[Paper] Synergizing Monetization, Orchestration, and Semantics in Computing Continuum

Source: arXiv - 2512.08288v1

Overview

The paper presents HERMES – a unified framework that ties together resource monetization, intelligent orchestration, and semantic interoperability across the cloud‑to‑edge continuum. By turning every compute node—from data‑center servers to tiny IoT sensors—into a market‑ready, orchestrable, and semantically aware asset, HERMES aims to unlock the next wave of hyper‑distributed applications in manufacturing, transport, agriculture, and beyond.

Key Contributions

• Continuum‑wide Monetization Model – a decentralized marketplace that lets owners price compute, storage, and data services at any tier of the continuum.
• Semantic‑Driven Orchestration Engine – leverages ontologies and knowledge graphs to automatically discover, compose, and deploy services while preserving context and intent.
• Open, Trust‑Centred Architecture – combines zero‑trust networking, blockchain‑backed transaction logs, and fine‑grained access control to guarantee data integrity and provenance.
• Prototype Implementation & Benchmarks – a working HERMES prototype evaluated on a realistic smart‑factory scenario, showing up to 35 % latency reduction and 22 % cost savings versus conventional edge‑cloud pipelines.
• Extensible API & SDK – language‑agnostic interfaces that let developers plug in custom resource providers, pricing strategies, or domain‑specific ontologies without rewriting core logic.

Methodology

1. Requirement Mapping – the authors surveyed industry use‑cases (e.g., predictive maintenance, autonomous logistics) to extract three cross‑cutting needs: (i) scalable resource sharing, (ii) trustworthy data exchange, and (iii) automated service composition.
2. Design of the HERMES Stack
   • Marketplace Layer: built on a permissioned blockchain that records offers, bids, and settlements in smart contracts.
   • Orchestration Layer: a rule‑based engine that consumes a Continuum Ontology (describing device capabilities, location, latency budgets, etc.) and produces deployment plans using a constraint‑solver.
   • Security Layer: mutual TLS, attribute‑based encryption, and verifiable credentials enforce zero‑trust across heterogeneous networks.
3. Prototype Deployment – a testbed comprising:
   • 2 cloud VMs (AWS)
   • 4 edge gateways (NVIDIA Jetson)
   • 12 micro‑controllers (Arduino‑compatible)
     Real‑world workloads (image classification, sensor fusion) were run both with and without HERMES.
4. Evaluation Metrics – end‑to‑end latency, monetary cost (cloud credits vs. edge credits), and trustworthiness (measured by the number of successful provenance checks).

Results & Findings

Key takeaways:

• Latency gains stem from HERMES automatically pushing compute to the nearest capable edge node while respecting QoS constraints encoded in the ontology.
• Cost reductions arise because edge providers can price spare cycles far lower than cloud spot instances, and the marketplace dynamically matches demand to the cheapest viable resource.
• Trust improves dramatically thanks to immutable transaction logs and verifiable credentials, making it easier for downstream services to audit data origins.

Practical Implications

• For DevOps & Platform Teams – HERMES offers a plug‑and‑play orchestration API that can replace ad‑hoc scripts for edge deployment, reducing operational overhead and enabling “pay‑as‑you‑go” resource scaling across the continuum.
• For SaaS Vendors – the marketplace model opens a new revenue stream: expose specialized AI models or sensor data as consumable services that edge devices can purchase on‑demand.
• For IoT Device Manufacturers – embedding the HERMES SDK allows devices to advertise spare compute or storage, turning idle hardware into a micro‑revenue source without compromising security.
• For Compliance Officers – the built‑in provenance and zero‑trust layers simplify GDPR‑style data‑lineage audits, as every data transformation is cryptographically recorded.
• For Researchers & Start‑ups – the open ontology can be extended to niche domains (e.g., precision agriculture), accelerating prototype development and cross‑organization collaboration.

Limitations & Future Work

• Scalability of the Blockchain Layer – while a permissioned ledger reduces overhead, the authors note that transaction throughput may become a bottleneck in massive deployments (≥ 10⁶ nodes).
• Ontology Maintenance – keeping the Continuum Ontology up‑to‑date across heterogeneous vendors requires governance mechanisms that are not yet fully fleshed out.
• Edge Heterogeneity – the prototype assumes relatively uniform edge hardware; handling wildly divergent instruction sets or energy constraints remains an open challenge.
• Future Directions – the authors plan to (i) integrate federated learning for dynamic pricing based on model utility, (ii) explore sharding techniques for the blockchain to boost scalability, and (iii) conduct large‑scale field trials in smart‑city testbeds.

Authors

• Chinmaya Kumar Dehury
• Lauri Lovén
• Praveen Kumar Donta
• Ilir Murturi
• Schahram Dustdar

Paper Information

• arXiv ID: 2512.08288v1
• Categories: cs.DC
• Published: December 9, 2025
• PDF: Download PDF