Application-level Swarm-based Orchestration Across the Cloud-to-Edge Continuum

Collecting and analysing large amounts of data in the Cloud-to-Edge computing continuum raises novel challenges.

Processing all this data centrally in cloud data centres is not feasible anymore as transferring large amounts of data to the cloud is time-consuming, expensive, degrade performance and may raise security concerns.

Therefore, novel distributed computing paradigms, such as edge and fog computing emerged to support processing data closer to its origin. However, such hyper-distributed systems require fundamentally new methods.

To overcome the limitation of current centralised application management approaches, Swarmhestrate will develop a completely novel decentralised application-level orchestrator, based on the notion of self-organised interdependent Swarms.

Application microservices are managed in a dynamic Orchestration Space by decentralised Orchestration Agents, governed by distributed intelligence that provides matchmaking between application requirements and resources, and supports the dynamic self-organisation of Swarms.

Knowledge and trust, essential for the operation of the Orchestration Space, will be managed through blockchain-based trusted solutions using methods of Self-Sovereign Identities (SSI) and Distributed Identifiers (DID).

End-to-end security of the overall system will be assured by utilising state-of-the-art cryptographic algorithms and privacy preserving data analytics.

Due to the imminent complexity of the decentralised system, novel simulation approaches will be developed to test and optimise system behaviour (e.g., energy efficiency) in the early stages of development.

Additionally, the simulator will be further extended into a digital twin running in parallel to the physical system and improving its behaviour with predictive feedback.

The Swarmchestrate concept will be prototyped on four real-life demonstrators from the areas of flood prevention, parking space management, video analytics and a digital twin of natural habitat.