Cloud Computing and Social Network Applications Using Symmetric/Asymmetric Methods

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 5421

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Computer and Decision Science, National Taipei University of Business, Taipei City 100025, Taiwan
Interests: mobile computing; wireless sensor networks; internet technology; cloud computing and social networks

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taitung University, No. 369, University Rd., Taitung 95092, Taiwan
Interests: intelligent communication system; IOT; cloud computing

Special Issue Information

Dear Colleagues,

Cloud computing has facilitated recent developments in various areas of computing and networking. Applications in the cloud are flourishing, and they help to promote the connections between people and people, between people and data, and between people and computing. One of the important applications is social networks such as Facebook, Weibo, Line, etc. The Cloud can be seen as an asymmetric platform where users store their data without actually knowing where they reside. On the other hand, social networks possess symmetric characteristics where users communicate in equal status. Further, social networks are also Cloud applications, so they also have asymmetric properties.

In this Special Issue, we welcome papers studying the various symmetric and/or asymmetric properties in the cloud computing and social network area. Topics of interest include but are not limited to the following:

  • Applications of cloud computing or social networks;
  • Symmetric or asymmetric characteristics in cloud computing or social networks;
  • Advantages or disadvantages in cloud computing or social networks;
  • AI in cloud computing and social networks;
  • Big data in cloud computing and social networks;
  • Trends in cloud computing and social networks.

Prof. Dr. Ruay-Shiung Chang
Prof. Dr. Yaochung Chang
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. Symmetry 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 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

  • symmetry/asymmetry
  • cloud computing
  • social networks
  • AI
  • big data

Published Papers (3 papers)

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

Research

28 pages, 2899 KiB  
Article
Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach
by Mazen Farid, Heng Siong Lim, Chin Poo Lee and Rohaya Latip
Symmetry 2023, 15(11), 2047; https://doi.org/10.3390/sym15112047 - 10 Nov 2023
Cited by 1 | Viewed by 1315
Abstract
One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem [...] Read more.
One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study. Full article
Show Figures

Figure 1

22 pages, 3773 KiB  
Article
A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces
by Insha Naz, Sameena Naaz, Parul Agarwal, Bhavya Alankar, Farheen Siddiqui and Javed Ali
Symmetry 2023, 15(5), 1025; https://doi.org/10.3390/sym15051025 - 05 May 2023
Cited by 2 | Viewed by 1615
Abstract
Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two [...] Read more.
Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms. Full article
Show Figures

Figure 1

17 pages, 679 KiB  
Article
Optimizing the Routing of Urban Logistics by Context-Based Social Network and Multi-Criteria Decision Analysis
by Mei-Yu Wu, Chih-Kun Ke and Szu-Cheng Lai
Symmetry 2022, 14(9), 1811; https://doi.org/10.3390/sym14091811 - 01 Sep 2022
Cited by 10 | Viewed by 1491
Abstract
The proper vehicle-route selection is a key challenge affecting the quality of urban logistics since any delay may cause disasters. This study proposes a novel approach of using symmetry/asymmetry traffic context data and multi-criteria decision analysis to optimize vehicle-route selection as part of [...] Read more.
The proper vehicle-route selection is a key challenge affecting the quality of urban logistics since any delay may cause disasters. This study proposes a novel approach of using symmetry/asymmetry traffic context data and multi-criteria decision analysis to optimize vehicle-route selection as part of urban-logistical planning. The traffic context data are collected from official urban transportation databases and metadata of Google Maps route planning to construct a context-based social network. The traffic features and routing criteria have symmetry/asymmetry properties to influence the decision of path selection. Multi-criteria decision analysis can generate a ranking of candidate paths based on an evaluation of traffic data in context-based social networks to recommend to the deliveryman. The deliveryman can select a reasonable path for delivering products according to the ranking of candidate paths. A case study demonstrates the steps of the proposed approach. Experimental results show that the precision is 79.65%, recall is 80.70%, and F1-score is 80.17%, thus proving the vehicle-route recommendation effectiveness. The contribution of this work is to optimize traffic-routing solutions for improved urban logistics in smart cities. It helps deliverymen send products as soon as possible to customers to retain quality, especially in cold-chain logistics. Full article
Show Figures

Figure 1

Back to TopTop