Concept

ELASTIC aims to advance 6G service orchestration by leveraging cutting-edge cloud-native technologies. By incorporating WebAssembly, serverless FaaS, TEE-enabled Confidential Computing, and eBPF/XDP, ELASTIC addresses the limitations of existing in-network and edge computing systems. These technologies collectively enable ELASTIC to efficiently manage a diverse range of workloads, including complex machine learning tasks and dynamic security policies, ensuring robust performance and adaptability in modern 6G environments.

The framework provides a comprehensive solution for orchestrating data and services securely and efficiently. ELASTIC supports privacy-preserving multi-party AI and federated machine learning, ensuring data authenticity and trusted interactions across dynamic service contexts. With its high level of abstraction and programmability, ELASTIC utilizes node containers accessed through a hardware abstraction layer to facilitate seamless cloud-to-edge deployment. This approach ensures that ELASTIC is capable of managing complex services across the entire network architecture, from core infrastructure to edge devices, offering a scalable and versatile solution for 6G service orchestration.

Current TRL and Expected Advancements

9

Wasm-Operator SDK and runtime for compiling, packaging, and running serverless Kubernetes operators as Wasm modules (IMEC)

  • TRL Start-End: 3-4
  • Remarks: A framework that recompiles Kubernetes agents into a serverless control plane with minimal code changes, including a Wasm runtime for serverless Kubernetes operators and features predictive loading/unloading of controllers to balance responsiveness and resource usage​.
9

WasmHAL SDK interfaces and runtime extensions for securely connecting Wasm applications to hardware across platforms (IMEC)

  • TRL Start-End: 2-4
  • Remarks: An SDK for compiling hardware drivers and hardware abstraction layers to Wasm, providing interfaces for application connection to packaged HAL and runtime extensions that enable curated access to underlying hardware through a WebAssembly runtime​.
9

Automatic MAC profiles for Wasm runtime containers (LUN)

  • TRL Start-End: 3-4
  • Remarks: Creation of access profiles for Wasm runtime containers, tailored for typical WebAssembly classes running on Docker. The profile generator will be integrated with the WebAssembly test and integration environment.
9

Automation tooling for confidential computing environments (LUN)

  • TRL Start-End: 3-4
  • Remarks: Development of tools for secure execution, application updates, deployment monitoring, and migration​.
9

Automatic MAC profiles for Wasm runtime containers (LUN)

  • TRL Start-End: 3-4
  • Remarks: Creation of access profiles for Wasm runtime containers, tailored for typical WebAssembly classes running on Docker. The profile generator will be integrated with the WebAssembly test and integration environment.
9

Automation tooling for confidential computing environments (LUN)

  • TRL Start-End: 3-4
  • Remarks: Development of tools for secure execution, application updates, deployment monitoring, and migration​.
9

Mobility attack robust IoT resource allocation model (LUN)

  • TRL Start-End: 2-3
  • Remarks: Development of new resource allocation models that are robust against malicious mobility patterns intended to influence NWDAF-based resource allocations.
9

Static eBPF code security analyzer (POLITO)

  • TRL Start-End: 2-3
  • Remarks: A static analyzer capable of identifying security vulnerabilities in eBPF code with reference to the source code​.
9

Polycube framework (POLITO)

  • TRL Start-End: 3-4
  • Remarks: A framework to create and deploy eBPF programs​.
9

AI-based Intrusion Detection/Prevention System (TUC)

  • TRL Start-End: 3-4
  • Remarks: An AI-based Intrusion Detection/Prevention System mapped on reconfigurable platforms, offering online ML training and applying trained models on streaming data. The goal is to offload cluster security tools from real-time network monitoring of security issues.
9

Hardware-based cryptography module (TUC)

  • TRL Start-End: 2-3
  • Remarks: Integration of hardware systems like GPUs and FPGAs into the ELASTIC structure, applying cryptography and certification keys on internal cluster moving data based n the cluster security status and user needs​.
9

Federated Learning as a Service (FLaaS) (TID)

  • TRL Start-End: 4-6
  • Remarks: Development of the first Federated Learning as a Service (FLaaS) platform, allowing third-party applications and services to build FL models in a seamless and transparent fashion on user devices, supporting higher resource heterogeneity, hierarchical layers of networks and devices, and more privacy-preserving ML models