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

收集和分析云到边缘计算连续体中的大量数据提出了新的挑战。在云数据中心集中处理所有这些数据不再可行将大量数据传输到云中既耗时又昂贵，会降低性能并可能引发安全问题。因此，出现了新的分布式计算范例，如边缘计算和雾计算，以支持更接近原始数据的处理。然而，这样的超分布式系统需要从根本上新的方法。为了克服当前集中式应用程序管理方法的局限性，Sarmhestrate将开发一种全新的分散式应用程序级协调器，它基于自组织的相互依赖的群的概念。应用微服务在动态编排空间中由分散的编排代理管理，由分布式智能管理，提供应用需求和资源之间的匹配，并支持群的动态自组织。对于协调空间的运营至关重要的知识和信任，将通过基于区块链的可信解决方案使用自主主权身份(SSI)和分布式标识符(DID)的方法进行管理。通过使用最先进的加密算法和隐私保护数据分析，将确保整个系统的端到端安全。由于分散系统的迫在眉睫的复杂性，将开发新的模拟方法，以在开发的早期阶段测试和优化系统行为(例如，能源效率)。此外，模拟器将进一步扩展为与物理系统并行运行的数字孪生兄弟，并通过预测反馈改善其行为。蜂群组织的概念将以四个现实生活中的演示者为原型，这些演示者来自防洪、停车位管理、视频分析和数字孪生自然栖息地。

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 centresis not feasible anymore astransferring large amounts of data to the cloud istime-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.