Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet—2nd Edition

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1632

Special Issue Editors

Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan
Interests: cloud computing; high-performance computing; distributed systems
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Guest Editor
Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
Interests: cloud–fog computing; network function virtualization; federated machine learning; distributed systems; peer-to-peer computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud computing and high-performance computing have emerged in the past two decades for the computer and communications technology industry. With the maturity of cloud technologies and service models, the cloud-first strategy has become essential for business operations and the maintenance of a variety of applications, such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), fifth generation (5G) communications, telematics, and so on. However, high-performance computing continues to face many challenges, particularly regarding its ability to fulfill the service requirements of emerging applications. For example, 5G and telematic applications require ultra-low latency for network delay, AI and machine learning applications require high computing throughputs for model training, big data applications require a high storage I/O performance for data-intensive processing, IoT applications require a high-speed network I/O for rapid packet processing from massive edge devices or connected vehicles, and emerging techniques related to next-generation internet require serverless computing and function as a service. In order to connect experts from the academic and industrial communities, we invite researchers to submit original research articles that present state-of-the-art techniques to this Special Issue on cloud and high-performance computing. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Emerging technologies with enhanced performance awareness on cloud and high-performance computing;
  • Performance benchmarking and analysis for emerging cloud and high-performance computing systems;
  • Novel algorithms and models for parallel and distributed processing;
  • Resource management and job scheduling for high-performance AI and ML on distributed systems;
  • Elastic resource provisioning for cloud–fog and edge computing frameworks;
  • Service orchestration for virtualized or containerized network functions on 5G or beyond 5G clouds;
  • Serverless and function as a service for next-generation computing.

Dr. Jerry Chou
Dr. Wu-Chun Chung
Guest Editors

Manuscript Submission Information

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Keywords

  • virtualization, container, and microservices
  • resource management and orchestration
  • performance benchmarking and modeling
  • cluster, distributed systems, and data centers
  • cloud, fog, and edge computing
  • AI, machine learning, and big data processing
  • serverless and function as a service

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Related Special Issue

Published Papers (2 papers)

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32 pages, 3449 KiB  
Article
Optimizing Internet of Things Services Placement in Fog Computing Using Hybrid Recommendation System
by Hanen Ben Rjeb, Layth Sliman, Hela Zorgati, Raoudha Ben Djemaa and Amine Dhraief
Future Internet 2025, 17(5), 201; https://doi.org/10.3390/fi17050201 - 30 Apr 2025
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Abstract
Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the [...] Read more.
Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the optimal assignment of services to these resources (the service placement problem (SPP)) is essential. Numerous studies have attempted to tackle this issue. However, to the best of our knowledge, none of the previously proposed works took into consideration the dynamic context awareness and the user preferences for IoT service placement. To deal with this issue, we propose a hybrid recommendation system for service placement that combines two techniques: collaborative filtering and content-based recommendation. By considering user and service context, user preferences, service needs, and resource availability, the proposed recommendation system provides optimal placement suggestions for each IoT service. To assess the efficiency of the proposed system, a validation scenario based on Internet of Drones (IoD) was simulated and tested. The results show that the proposed approach leads to a considerable reduction in waiting time and a substantial improvement in resource utilization and the number of executed services. Full article
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29 pages, 2569 KiB  
Review
Survey on Secure Scientific Workflow Scheduling in Cloud Environments
by Hadeel Amjed Saeed, Sufyan T. Faraj Al-Janabi, Esam Taha Yassen and Omar A. Aldhaibani
Future Internet 2025, 17(2), 51; https://doi.org/10.3390/fi17020051 - 21 Jan 2025
Cited by 1 | Viewed by 1022
Abstract
In cloud computing environments, the representation and management of data through workflows are crucial to ensuring efficient processing. This paper focuses on securing scientific workflow scheduling, which involves executing complex data-processing tasks with specific dependencies. The security of intermediate data, often transmitted between [...] Read more.
In cloud computing environments, the representation and management of data through workflows are crucial to ensuring efficient processing. This paper focuses on securing scientific workflow scheduling, which involves executing complex data-processing tasks with specific dependencies. The security of intermediate data, often transmitted between virtual machines during workflow execution, is critical for maintaining the integrity and confidentiality of scientific workflows. This review analyzes methods for securing scientific workflow scheduling in cloud environments, emphasizing the application of security principles such as confidentiality, authentication, and integrity. Various scheduling algorithms, including heuristics and metaheuristics, are examined for their effectiveness in balancing security with constraints like execution time and cost. Full article
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