Advances in High Performance Cloud Computing

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: closed (31 August 2022) | Viewed by 7729

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 has revolutionized the modern IT infrastructure and software industry. Cloud technologies and paradigms, such as on-demand services, resource virtualization, elastic computing and the pay-as-you-use pricing model, help improve the accessibility, agility and cost efficiency of computing resources. Today, clouds have become one of the most popular platforms for hosting various applications, including the Web, IoT, AI and enterprise software. However, many challenges remain for cloud computing to meet the service requirements of applications. Among the most important challenges is performance. For instance, IoT applications require a low network latency delay, deep learning requires high computing throughput for model training and data-intensive applications need high IO performance. Addressing the performance issues in the cloud environment is particularly challenging as resources are shared, virtualized and accessed remotely. Many efforts have been made to improve the performance of clouds in various aspects. Resource management strategies are still critical for performance optimization in clouds. However, new technologies and architectures have also been discussed. To minimize virtualization overhead, there is a growing trend to use lightweight virtualization techniques, such as container. To avoid long latency delay, hybrid cloud architectures, such as edge clouds and geo-distributed clouds, have emerged. Finally, techniques such as modeling, prediction, monitoring and benchmarking are also essential for understanding and optimizing cloud performance. The purpose of this Special Issue is to provide the academic and industrial communities with an excellent platform covering all aspects of current work on the emerging trends of cloud computing related to performance. Potential topics include, but are not limited to, the following:

  • Cloud computing and services;
  • Resource virtualization, including container and virtual machine;
  • Resource management and scheduling;
  • High-performance computing and runtime environment;
  • Performance modeling, analysis and optimization;
  • Emerging applications and technologies for high-performance clouds;
  • Hybrid cloud architectures, such as edge computing, geo-distributed clouds and hybrid clouds.

Dr. Jerry Chou
Dr. Wu-Chun Chung
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.

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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 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

  • services
  • virtualization
  • container
  • performance
  • resource management
  • distributed systems
  • data centers
  • clouds
  • benchmarking and modeling

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Published Papers (2 papers)

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Research

20 pages, 616 KiB  
Article
The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase
by Chia-Ping Tsai, Che-Wei Chang, Hung-Chang Hsiao and Haiying Shen
Future Internet 2022, 14(3), 92; https://doi.org/10.3390/fi14030092 - 15 Mar 2022
Cited by 3 | Viewed by 3069
Abstract
Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to [...] Read more.
Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments. Full article
(This article belongs to the Special Issue Advances in High Performance Cloud Computing)
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18 pages, 930 KiB  
Article
A Queueing-Based Model Performance Evaluation for Internet of People Supported by Fog Computing
by Laécio Rodrigues, Joel J. P. C. Rodrigues, Antonio de Barros Serra and Francisco Airton Silva
Future Internet 2022, 14(1), 23; https://doi.org/10.3390/fi14010023 - 8 Jan 2022
Cited by 12 | Viewed by 3743
Abstract
Following the Internet of Things (IoT) and the Internet of Space (IoS), we are now approaching IoP (Internet of People), or the Internet of Individuals, with the integration of chips inside people that link to other chips and the Internet. Low latency is [...] Read more.
Following the Internet of Things (IoT) and the Internet of Space (IoS), we are now approaching IoP (Internet of People), or the Internet of Individuals, with the integration of chips inside people that link to other chips and the Internet. Low latency is required in order to achieve great service quality in these ambient assisted living facilities. Failures, on the other hand, are not tolerated, and assessing the performance of such systems in a real-world setting is difficult. Analytical models may be used to examine these types of systems even in the early phases of design. The performance of aged care monitoring systems is evaluated using an M/M/c/K queuing network. The model enables resource capacity, communication, and service delays to be calibrated. The proposed model was shown to be capable of predicting the system’s MRT (mean response time) and calculating the quantity of resources required to satisfy certain user requirements. To analyze data from IoT solutions, the examined architecture incorporates cloud and fog resources. Different circumstances were analyzed as case studies, with four main characteristics taken into consideration. These case studies look into how cloud and fog resources differ. Simulations were also run to test various routing algorithms with the goal of improving performance metrics. As a result, our study can assist in the development of more sophisticated health monitoring systems without incurring additional costs. Full article
(This article belongs to the Special Issue Advances in High Performance Cloud Computing)
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