Distributed Computing Systems and 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: closed (30 September 2022) | Viewed by 7609

Special Issue Editors

Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology, 90-924 Lodz, Poland
Interests: process tomography; applied radiation; medical measurements; image and signal processing
Special Issues, Collections and Topics in MDPI journals
Institute of Applied Computer Science, Lodz University of Technology, 90-924 Lodz, Poland
Interests: computational intelligence; applied artificial intelligence; clustering; machine intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, trends in the computing industry have been towards distributed, low-cost, and high-volume units. Therefore, this Special Issue is dedicated to distributed systems, whose components are located on different networked computers and which communicate and coordinate their actions by passing messages to one another. Currently, there is a wide spectrum of types of distributed systems varying from SOA-based systems to massively multiplayer online games and peer-to-peer applications.

The control of distributed systems is a well-known challenge which requires complex computational software referred to as distributed computing. Therefore, authors should demonstrate new methods allowing to increase distributed system performance, for instance, by rebalancing resource loads and thereby avoiding networking failures caused by node overstrain.

Particularly welcome will be works that validate, at the experimental level, improved networking performance by managing resource loads and hence preventing system failures. Since such systems are generally required to operate across the Internet and different administrative domains, new algorithms fulfilling these scalability requirements without loss of performance will be a valuable contribution to the Special Issue.

We invite authors interested in the proposed topics to contribute to this Special Issue by publishing their results of research related, but not limited, to the following topics: multiprocessing, multicomputing, cybersecurity for distributed systems applications, programming paradigms for distributed systems, and load balancing algorithms.

Prof. Dr. Volodymyr Mosorov
Dr. Jacek Kucharski
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. 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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 962 KiB  
Article
Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
by Pablo Sanabria, Tomás Felipe Tapia, Rodrigo Toro Icarte and Andres Neyem
Appl. Sci. 2022, 12(14), 7137; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147137 - 15 Jul 2022
Cited by 7 | Viewed by 1599
Abstract
Due to mobile and IoT devices’ ubiquity and their ever-growing processing potential, Dew computing environments have been emerging topics for researchers. These environments allow resource-constrained devices to contribute computing power to others in a local network. One major challenge in these environments is [...] Read more.
Due to mobile and IoT devices’ ubiquity and their ever-growing processing potential, Dew computing environments have been emerging topics for researchers. These environments allow resource-constrained devices to contribute computing power to others in a local network. One major challenge in these environments is task scheduling: that is, how to distribute jobs across devices available in the network. In this paper, we propose to distribute jobs in Dew environments using artificial intelligence (AI). Specifically, we show that an AI agent, known as Proximal Policy Optimization (PPO), can learn to distribute jobs in a simulated Dew environment better than existing methods—even when tested over job sequences that are five times longer than the sequences used during the training. We found that using our technique, we can gain up to 77% in performance compared with using human-designed heuristics. Full article
(This article belongs to the Special Issue Distributed Computing Systems and Applications)
Show Figures

Figure 1

16 pages, 2535 KiB  
Article
Intelligent Cyber Security Framework Based on SC-AJSO Feature Selection and HT-RLSTM Attack Detection
by Mahima Dahiya, Nitin Nitin and Deepak Dahiya
Appl. Sci. 2022, 12(13), 6314; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136314 - 21 Jun 2022
Cited by 2 | Viewed by 1435
Abstract
Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical [...] Read more.
Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical data should be acquired by the system to implement the latest cyber security defense and protection. It also makes intelligent decisions that can provide adaptive security management and control. An intelligent cyber security framework using Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM) technique was developed in this work to elevate the security level of core system assets. To detect various attacks, the proposed framework was trained and tested on the collection of data. Owing to missing values, poor scaling, imbalanced and overlapped data, the data was primarily incomplete and inconsistent. To elevate the decision making for detecting attacks, the inconsistent or unstructured data issue was addressed. The missing values were handled by this work along with scaling performance using the developed Kernelized Robust Scaler (KRS). Using the developed Random Over Sample-Based Density-Based Spatial Clustering Associated with Noise (ROS-DBSCAN), the imbalanced and overlapped data were handled, which was followed by the relevant feature selection of data utilizing the Sine Cosine-Based Artificial Jellyfish Search Optimization (SC-AJSO) technique. The data were split under the provision of Stratified K-Fold cross-validation along being trained in the proposed HT-RLSTM. The experimental analysis depicted that better accuracy was attained in detecting attacks by the proposed work for different datasets. When analogized with prevailing state-of-the-art methods, a low false detection rate, as well as computation time, was attained by the proposed scheme. Full article
(This article belongs to the Special Issue Distributed Computing Systems and Applications)
Show Figures

Figure 1

18 pages, 1351 KiB  
Article
Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
by Eunsam Kim, Yunho Cho and Hyoseop Shin
Appl. Sci. 2021, 11(23), 11527; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311527 - 05 Dec 2021
Viewed by 1234
Abstract
Distributed appliances connected to the Internet have provided various multimedia services. In particular, networked Personal Video Recorders (PVRs) can store broadcast TV programs in their storage devices or receive them from central servers, enabling people to watch the programs they want at any [...] Read more.
Distributed appliances connected to the Internet have provided various multimedia services. In particular, networked Personal Video Recorders (PVRs) can store broadcast TV programs in their storage devices or receive them from central servers, enabling people to watch the programs they want at any desired time. However, the conventional CDNs capable of supporting a large number of concurrent users have limitations in scalability because more servers are required in proportion to the increased users. To address this problem, we have developed a time-shifted live streaming system over P2P networks so that PVRs can share TV programs with each other. We propose cooperative buffering schemes to provide the streaming services for time-shifted periods even when the number of PVRs playing back at the periods is not sufficient; we do so by utilizing the idle resources of the PVRs playing at the live broadcast time. To determine which chunks to be buffered, they consider the degree of deficiency and proximity and the ratio of playback requests to chunk copies. Through extensive simulations, we show that our proposed buffering schemes can significantly extend the time-shifting hours and compare the performance of two buffering schemes in terms of playback continuity and startup delay. Full article
(This article belongs to the Special Issue Distributed Computing Systems and Applications)
Show Figures

Figure 1

18 pages, 452 KiB  
Article
Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management
by Fatma Mbarek and Volodymyr Mosorov
Appl. Sci. 2021, 11(22), 10807; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210807 - 16 Nov 2021
Cited by 2 | Viewed by 1537
Abstract
Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of [...] Read more.
Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics. Full article
(This article belongs to the Special Issue Distributed Computing Systems and Applications)
Show Figures

Figure 1

Back to TopTop