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AI-Driven Observability in Cloud-Edge with OpenNebula

Open Nebula
03/28/2026
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TL;DR

  • OpenNebula has developed OneAIOps, an experimental open-source framework that combines cloud infrastructure management with AI-driven observability using Prometheus, Grafana, and machine learning algorithms to provide intelligent workload forecasting and automated resource optimization.
  • The framework addresses data sovereignty concerns by keeping operational data under organizational control while avoiding vendor lock-in, supporting hybrid cloud environments that span on-premise data centers, public cloud providers, and edge nodes through a unified management interface.
  • OneAIOps uses machine learning to predict CPU, memory, and network usage patterns, then applies decision algorithms to optimize VM placement through strategies like load balancing, migration reduction, and resource consolidation, currently operating in advisory mode with strong prediction accuracy.
  • Development roadmap includes implementing automated operations, expanding prediction capabilities, adding anomaly detection, and integrating OneAIOps directly into OpenNebula's software distribution, with funding from the EU's Horizon Europe COGNIT project.
  • The framework is open source and available for community contribution, with OpenNebula encouraging participation through its GitHub repository and user forum to advance AI-driven infrastructure management while maintaining transparency and organizational control.

OneAIOps Framework Architecture and Integration

Victor Palma introduces OneAIOps, an experimental open-source framework developed by OpenNebula that combines cloud infrastructure management with AI-driven observability. The framework integrates OpenNebula's cloud management platform with Prometheus for metrics collection and Grafana for visualization, layering machine learning algorithms on top to enable intelligent workload forecasting and automated infrastructure orchestration. The architecture addresses data sovereignty concerns by keeping sensitive operational data under organizational control rather than with third-party providers. OpenNebula manages hybrid cloud environments spanning on-premise data centers, public cloud resources, and edge nodes through a unified interface, supporting multiple hypervisors including VMware, KVM, and LXC while providing multi-tenancy, self-service provisioning, and elasticity capabilities.

Predictive Analytics and Resource Optimization

The OneAIOps framework leverages machine learning algorithms to predict CPU, memory, and network traffic usage based on historical metrics collected through Prometheus exporters deployed across the infrastructure. These predictions feed into decision algorithms that optimize VM placement and resource allocation across the cloud environment. The system offers multiple optimization strategies including load balancing to distribute workloads evenly, migration reduction to minimize disruption, and resource consolidation to maximize efficiency in on-premise infrastructures. Grafana dashboards provide visibility into prediction accuracy, with the demonstration environment showing strong correlation between predicted and actual usage patterns. The framework currently operates in advisory mode, providing migration suggestions to cloud administrators rather than executing changes automatically.

Development Roadmap and EU Funding Context

OpenNebula plans to evolve OneAIOps from its current experimental state into a production-ready component integrated directly into the OpenNebula software distribution. Near-term development priorities include implementing automated operations to execute optimization suggestions without manual intervention, expanding prediction capabilities to cover memory and network traffic allocation, and adding anomaly detection with intelligent alerting. The project receives funding from the European Union's Horizon Europe program through the COGNIT initiative, which focuses on cognitive service frameworks for the cloud-edge continuum. OpenNebula encourages community participation through its open-source repository and active user forum, positioning OneAIOps as a collaborative effort to advance AI-driven infrastructure management while maintaining data sovereignty and avoiding vendor lock-in.

Chapters

0:00 - Introduction
1:14 - About Victor Palma
1:35 - Why Observability Matters
3:03 - AI for Observability
3:49 - Data Sovereignty Challenges
5:49 - OneAIOps Framework Overview
6:18 - What is OpenNebula?
8:36 - Multi-Cloud Management
9:20 - Prometheus Integration
11:06 - Adding AI to the Formula
11:47 - OneAIOps Architecture
13:45 - Features and Capabilities
16:48 - Example Environment Demo
17:25 - Next Steps and Challenges
18:26 - Community Forum
18:51 - COGNIT EU Funding

Key Quotes

1:52 "It's nothing more than the ability to understand and analyze the inner workings of a system by collecting and analyzing relevant information. Or in other words, it's just transforming data input into information, into something that can be useful for us."
4:03 "Many organizations currently entrust sensitive data to external observability providers, raising concerns about data ownership and privacy. The information and the data is on the provider servers. So this in certain situations can be a risk."
5:43 "The solution for this is the OneAI Ops Framework, the open source solution for AI-driven observability."
11:16 "We can predict through machine learning, the CPU, memory, and network traffic usage we may have in the future based on all past metrics. And then, thanks to the implementation of decision algorithms, we can allocate resources based on the current context of our cloud."
17:04 "Since it's a prototype, no automatic migration actions are performed here. This is only limited to suggestions for the cloud administrator. However, according to the results of our labs, the recommendations are quite good and help greatly to optimize the cloud."
Categories:
  • » Cloud » Hybrid Cloud
  • » Cybersecurity » Application Security
  • » Cybersecurity » Cloud Security
  • » AI & Machine Learning
  • » Data Protection
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Tags:
  • Cloud Security
  • AI & Machine Learning
  • DevSecOps
  • Technical Deep Dive
  • Demo
  • AIOps
  • Cloud Infrastructure Management
  • Observability
  • Machine Learning
  • Hybrid Cloud
  • Data Sovereignty
  • Resource Optimization
  • Prometheus Monitoring
  • Open Source
  • Edge Computing
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