The 25th 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 15 - 19, 2023, 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, four 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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In this work, we propose the Network Performance Collector (NPC) workflow for automated network performance characterization. The workflow is based on the collection, processing as well as visualization of network performance metrics such as throughput and latency and can be used for analysis with different network performance models. Depending on the chosen model, benchmark tools such as iperf or sockperf as well as microbenchmarks specific to parallel programming models can be automated and orchestrated for data collection with the NPC. The data obtained can then be used by NPC, for example, to validate and characterize the performance of the underlying network or to analyze the system boundaries for a particular application. We provide a prototype implementation of the proposed workflow and demonstrate its effectiveness by automating an extended Roofline model analysis.

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For an undirected graph G = (V,E) and a k-non-negative integer vector p = (p1, . . . , pk), a mapping l : V → N∪{0} is called an L(p)-labeling of G if |l(u) − l(v)| ≥ pd for any two distinct vertices u, v ∈ V with distance d, and the maximum value of {l(v) | v ∈ V } is called the span of l. Originally, L(p)-labeling of G for p = (2, 1) is introduced in the context of frequency assignment in radio networks, where ‘close’ transmitters must receive different frequencies and ‘very close’ transmitters must receive frequencies that are at least two frequencies apart so that they can avoid interference. L(p)-Labeling is the problem of finding the minimum span λp among L(p)-labelings of G, which is NP-hard for every non-zero p. L(p)-Labeling is well studied for specific p’s; in particular, many (exact or approximation) algorithms for general graphs or restricted classes of graphs are proposed for p = (2, 1) or more generally p = (p, q). Unfortunately, most algorithms strongly depend on the values of p, and it is not apparent to extend algorithms for p to ones for another p′ in general. In this paper, we give a simple polynomial-time reduction of L(p)-Labeling on graphs with a small diameter to Metric (Path) TSP, which enables us to use numerous results on (Metric) TSP. On the practical side, we can utilize various high-performance heuristics for TSP, such as Concordo and LKH, to solve our problem. On the theoretical side, we can see that the problem for any p under this framework is 1.5-approximable, and it can be solved by the Held-Karp algorithm in O(2nn2) time, where n is the number of vertices, and so on.

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A program’s architecture—how it organizes the invocation of application-specific logic—influences important program characteristics including its scalability and security. Architecture details are usually expressed in the same programming language as the rest of a program, and can be difficult to distinguish from non-architecture code. Once defined, a program’s architecture is difficult and risky to change because it couples tightly with application logic over time. We introduce C-Saw: an approach to express a software’s architecture using a new embedded domain-specific language (EDSL) designed for that purpose. It decouples application-specific logic from architecture, making it easier to identify architectural details of software. C-Saw leverages three ideas: (i) introducing a new, formally-specified EDSL to separate an application’s architecture description from its programming language; (ii) reducing architecture implementation to the definition and management of distributed key-value tables, and (iii) introducing an expressive state-management abstraction for distributed applications. We describe a prototype implementation of C-Saw for C programs and use its implementation to build end-to-end examples of expressing and changing the architecture of widely-used, thirdparty software. We evaluate this on Redis, cURL, and Suricata and find that C-Saw provides expressiveness and reusability, requires fewer lines of code when compared to directly using C to express architectural patterns, and imposes low performance overhead on typical workloads.

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Transaction processing is common in our daily lives, and various protocols have been studied to manage concurrency control. One such protocol is the deterministic concurrency control protocol, which is highly efficient in handling workloads with high contention because of its deterministic transaction scheduling nature. This paper introduces a new lock manager, the concurrent lock manager for determinism or CLMD. CLMD allows non-conflicting transactions to be executed concurrently on deterministic concurrency control protocols. The concept is to check for conflicts between the current and all future transactions while the conventional scheme checks only the current and the following ones. The CLMD eliminates the bottleneck present in the conventional locking scheme used in the original Calvin paper. We evaluated CLMD with Calvin in experiments. The results showed that the proposed method outperformed SS2PL under high-contention workloads and performed better than Calvin under low and high-contention workloads. The maximum performance improvement observed was approximately 44.4 times using 64 logical cores.

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Hotel booking sites provide evaluations, including textual reviews and numerical ratings by hotel guests. However, some evaluations do not include numerical ratings, and there are some evaluations in which textual reviews and numerical ratings are inconsistent (i.e., a positive review text is posted along with a low rating, or vice versa). Such evaluations may need to be clarified for site users. To resolve such problems, we propose three highly accurate methods to predict an overall numerical rating from a textual review. Our new proposal is to use Categoryoriented Sentiment Polarity Dictionaries (CSPD), which are automatically compiled for each category using a Rakuten Travel review database. The CSPD gives the sentiment polarity value (i.e., the positivity/negativity value) for each sentiment word for each category. Our proposed methods first predict category ratings from the BERT vector for the review and the CSPD. After that, based on the predicted category ratings and the BERT vector, our methods predict the overall rating. We conducted evaluation experiments using the Rakuten Travel review dataset for 2014-2019. Our experimental results show that our methods achieve higher accuracy than using only BERT vectors and successfully detect inconsistent evaluations.

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