Eclipse KuDECO

Eclipse KuDECO is an open-source Kubernetes-oriented framework designed to enable intelligent, adaptive orchestration of containerised applications across highly dynamic and distributed edge-cloud environments. Unlike traditional cloud-centric orchestrators, Eclipse KuDECO introduces cognitive, decentralised decision-making capabilities that align with the operational demands of modern industrial and cyber-physical systems.

As Edge computing becomes foundational to digital transformation across different competitiveness domains such as Manufacturing, Smart Cities, Eclipse KuDECO addresses the limitations of centralised orchestration platforms like Kubernetes when operating at the network edge, and across an heterogeneous, multi-tenant IoT-Edge-Cloud continuum. KuDECO assumes that  nodes may disconnect, network conditions vary, and latency-sensitive services require rapid, localized decision-making. KuDECO overcomes these challenges by embedding reasoning and context-awareness directly into the orchestration layer.

Key Features:

• Cognitive Orchestration at the node/cluster Level
  KuDECO augments Kubernetes with decentralized intelligence at each node and at a cluster level, enabling real-time container scheduling based on live context (e.g., CPU usage, network usage, energy consumption, and data freshness).
• Cross-layer Context Awareness
  KuDECO integrates monitoring of resources, network conditions, and data lifecycle to inform orchestration decisions that meet application-specific goals.
• Decentralized Architecture
  KuDECO components operate with minimal reliance on central control, promoting scalability, resilience, and autonomy across the edge-cloud continuum.
• Unified Management via a common operator
  The KuDECO Automated Configuration Management (ACM) component offers a cohesive user interface for developers and cluster managers, managing deployments, configuration policies, and integration with non-Kubernetes systems.
• Data-Centric Observability
  The Metadata Manager (MDM) provides observability into the full data workflow, treating data as a first-class entity and improving orchestration decisions.
• Seamless Workload Migration
  While KuDECO can be used with different Kubernetes schedulers, it integrates SWM, which uses a solver-based approach to match containerized workloads with compute, data, and network resources using a min-max graph model.
• Network Awareness by design
  The Network Management and Adaptability component (NetMA) enables secure, adaptive connectivity and exposes metrics for optimized workload distribution.
• Privacy-Preserving Learning and Context-Awareness via PDLC
  PDLC is the cognitive “brain” of KuDECO , performing node cost estimation and system stability analysis using decentralized, privacy-preserving learning algorithms.
• End-to-End Monitoring
  KuDECO offers multi-layered observability, covering system resources (ACM), data workflows (MDM), and network state (NetMA), enabling more intelligent, fault-tolerant orchestration.

Validation and Relevance

KuDECO has been validated in real-world experimental setups, including:

• Edge clusters using Raspberry Pi and k3s
• Cloud deployments using scalable virtual machines
• Test automation via the Horizon Europe CODECO Experimentation Framework (CODEF)

It has demonstrated applicability in several industrial contexts:

• Real-time orchestration in factory automation
• Adaptive workloads in smart city scenarios
• Resilient infrastructure for critical systems

Open Ecosystem

KuDECO is designed for extensibility and collaboration. Its open-source codebase and modular design invite contributions from both academia and industry. Integration with standard Kubernetes environments ensures compatibility and ease of adoption, while its decentralized AI-driven architecture makes it a future-ready alternative for Edge-Cloud orchestration.