Ecosystem-Agnostic Orchestration

• Ecosystem-Agnostic Orchestration Layer is a framework that uniformly manages and deploys distributed workloads regardless of underlying infrastructure or vendor specifics.
• It leverages domain-neutral APIs, decoupled control/data interfaces, and model-driven resource allocation to ensure flexible, privacy-preserving operation across multi-domain systems.
• The approach enhances multi-vertical environments—such as smart factories, edge-cloud deployments, and IoT ecosystems—through autonomous scaling, real-time monitoring, and optimized resource use.

An ecosystem-agnostic orchestration layer is an abstraction and control architecture that enables uniform management, deployment, and coordination of distributed computational workloads and services—irrespective of the underlying virtualization, hardware, application framework, protocol, or administrative domain. Its defining attributes are decoupled control/data interfaces, domain-neutral APIs, model-driven resource allocation, and mechanisms for autonomy and privacy preservation across heterogeneous environments. The central ambition is to allow arbitrary workloads—typically ML pipelines, containerized microservices, or distributed dataflows—to be flexibly orchestrated, monitored, and optimized in real time without revealing internal topology, technology choices, or proprietary details. This paradigm is crucial for complex, multi-domain infrastructures such as mobile networks, edge-cloud federations, hierarchical IoT deployments, and cross-ecosystem verticals, where operational constraints, privacy, and interoperability requirements exceed the capabilities of monolithic or vendor-locked orchestration stacks (Dandekar, 2021, Aslan et al., 10 Oct 2025, Guo et al., 9 Sep 2025).

1. Architectural Foundations and Model Reference Frameworks

Ecosystem-agnostic orchestration layers generalize beyond domain-specific orchestrators by introducing architecture patterns that mediate between intents, resources, and operational feedback.

A canonical model is the ITU Y.3172-based framework, as extended by (Dandekar, 2021):

• Management subsystem: Integrates traditional OAM with a domain-neutral Machine Learning Function Orchestrator (MLFO) responsible for intent parsing, pipeline lifecycle management (instantiate, scale, teardown), config/data control, and model selection/deployment.
• ML pipeline subsystem: Graph of logical operators (e.g., Data Source → Pre-Processor → Model → Policy → Data Sink).
• Underlying substrate (Underlay): Live IMT-2020 (5G/6G/Wi-Fi/private campus) networks where actual inference/training runs.
• Sandbox: Isolated testbed for pipeline pre-deployment testing.
• Interfaces: Northbound for "Intent" (declarative operator goals), Southbound for resource instantiation, monitoring, and telemetry aggregation.

The MLFO abstraction can be instantiated per administrative or functional domain, forming a hierarchical, federated control tree. Higher-level MLFOs issue derived "MLFO-intents" to subordinate MLFOs, which return telemetry and event statistics asynchronously via dedicated monitoring interfaces.

Analogous stratified architectures appear in cross-domain Kubernetes clusters (Aslan et al., 10 Oct 2025), federated container orchestration for SSI (Yildiz et al., 29 Dec 2025), and IoT/edge PaaS meta-engines (Ravindra et al., 2017). All employ a global control plane and domain-neutral APIs to unify heterogeneous management systems, isolate provider details, and simplify the orchestration of multi-provider, multi-domain resources.

2. Abstraction Mechanisms and API Design

Abstraction is achieved via declarative, technology-neutral intent languages and standardized APIs. In (Dandekar, 2021), the MLFOs expose REST/gRPC northbound and southbound interfaces, capable of accepting JSON or YAML intent objects and streaming telemetry. Intent objects encapsulate operational requirements as key-value constraints:

{ "intentid": "12345", "targetid": "FactoryMLFO", "operation": "maintain", "operand": "QoS", "oparams": { "accuracy": "95%" }, "constraints": { "latency": "30ms", "privacy": "strict" } }

These allow operators to express goals ("Maintain SLA between Edge and Factory") without specifying technology, resource names, or implementation details.

This abstraction enables plug-and-play compatibility with arbitrary ML frameworks (TensorFlow, PyTorch, custom VNF, ONNX), as all interactions are mediated via the intent language. Other platforms such as QONNECT (Aslan et al., 10 Oct 2025) use minimal YAML-based deployment policies, where application manifest and weighted QoS objectives (energy, cost, performance) are declaratively submitted to the orchestration layer:

application: bookinfo qos: energy: 0.5 cost: 0.3 perf: 0.2

All resource matching, scheduling, and migration occur without direct reference to underlying nodes, runtimes, or vendor specifics.

3. Autonomic, Privacy-Preserving, and Self-Optimizing Capabilities

Ecosystem-agnostic orchestration layers implement self-configuration and self-optimization through closed-loop feedback between intent interfaces and asynchronous monitoring. The MLFO autonomously deploys, scales, or tears down ML pipelines when intents or domain telemetry exceed threshold constraints—no human-in-the-loop is required (Dandekar, 2021). Optimization is performed by solving real-time constrained placement problems:

$$\min_{x} J = \sum_{r \in R} \sum_{t \in T} d_{r,t} \cdot x_{r,t}$$

subject to per-task assignment, capacity, and aggregate latency constraints.

Privacy is strictly enforced: only high-level intents and aggregated KPIs (e.g., "inference accuracy = 96%," "CPU usage = 45%") propagate upward; no raw data or proprietary parameters cross domain boundaries. In the case of SSI verifier orchestration (Yildiz et al., 29 Dec 2025), the controller layer exposes a unified API while routing all verification and credential flows to ecosystem-specific adapters that are isolated per container. The orchestration overhead is measured (<80 ms per proof request) and deemed negligible, with zero leakage of configuration or credentials between verifier ecosystems.

4. Multi-Domain and Federated Orchestration Workflows

Distributed orchestration spans hierarchies of domains or clusters, with parent orchestrators synthesizing intents for sub-domains and collecting domain-local telemetry for global optimization. An exemplar workflow in a smart factory setting (Dandekar, 2021):

1. Operator→OSS-MLFO: Intent ("Maintain Factory SLA")
2. OSS-MLFO → Edge-MLFO: Derived intent ("Anticipate QoS drop ≥20%, keep accuracy ≥95%")
3. Edge-MLFO → Factory-MLFO: Relocation/teardown intent ("Stop ML offload")
4. Factory-MLFO: Confirmation + stats

Scheduling typically involves multi-objective or weighted placement optimization. QONNECT uses a Borda count-based scorer:

$$w_n = q_E \cdot E^{\rm Borda}_n + q_P \cdot P^{\rm Borda}_n + q_C \cdot C^{\rm Borda}_n$$

Pods and microservices are placed across clusters (cloud, fog, edge) to maximize $w_n$, subject to capacity and affinity constraints (Aslan et al., 10 Oct 2025). Failover, migration, and leader re-election are handled by distributed control algorithms (e.g. Raft leadership and log replication).

5. Extension, Interoperability, and Ecosystem Isolation

A fundamental property is the ability to onboard new domains, clusters, service frameworks, or verification backends with minimal integration overhead. This is managed through modular adapter classes (REST/gRPC wrappers), registry-based discovery of endpoint capabilities, and pluggable policy engines for intent mapping and verification. For instance, new SSI ecosystems in interID (Yildiz et al., 29 Dec 2025) can be incorporated by containerizing their verifier, extending a standardized service adapter, and updating the proof template schema. All other orchestration logic, management flows, and routing remain unchanged.

In container/VM infrastructure, the orchestration logic abstracts away Docker, containerd, Firecracker, KVM, etc.—image layers are fetched by content hash (IPFS), containers are run via generic resource-limited calls, and telemetry is pulled via runtime-agnostic metrics APIs. All orchestration is executed by event-driven microservices (Monitor, Analyzer, Forecaster, Deployer) communicating over MQTT or similar protocols (Özyar et al., 2022).

6. Performance, Evaluation, and Practical Impact

Empirical validation demonstrates immediate operational gains:

• Rapid, zero-downtime migration and scaling of ML pipelines or microservices to meet dynamic intent constraints.
• Robust domain isolation: operator policies are fulfilled without knowledge of internal domain features, topology, or data.
• Substantial reductions in resource overhead: Oakestra achieves <2% CPU and 30 MB memory footprint per worker, outperforming K3s/K8s by 6x and 30%, respectively (Bartolomeo et al., 2022).
• Transparent workload redistribution in cloud–edge IoT PaaS (ECHO), sustaining vertical and horizontal scale-out without coupling to engine or device type (Ravindra et al., 2017).
• Fault-tolerant orchestration with failover recovery time in the hundreds of milliseconds and QoS compliance rates above 95% in distributed Kubernetes federation (Aslan et al., 10 Oct 2025).
• In geo-distributed serverless platforms, orchestration layers enable multi-provider, multi-endpoint scaling via policy- and performance-driven selection models, ensuring low-latency EOS operations without vendor lock-in (Sánchez-Gallegos et al., 26 Mar 2025).

These data collectively establish the orchestration layer as a non-disruptive, highly scalable, and resilient substrate for future multi-domain, multi-vertical network and service management under extreme heterogeneity.

7. Limitations and Prospects for Enhancement

Existing designs assume robust intent parsing and telemetry integration; the quality of autonomy is bounded by granularity of monitoring, expressivity of intent schemas, and solver scalability. Extensions include integration of ML-driven "Orchestration Advisers", distributed service registries with eventual consistency, polyglot adaptation agents, and protocol-neutral overlays for higher throughput and compatibility. The inclusion of computational intelligence will drive these orchestration layers from reactive optimization mechanisms toward proactive, cross-domain, self-tuning control fabrics (Bassil, 2012, Dandekar, 2021). The foundational architecture aligns with ongoing research into self-managing, vendor-neutral, privacy-aware orchestration platforms for advanced computing and networking environments.