Cloud Computing and Distributed Big Data Analytics

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 (20 January 2024) | Viewed by 930

Special Issue Editor

School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
Interests: edge-cloud collaborative intelligence; privacy computing; distributed big data analytics

Special Issue Information

Dear Colleagues,

Both cloud computing and big data play a huge role in our digital society. In particularly, cloud computing allows us to easily process big data via a “software as a service” model. In turn, big data also greatly fuel cloud-computing-based application development. The two, linked together, allow people with great ideas but limited resources a chance at business success. They also allow established businesses to utilize data that they collect but previously had no way of analyzing. However, due to the exponentially increasing data and the emerging computing demands in the post-pandemic era, cloud computing and big data applications are facing enormous challenges in many aspects, such as infrastructure, system design, computing paradigm, trust, security and privacy, etc.

In this Special Issue, recent efforts and advances made for cloud computing, big data, and their applications will be discussed. The topics of interest for this Special Issue include but are not limited to the following:

  • Cloud-native databases and applications;
  • Cloud gaming, virtual reality (VR), and augmented reality (AR);
  • Artificial intelligence (AI) and AI-driven clouds;
  • The Internet of Things, edge computing, and cloud–edge collaboration;
  • Distributed clouds and federated learning;
  • Serverless computing and edge clouds;
  • Trust and Blockchain;
  • System and data security;
  • Privacy computing and privacy-enhanced technologies.

Dr. Peng Zhao
Guest Editor

Manuscript Submission Information

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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
  • big data
  • cloud-based applications
  • data-based AI algorithms
  • data-driven applications
  • data security
  • data privacy

Published Papers (1 paper)

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Research

18 pages, 2047 KiB  
Article
Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
by Umer Nauman, Yuhong Zhang, Zhihui Li and Tong Zhen
Appl. Sci. 2024, 14(2), 540; https://doi.org/10.3390/app14020540 - 08 Jan 2024
Cited by 1 | Viewed by 584
Abstract
Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, [...] Read more.
Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively. Full article
(This article belongs to the Special Issue Cloud Computing and Distributed Big Data Analytics)
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