👉 Eligibility computing is a novel approach in distributed systems that enables efficient and scalable decision-making in environments with dynamic membership and resource availability. It leverages a probabilistic data structure, typically a Bloom filter combined with a counting Bloom filter, to track the eligibility of nodes (or resources) to participate in specific computations or access shared resources. When a node joins or leaves the system, its eligibility status is updated based on a predefined set of membership rules and resource availability constraints. This allows the system to quickly determine whether a node can be trusted or should be included in a computation without requiring a full membership check, thus reducing communication overhead and improving performance. Eligibility computing is particularly valuable in scenarios like distributed machine learning, where nodes may frequently join or leave the network, and in peer-to-peer networks where resource availability can change rapidly.
