Dynamic graphs are increasingly being utilized in domain knowledge modeling and large-scale scientific data management. Managing dynamic graph data requires a graph database system that can handle constantly changing volumes and data versions, while maintaining an acceptable query latency related to versioning. To understand how the design of storage structures affects database performance and assist scientific application developers in finding the optimal storage structure for their dynamic graph application scenarios, we have designed an easy-to-use benchmark framework called SciDG. We also conducted a study on the latencies of five fundamental version-related queries for various scientific application scenarios using SciDG. We evaluated the performance of databases based on three distinct storage principles: Sp-DB, Dp-DB, and Tp-DB. The experimental results indicate that SciDG is a valuable tool for assessing the strengths and weaknesses of different storage structures for dynamic graphs in various scenarios. Additionally, it assists scientists in selecting the most suitable dynamic graph database system for their work.

(n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. This paper developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1),..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k.