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Electronics
Special Issues
Distributed Computing and Storage Challenges for Emerging Applications
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Distributed Computing and Storage Challenges for Emerging Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 2148

Special Issue Editors

Prof. Dr. Sungyong Park

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sogang University, Seoul, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
Interests: big data processing and real-time streaming analytics; software platform for big data and machine learning; cloud and edge computing; operating systems and embedded software
Dr. Gordon Euhyun Moon

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sogang University, Seoul, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
Interests: high-performance computing; parallel and distributed deep learning; federated learning; deep learning applications

Special Issue Information

Dear Colleagues,

This Special Issue explores new research on distributed computing and storage for emerging applications such as artificial intelligence, big data, blockchain, and metaverse. Given the growing size of data and the increasing complexity of applications, it is becoming crucial to develop applications and systems on a distributed platform instead of relying on a single node. As a result, for the efficient development of emerging applications, there are numerous design challenges and optimization issues that need to be addressed.

In this Special Issue, contributions that highlight novel ideas about distributed algorithms and system optimization techniques for developing future emerging applications will be considered. This Special Issue aims to bring together community research in the areas of emerging applications and computer systems, and curate a selection of articles.

Prof. Dr. Sungyong Park
Dr. Gordon Euhyun Moon
Guest Editors

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. Electronics 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

  • distributed algorithms and optimization issues in emerging applications such as AI, big data, block chain, meta verse, and so on
  • scalable IO techniques for emerging applications
  • resource management in emerging applications
  • big data platforms and streaming analytics
  • storage systems for data-intensive computing
  • distributed deep learning approaches: data parallelism and model parallelism
  • high-performance computing for emerging applications
  • federating learning applications and algorithms
  • convergence of high-performance computing, cloud, edge, and other distributed computing resources
  • distributed systems and storage for AI, and AI for distributed systems and storage

Published Papers (2 papers)

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Research

20 pages, 5551 KiB  
Article
OctoFAS: A Two-Level Fair Scheduler That Increases Fairness in Network-Based Key-Value Storage
by Yeohyeon Park, Junhyeok Park, Junghwan Park, Awais Khan, Kyeongpyo Kim, Sung-Soon Park and Youngjae Kim
Electronics 2024, 13(3), 619; https://doi.org/10.3390/electronics13030619 - 1 Feb 2024
Viewed by 604
Abstract
We identified a fairness problem in a network-based key-value storage system using Intel Storage Performance Development Kit (SPDK) in a multitenant environment. In such an environment, each tenant’s I/O service rate is not fairly guaranteed compared to that of other tenants. To address [...] Read more.
We identified a fairness problem in a network-based key-value storage system using Intel Storage Performance Development Kit (SPDK) in a multitenant environment. In such an environment, each tenant’s I/O service rate is not fairly guaranteed compared to that of other tenants. To address the fairness problem, we propose OctoFAS, a two-level fair scheduler designed to improve overall throughput and fairness among tenants. The two-level scheduler of OctoFAS consists of (i) inter-core scheduling and (ii) intra-core scheduling. Through inter-core scheduling, OctoFAS addresses the load imbalance problem that is inherent in SPDK on the storage server by dynamically migrating I/O requests from overloaded cores to underloaded cores, thereby increasing overall throughput. Intra-core scheduling prioritizes handling requests from starving tenants over well-fed tenants within core-specific event queues to ensure fair I/O services among multiple tenants. OctoFAS is deployed on a Linux cluster with SPDK. Through extensive evaluations, we found that OctoFAS ensures that the total system throughput remains high and balanced, while enhancing fairness by approximately 10% compared to the baseline, when both scheduling levels operate in a hybrid fashion. Full article
(This article belongs to the Special Issue Distributed Computing and Storage Challenges for Emerging Applications)
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18 pages, 5360 KiB  
Article
Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink
by Qian Zou, Wen Xiong, Xiaoxuan Wang and Fukun Qin
Electronics 2023, 12(18), 3897; https://doi.org/10.3390/electronics12183897 - 15 Sep 2023
Viewed by 752
Abstract
Bus transportation system has become the primary mode of traffic for urban residents. Every day, thousands of buses provide services for millions of passengers. Efficiently monitoring bus trajectories is essential for evaluating service quality and ensuring public safety. In this study, we propose [...] Read more.
Bus transportation system has become the primary mode of traffic for urban residents. Every day, thousands of buses provide services for millions of passengers. Efficiently monitoring bus trajectories is essential for evaluating service quality and ensuring public safety. In this study, we propose a Flink-based solution to detect anomalies for bus trajectories in real time. Specifically, it can identify two types of anomalies. The first type is when a bus deviates from its designated route during a trip. The second type is when a bus arrives at a scheduled stop along its route but fails to stop. This solution employs CEP (Complex Event Processing) to determine bus arrival events and control the detection process. In this process, it utilizes the state management mechanism to save and update a bus’s actual trajectory, which is derived from the raw GPS trajectory and maintained as a stop sequence. Subsequently, it uses LCSS (Longest Common Subsequence) to measure the trajectory similarity between the actual bus trajectory and the scheduled route. We validate the solution using a large-scale real dataset in a Flink cluster with six virtual machines. The experimental results show that (1) each core can handle anomaly detection on 12.5 buses simultaneously and (2) the detection accuracies of the two anomalies are 90.5% and 89.3%, respectively. Full article
(This article belongs to the Special Issue Distributed Computing and Storage Challenges for Emerging Applications)
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