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Applied Sciences
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Recent Advances in Intelligent Distributed Computing and Its Applications
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Recent Advances in Intelligent Distributed Computing and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 2790

Special Issue Editor

Prof. Dr. Wenzhong Li

E-Mail Website
Guest Editor
National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
Interests: distributed computing; artificial intelligence; big data; social network

Special Issue Information

Dear Colleagues,

Recent years have witnessed a popular trend in applying artificial intelligence and machine learning techniques for distributed computing and its applications. Emerging AI techniques such as deep neural networks, reinforcement learning, graph neural networks, game theory, etc. have been shown to be effective in facilitating a wide range of distributed computing scenarios, such as big data processing and analysis, learning-based resource management, network optimization, smart sensing, social network analysis, etc. On the other hand, novel distributed computing paradigms such as distributed machine learning, federated learning, crowdsourcing, etc., also provide basic support for the rapid development and application of AI techniques. This Special Issue is dedicated to intelligent distributed computing and its applications, whose interests include but are not limited to the following topics:

  • Intelligent resource management for cloud computing;
  • Intelligent communication for network optimization;
  • Learning-based network protocol design and application;
  • Machine learning for multimedia systems;
  • Adaptive video streaming systems;
  • Big data processing and analysis;
  • Social network analysis and applications;
  • Graph neural networks and their applications;
  • Federated learning;
  • Smart sensing and smart sensing;
  • AIoT—Artificial Intelligence of Things;
  • Distributed machine learning methodology and systems.

Prof. Dr. Wenzhong Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent distributed computing
  • learning-based resource management
  • data-driven network optimization
  • big data analysis
  • social network analysis
  • federated learning

Published Papers (3 papers)

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Research

17 pages, 3402 KiB  
Article
Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning
by Ding Li, Wenzhong Li, Guoqiang Zhang, Yizhou Chen, Xu Zhong, Mingkai Lin and Sanglu Lu
Appl. Sci. 2023, 13(24), 13306; https://doi.org/10.3390/app132413306 - 16 Dec 2023
Viewed by 663
Abstract
In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, which is usually composed of a sequence of [...] Read more.
In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, which is usually composed of a sequence of edges, where each edge is represented by two endpoints and a weight. As a result of its large volume and highly dynamic nature, several graph sketches were proposed for the purposes of summarizing large-scale graph streams and enabling fast query processing. By using a compact data structure with hash functions, the graph sketches sequentially store the edges. Nevertheless, the existing graph sketches suffer from low performance on graph query tasks as a result of unpredictable collisions between heavy edges and light edges. To store heavy edges and light edges, this paper introduces a novel learning-based Dichotomy Graph Sketch (DGS) mechanism that uses two separate graph sketches, a heavy sketch and a light sketch. During a graph stream session, DGS obtains heavy edges and light edges, and uses these edges as training samples for a deep neural network (DNN) based binary classifier. The DNN-based classifier is then used to determine whether the upcoming edges are heavy or not. We will store the edges that are classified as heavy edges in the heavy sketch, and those that are classified as light edges in the light sketch. By combining the learnable classifier and Dichotomy Graph Sketches, the proposed mechanism resolves the hashing collision problem in conventional graph sketches and significantly improves graph query accuracy. The DGS algorithm outperforms the state-of-the-art graph sketches in a variety of graph query tasks based on extensive experiments that were conducted on four real-world graph stream datasets. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Distributed Computing and Its Applications)
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19 pages, 883 KiB  
Article
OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
by Rui Zhu, Bo Liu, Ruwen Zhang, Shengxiang Zhang and Jiuxin Cao
Appl. Sci. 2023, 13(23), 12915; https://doi.org/10.3390/app132312915 - 02 Dec 2023
Cited by 1 | Viewed by 735
Abstract
The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper proposes a knowledge graph framework to fill [...] Read more.
The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper proposes a knowledge graph framework to fill the gap in the knowledge management application of the ocean engineering field. Subsequently, we propose an intelligent question-answering framework named OEQA based on an ocean engineering-oriented knowledge graph. Firstly, we define the ontology of ocean engineering and adopt a top-down approach to construct a knowledge graph. Secondly, we collect and analyze the data from databases, websites, and textual reports. Based on these collected data, we implement named entity recognition on the unstructured data and extract corresponding relations between entities. Thirdly, we propose an intent-recognizing-based user question classification method, and according to the classification result, construct and fill corresponding query templates by keyword matching. Finally, we use T5-Pegasus to generate natural answers based on the answer entities queried from the knowledge graph. Experimental results show that the accuracy in finding answers is 89.6%. OEQA achieves in the natural answer generation in the ocean engineering domain significant improvements in relevance (1.0912%), accuracy (4.2817%), and practicability (3.1071%) in comparison to ChatGPT. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Distributed Computing and Its Applications)
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15 pages, 1765 KiB  
Article
SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing
by Kaidala Jayaram Rajashekar, Channakrishnaraju, Puttamadappa Chaluve Gowda and Ananda Babu Jayachandra
Appl. Sci. 2023, 13(19), 10850; https://doi.org/10.3390/app131910850 - 29 Sep 2023
Cited by 1 | Viewed by 646
Abstract
Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In [...] Read more.
Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Distributed Computing and Its Applications)
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