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Algorithms
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Distributed Algorithms and Applications
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Distributed Algorithms and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Parallel and Distributed Algorithms".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 5791

Special Issue Editors

Dr. Michele Scquizzato

E-Mail Website
Guest Editor
Department of Computer Science, University of Padova, Via VIII Febbraio, 2, 35122 Padova, Italy
Interests: design and analysis of algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gopal Pandurangan

E-Mail Website
Guest Editor
Department of Computer Science, University of Houston, Houston, TX 77204, USA
Interests: algorithms; networks; distributed computing; Big Data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are soliciting original contributions to the design, analysis, implementation, application, or limitations of distributed algorithms. Papers from all viewpoints, including theory, practice, and experimentation, are welcome. Topics of interest include, but are not limited to, the following:

  • Biological distributed algorithms;
  • Blockchain protocols;
  • Communication networks: algorithms, protocols, applications;
  • Complexity and impossibility results for distributed computing;
  • Design and analysis of distributed algorithms;
  • Distributed algorithms for big data computations;
  • Distributed data structures;
  • Distributed graph algorithms;
  • Distributed machine learning algorithms;
  • Distributed resource management and scheduling;
  • Experimental evaluation of distributed algorithms;
  • Mobile computing and autonomous agents;
  • Multiprocessor and multi-core algorithms;
  • Population protocols

Dr. Michele Scquizzato
Prof. Dr. Gopal Pandurangan
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
  • Distributed computing
  • Parallel algorithms
  • Networks
  • Complexity theory
  • Applications

Published Papers (2 papers)

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Research

15 pages, 897 KiB  
Article
Merging Discrete Morse Vector Fields: A Case of Stubborn Geometric Parallelization
by Douglas Lenseth and Boris Goldfarb
Algorithms 2021, 14(12), 360; https://doi.org/10.3390/a14120360 - 11 Dec 2021
Viewed by 1798
Abstract
We address the basic question in discrete Morse theory of combining discrete gradient fields that are partially defined on subsets of the given complex. This is a well-posed question when the discrete gradient field V is generated using a fixed algorithm which has [...] Read more.
We address the basic question in discrete Morse theory of combining discrete gradient fields that are partially defined on subsets of the given complex. This is a well-posed question when the discrete gradient field V is generated using a fixed algorithm which has a local nature. One example is ProcessLowerStars, a widely used algorithm for computing persistent homology associated to a grey-scale image in 2D or 3D. While the algorithm for V may be inherently local, being computed within stars of vertices and so embarrassingly parallelizable, in practical use, it is natural to want to distribute the computation over patches Pi, apply the chosen algorithm to compute the fields Vi associated to each patch, and then assemble the ambient field V from these. Simply merging the fields from the patches, even when that makes sense, gives a wrong answer. We develop both very general merging procedures and leaner versions designed for specific, easy-to-arrange covering patterns. Full article
(This article belongs to the Special Issue Distributed Algorithms and Applications)
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24 pages, 1157 KiB  
Article
An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
by Suleiman Sa’ad, Abdullah Muhammed, Mohammed Abdullahi, Azizol Abdullah and Fahrul Hakim Ayob
Algorithms 2021, 14(7), 200; https://doi.org/10.3390/a14070200 - 30 Jun 2021
Cited by 10 | Viewed by 2984
Abstract
Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain [...] Read more.
Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time. Full article
(This article belongs to the Special Issue Distributed Algorithms and Applications)
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