But when network links deteriorate, nodes must automatically reduce the fidelity of the information transmitted and enable suitable algorithmic and decision-making processes directly at the edge - while still maximising the quality of information and the system’s overall capabilities.Ī Node Computing network is scale-invariant in the sense that it exhibits the same conceptual behaviour in a single node, in a group of interconnected nodes, or as a whole. When network links are good, high-fidelity, real-time information can be transmitted from edge systems to central cloud-based infrastructure in order to be indexed and processed by central systems. Node communication is adaptive vis-à-vis changes to the topology or quality of the communication network. Nodes exchange both data (eg, in the form of real-time sensor streams, meta data, or human inputs) and computation (eg, as algorithms, models, or complex AI-enabled applications). The nodes in a Node Computing system can be tiny, embedded edge devices on the one side, or scale-out compute clusters in the other extreme. The Node Computing paradigm is applicable whenever data is produced by distributed agents, communication links are sometimes present and at other times unreliable or non-existent, and when decision-making benefits from central knowledge and coordination but still needs to happen in their absence. Node Computing is a generalisation of Cloud and Edge Computing and reconciles their seemingly contradictory design goals. It is clear that neither Cloud Computing nor Edge Computing are satisfactory architectural paradigms on their own: complex Cloud Computing systems collapse when communication networks become unstable, and pure Edge Computing systems suffer from a lack of coordination and cannot benefit from the compounding effects of shared knowledge. Edge Computing also distributes the distillation of data into information and thus localises organisational knowledge and decision-making ability. The rationale for Edge Computing is straightforward: since algorithms and models are usually orders of magnitudes smaller than data, edge computing yields lower latencies, in particular in environments with unreliable communication networks. Edge Computing is the inverse: assuming a swarm of independent agents - each of which acting as a source of data - the Edge Computing paradigm promotes distribution of computation.