Advances in Cloud and Distributed System Applications

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 1803

Special Issue Editors


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Guest Editor
Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy
Interests: efficient resource management techniques for large-scale distributed systems, particularly cloud and edge/fog systems

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Guest Editor
Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Interests: cloud computing; edge and fog computing; large-scale data processing; mobile device forensics; resource management systems; decision support systems; medical informatics
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Special Issue Information

Dear Colleagues,

Cloud computing and distributed systems have become crucial components of modern computing, enabling the deployment and management of large-scale applications and services.

The rapid progress of these fields has led to advances in many areas, including system-level resource management (such as virtualization and containerization, as well as on-demand and elastic resource provisioning); distributed computing paradigms (such as edge computing and serverless computing); distributed data stores (e.g., distributed databases); and distributed data processing (e.g., stream processing).

This progress has also revolutionized the ways in which we develop, deploy, and maintain software applications, moving some or all of these phases to one or more remote computing infrastructures, like a public cloud.

As cloud computing and distributed systems continue to play a pivotal role in shaping the future of information technology, this Special Issue aims to explore cutting-edge research and innovative applications that contribute to the advancement of these fields.

We welcome original research papers, review articles, and case studies that address, but are not limited to, the following topics:

  • The design and analysis of distributed algorithms;
  • Fault tolerance, reliability, and availability;
  • Architectures and protocols for communication networks and peer-to-peer systems;
  • Mobile, sensor, and ad hoc networks;
  • Internet applications;
  • Computing paradigms for the cloud-to-edge continuum;
  • Large-scale data management and analytics;
  • Security and privacy considerations in cloud and distributed systems;
  • Emerging trends and challenges in distributed systems.

Submitted papers should present original research contributions, describe innovative concepts, and demonstrate practical applications or theoretical advancements. Both theoretical and applied studies are welcome. Authors are encouraged to highlight the impact and relevance of their research to both academia and industry.

We look forward to receiving your contributions.

Sincerely,

Dr. Massimo Canonico
Dr. Marco Guazzone
Guest Editors

Manuscript Submission Information

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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

  • cloud computing
  • fog and edge computing
  • distributed systems
  • peer-to-peer

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Published Papers (1 paper)

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Research

41 pages, 13625 KiB  
Article
Horizontally Scalable Implementation of a Distributed DBMS Delivering Causal Consistency via the Actor Model
by Carl Camilleri, Joseph G. Vella and Vitezslav Nezval
Electronics 2024, 13(17), 3367; https://doi.org/10.3390/electronics13173367 - 24 Aug 2024
Cited by 1 | Viewed by 1187
Abstract
Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish [...] Read more.
Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish a distributed middleware that enforces causal consistency on a widely used relational database management system (RDBMS). D-Thespis prioritises developer experience by encapsulating the intricacies of causal consistency behind an interface that is accessible over standard REST protocol. Here, we discuss several novel results. Firstly, we define a method that builds a causally consistent DBMS supporting elastic horizontal scalability. Secondly, we deliver a cloud-native implementation of the middleware and provide results and insights on 6804 benchmark configurations executed on our implementation while running on a public cloud infrastructure across several data centres. The evaluation concerns transaction processing performance, an evaluation of our implementation’s update visibility latency, and a memory profiling exercise. The results of our evaluation show that under a transactional workload, a single-node installation of our implementation of D-Thespis is 1.5 times faster than a relational DBMS running serialisable transaction processing, while the performance of the middleware can improve by more than three times when scaled horizontally within the same data centre. Our study of the memory profile of the D-Thespis implementation shows that the system distributes its memory requirements evenly across all the available machines, as it is scaled horizontally. Finally, we also illustrate how our middleware propagates data changes across geographically-distributed infrastructures in a timely manner: our tests show that most of the effects of data change operations in one data centre are available in a remote data centre within less than 300 ms over and above the network round trip latency between the two data centres. Full article
(This article belongs to the Special Issue Advances in Cloud and Distributed System Applications)
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