The 26th Workshop on Advances in Parallel and Distributed Computational Models (APDCM), which was held in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS) on May 27 - 31, 2024, aims to provide a timely forum for the exchange and dissemination of new ideas, techniques and research in the field of the parallel and distributed computational models. The APDCM workshop has a history of attracting participation from reputed researchers worldwide. The program committee has encouraged the authors of accepted papers to submit full-versions of their manuscripts to the International Journal of Networking and Computing (IJNC) after the workshop. After a thorough reviewing process, with extensive discussions, three articles on various topics have been selected for publication on the IJNC special issue on APDCM. On behalf of the APDCM workshop, we would like to express our appreciation for the large efforts of reviewers who reviewed papers submitted to the special issue. Likewise, we thank all the authors for submitting their excellent manuscripts to this special issue. We also express our sincere thanks to the editorial board of the International Journal of Networking and Computing, in particular, to the Editor-in-chief Professor Koji Nakano. This special issue would not have been possible without his support.

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Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we design efficient community detection algorithms in the batch dynamic setting. First, we present our parallel Dynamic Frontier approach. Given a batch update of edge deletions or insertions, this approach incrementally identifies an approximate set of affected vertices in the graph with minimal overhead. We apply this approach to both Louvain, a high quality, and Label Propagation Algorithm (LPA), a fast static community detection algorithm. Our approach achieves a mean speedup of 7.3× and 6.7×, when applied to Louvain and LPA respectively, compared to our parallel and optimized implementation of Δ-screening, a recently proposed state-of-the-art approach. Finally, we show how to combine Louvain and LPA with the Dynamic Frontier approach to arrive at a hybrid algorithm. This algorithm produces high-quality communities while being 14.6× faster than state-of-the-art, and identifying communities with the same quality score.

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This paper presents Cataphract, a batch processing method specialized for BFT databases. Batch processing is a common technique for byzantine fault-tolerant state machine replication (BFT SMR) and distributed databases. However, no batch processing method is optimized for BFT databases, which possess characteristics of both BFT SMR and distributed databases. Cataphract optimizes cryptographic and communication processing, which are bottlenecks in BFT databases. We evaluate Cataphract with Basil (state-of-the-art BFT database) in experiments. In an environment where nodes are within an availability zone, Basil with Cataphract demonstrates up to approximately 5.6 times higher throughput and reduces latency by up to about 55% compared to the vanilla Basil. In an environment where nodes are within a region, Basil with Cataphract demonstrates up to approximately 13.8 times higher throughput and reduces latency by up to about 74% compared to the vanilla Basil. In an environment where nodes are geographically distributed, Basil with Cataphract demonstrates up to approximately 80.4 times higher throughput and reduces latency by up to about 76% compared to the vanilla Basil.

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Network calculus has seen extensive use in the performance modeling of communications systems. Here, we apply network calculus techniques to the modeling of streaming data applications running on heterogeneous computing platforms. We quantitatively compare the performance predictions from network calculus with predictions from a discrete-event simulation model and a previously presented queuing theory model for two different applications.

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