QueGs

QueGs is a fictional or hypothetical framework intended to explore the interaction between queueing theory and graph query processing. It provides an abstract platform that models how graph queries compete for shared processing resources and how different scheduling policies affect latency and throughput.

Architecture of QueGs centers on a modular stack that includes a query planner, a queue manager, and a graph execution engine connected to a graph store. Workloads are represented as arrival streams of queries with attributes such as size, priority, and required results. The queue manager applies scheduling policies such as first-come-first-served, priority-based, or weighted fair queuing, and can enforce backpressure to prevent overloading.

History and status: the concept emerged in academic discussions as a teaching and research tool to illustrate scheduling effects on graph workloads. It is not a commercial product with a standard implementation, and no official specification is widely adopted.

Features and use cases: QueGs supports modular adapters for various graph databases, simulation of workloads, and instrumentation for latency, queue length, and throughput metrics. It is used for experiments in computer science education, research papers, and demonstrations of quality of service in graph querying.

Impact and context: by combining queuing models with graph query semantics, QueGs helps researchers evaluate trade-offs between latency, throughput, and fairness, and informs design choices for real-world graph database systems. Related topics include graph databases, queuing theory, and scheduling algorithms.