show that DEMon efficiently disseminates and retrieves the monitoring information, addressing the challenges of edge monitoring.
arXiv Detail & Related papers (2024-05-13T14:47:34Z) - Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach [8.862212993027658]
Gaps in monitoring can lead to delay in incident detection and significant negative customer impact.
Developers create monitors using their tribal knowledge and, primarily, a trial and error based process.
We propose an intelligent monitoring framework that recommends monitors for cloud services based on their service properties.
arXiv Detail & Related papers (2024-02-29T19:40:32Z) - Monitoring Auditable Claims in the Cloud [0.0]
We propose a flexible monitoring approach that is independent of the implementation of the observed system.
Our approach is based on combining distributed Datalog-based programs with tamper-proof storage based on Trillian.
We apply our approach to an industrial use case that uses a cloud infrastructure for orchestrating unmanned air vehicles.
arXiv Detail & Related papers (2023-12-19T11:21:18Z) - Optimal Event Monitoring through Internet Mashup over Multivariate Time
Series [77.34726150561087]
This framework supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals.
We further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation.
arXiv Detail & Related papers (2022-10-18T16:56:17Z) - Device-Cloud Collaborative Recommendation via Meta Controller [65.97416287295152]
We propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender.
On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller.
arXiv Detail & Related papers (2022-07-07T03:23:04Z) - Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
The paper studies existing methods in this field via a Systematic Literature Review ( SLR)
The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
arXiv Detail & Related papers (2021-12-03T08:06:24Z) - Reproducible Performance Optimization of Complex Applications on the
Edge-to-Cloud Continuum [55.6313942302582]
We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum.
Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour.
Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
arXiv Detail & Related papers (2021-08-04T07:35:14Z) - Monitoring and explainability of models in production [58.720142291102135]
Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services.
We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.
arXiv Detail & Related papers (2020-07-13T10:37:05Z) - A Privacy-Preserving Distributed Architecture for
Deep-Learning-as-a-Service [68.84245063902908]
This paper introduces a novel distributed architecture for deep-learning-as-a-service.
It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
arXiv Detail & Related papers (2020-03-30T15:12:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.