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Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet

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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 July 2024) | Viewed by 18163

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Dr. Jerry Chou

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Guest Editor

Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan
Interests: cloud computing; high-performance computing; distributed systems
Special Issues, Collections and Topics in MDPI journals

Dr. Wu-Chun Chung

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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 been emerging paradigms over 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 one of the essential decisions for a business operation to sustain the 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, many challenges remain for high-performance computing 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 fast packet processing from massive edge devices or connected vehicles, and emerging trends for the next-generation Internet require serverless computing and function as a service. To bring together experts from the academic and industrial communities, we invite researchers to submit original research articles and the state-of-the-art techniques to this Special Issue on cloud and high-performance computing. Potential topics include but are not limited to the following:

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

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. Future Internet 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

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

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

Published Papers (10 papers)

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Research

16 pages, 747 KiB

Open AccessArticle

Automatically Injecting Robustness Statements into Distributed Applications

by Daniele Marletta, Alessandro Midolo and Emiliano Tramontana

Future Internet 2024, 16(11), 416; https://doi.org/10.3390/fi16110416 - 10 Nov 2024

Viewed by 371

Abstract

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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18 pages, 2216 KiB

Open AccessArticle

Optimizing Data Parallelism for FM-Based Short-Read Alignment on the Heterogeneous Non-Uniform Memory Access Architectures

by Shaolong Chen, Yunzi Dai, Liwei Liu and Xinting Yu

Future Internet 2024, 16(6), 217; https://doi.org/10.3390/fi16060217 - 19 Jun 2024

Viewed by 835

Abstract

Sequence alignment is a critical factor in the variant analysis of genomic research. Since the FM (Ferrainas–Manzini) index was developed, it has proven to be a model in a compact format with efficient pattern matching and high-speed query searching, which has attracted much research interest in the field of sequence alignment. Such characteristics make it a convenient tool for handling large-scale sequence alignment projects executed with a small memory. In bioinformatics, the massive success of next-generation sequencing technology has led to an exponential growth in genomic data, presenting a computational challenge for sequence alignment. In addition, the use of a heterogeneous computing system, composed of various types of nodes, is prevalent in the field of HPC (high-performance computing), which presents a promising solution for sequence alignment. However, conventional methodologies in short-read alignment are limited in performance on current heterogeneous computing infrastructures. Therefore, we developed a parallel sequence alignment to investigate the applicability of this approach in NUMA-based (Non-Uniform Memory Access) heterogeneous architectures against traditional alignment algorithms. This proposed work combines the LF (longest-first) distribution policy with the EP (enhanced partitioning) strategy for effective load balancing and efficient parallelization among heterogeneous architectures. The newly proposed LF-EP-based FM aligner shows excellent efficiency and a significant improvement over NUMA-based heterogeneous computing platforms. We provide significantly improved performance over several popular FM aligners in many dimensions such as read length, sequence number, sequence distance, alignment speedup, and result quality. These resultant evaluation metrics cover the quality assessment, complexity analysis, and speedup evaluation of our approach. Utilizing the capabilities of NUMA-based heterogeneous computing architectures, our approach effectively provides a convenient solution for large-scale short-read alignment in the heterogeneous system. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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29 pages, 2761 KiB

Open AccessArticle

Metric Space Indices for Dynamic Optimization in a Peer to Peer-Based Image Classification Crowdsourcing Platform

by Fernando Loor, Veronica Gil-Costa and Mauricio Marin

Future Internet 2024, 16(6), 202; https://doi.org/10.3390/fi16060202 - 6 Jun 2024

Viewed by 801

Abstract

Large-scale computer platforms that process users’ online requests must be capable of handling unexpected spikes in arrival rates. These platforms, which are composed of distributed components, can be configured with parameters to ensure both the quality of the results obtained for each request and low response times. In this work, we propose a dynamic optimization engine based on metric space indexing to address this problem. The engine is integrated into the platform and periodically monitors performance metrics to determine whether new configuration parameter values need to be computed. Our case study focuses on a P2P platform designed for classifying crowdsourced images related to natural disasters. We evaluate our approach under scenarios with high and low workloads, comparing it against alternative methods based on deep reinforcement learning. The results show that our approach reduces processing time by an average of 40%. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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18 pages, 2003 KiB

Open AccessArticle

Analyzing GPU Performance in Virtualized Environments: A Case Study

by Adel Belkhiri and Michel Dagenais

Future Internet 2024, 16(3), 72; https://doi.org/10.3390/fi16030072 - 23 Feb 2024

Viewed by 2396

Abstract

The graphics processing unit (GPU) plays a crucial role in boosting application performance and enhancing computational tasks. Thanks to its parallel architecture and energy efficiency, the GPU has become essential in many computing scenarios. On the other hand, the advent of GPU virtualization has been a significant breakthrough, as it provides scalable and adaptable GPU resources for virtual machines. However, this technology faces challenges in debugging and analyzing the performance of GPU-accelerated applications. Most current performance tools do not support virtual GPUs (vGPUs), highlighting the need for more advanced tools. Thus, this article introduces a novel performance analysis tool that is designed for systems using vGPUs. Our tool is compatible with the Intel GVT-g virtualization solution, although its underlying principles can apply to many vGPU-based systems. Our tool uses software tracing techniques to gather detailed runtime data and generate relevant performance metrics. It also offers many synchronized graphical views, which gives practitioners deep insights into GVT-g operations and helps them identify potential performance bottlenecks in vGPU-enabled virtual machines. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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19 pages, 3392 KiB

Open AccessArticle

A New Dynamic Game-Based Pricing Model for Cloud Environment

by Hamid Saadatfar, Hamid Gholampour Ahangar and Javad Hassannataj Joloudari

Future Internet 2024, 16(2), 49; https://doi.org/10.3390/fi16020049 - 31 Jan 2024

Cited by 1 | Viewed by 1637

Abstract

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To establish a justifiable price, it is imperative to take into account the circumstances and requirements of both the provider and the user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The suggested approach considers three aspects: the likelihood of faults, the interplay among virtual machines, and the amount of energy used, in order to determine a justifiable price. In the game that is being proposed, the provider is responsible for determining the price of the virtual machine that can be made available to the user on each physical machine. The user, on the other hand, has the authority to choose between the virtual machines that are offered in order to run their application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. The results indicate that the suggested mechanism has successfully identified a more rational price for both the user and the provider, consequently enhancing the overall profitability of the cloud system. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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20 pages, 892 KiB

Open AccessArticle

Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures

by Herve M. Kabamba, Matthew Khouzam and Michel R. Dagenais

Future Internet 2024, 16(1), 13; https://doi.org/10.3390/fi16010013 - 29 Dec 2023

Cited by 2 | Viewed by 2624

Abstract

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With Node.js emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment’s instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a Node.js environment. Our method achieves transparency by directly instrumenting the Node.js virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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21 pages, 3136 KiB

Open AccessArticle

Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

by Tsu-Chuan Shen and Edward T.-H. Chu

Future Internet 2023, 15(10), 337; https://doi.org/10.3390/fi15100337 - 14 Oct 2023

Cited by 1 | Viewed by 2289

Abstract

Existing elevator systems lack the ability to display the number of people waiting on each floor and inside the elevator. This causes an inconvenience as users cannot tell if they should wait or seek alternatives, leading to unnecessary time wastage. In this work, we adopted edge computing by running the MobileNet–Single-Stage Object Detection (SSD) algorithm on edge devices to recognize the number of people inside an elevator and waiting on each floor. To ensure the accuracy of people counting, we fine-tuned the SSD parameters, such as the recognition frequency and confidence thresholds, and utilized the line of interest (LOI) counting strategy for people counting. In our experiment, we deployed four NVIDIA Jetson Nano boards in a four-floor building as edge devices to count people when they entered specific areas. The counting results, such as the number of people waiting on each floor and inside the elevator, were provided to users through a web app. Our experimental results demonstrate that the proposed method achieved an average accuracy of 85% for people counting. Furthermore, when comparing it to sending all images back to a remote server for people counting, the execution time required for edge computing was shorter, without compromising the accuracy significantly. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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17 pages, 3268 KiB

Open AccessArticle

Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing

by Vyas O’Neill and Ben Soh

Future Internet 2023, 15(9), 288; https://doi.org/10.3390/fi15090288 - 27 Aug 2023

Viewed by 1333

Abstract

Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability generally come at the cost of additional replication. Existing approaches have focused on the replication of virtual environments as a method of improving the reliability of cloud services. As cloud systems move towards microservices-based architectures, a more granular approach to replication is now possible. In this paper, we propose a cloud orchestration approach that balances the potential cost of failure with the spot market running cost, optimizing the resource usage of the cloud system. We present the results of empirical testing we carried out using a simulator to compare the outcome of our proposed approach to a control algorithm based on a static reliability requirement. Our empirical testing showed an improvement of between 37% and 72% in total cost over the control, depending on the specific characteristics of the cloud models tested. We thus propose that in clouds where the cost of failure can be reasonably approximated, our approach may be used to optimize the cloud redundancy configuration to achieve a lower total cost. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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19 pages, 1167 KiB

Open AccessArticle

Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS

by George Fragiadakis, Evangelia Filiopoulou, Christos Michalakelis, Thomas Kamalakis and Mara Nikolaidou

Future Internet 2023, 15(8), 277; https://doi.org/10.3390/fi15080277 - 19 Aug 2023

Cited by 2 | Viewed by 2209

Abstract

When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offer various service types with different pricing policies. Currently, infrastructure-as-a-Service (IaaS) is considered the most mature cloud service, while reserved instances, where virtual machines are reserved for a fixed period of time, have the largest market share. In this work, we employ a machine-learning approach based on the CatBoost algorithm to explore a price-prediction model for the reserve instance market. The analysis is based on historical data provided by Amazon Web Services from 2016 to 2022. Early results demonstrate the machine-learning model’s ability to capture the underlying evolution patterns and predict future trends. Findings suggest that prediction accuracy is not improved by integrating data from older time periods. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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19 pages, 2803 KiB

Open AccessArticle

A Comparative Analysis of High Availability for Linux Container Infrastructures

by Marek Šimon, Ladislav Huraj and Nicolas Búčik

Future Internet 2023, 15(8), 253; https://doi.org/10.3390/fi15080253 - 28 Jul 2023

Cited by 1 | Viewed by 2388

Abstract

In the current era of prevailing information technology, the requirement for high availability and reliability of various types of services is critical. This paper focusses on the comparison and analysis of different high-availability solutions for Linux container environments. The objective was to identify the strengths and weaknesses of each solution and to determine the optimal container approach for common use cases. Through a series of structured experiments, basic performance metrics were collected, including average service recovery time, average transfer rate, and total number of failed calls. The container platforms tested included Docker, Kubernetes, and Proxmox. On the basis of a comprehensive evaluation, it can be concluded that Docker with Docker Swarm is generally the most effective high-availability solution for commonly used Linux containers. Nevertheless, there are specific scenarios in which Proxmox stands out, for example, when fast data transfer is a priority or when load balancing is not a critical requirement. Full article

(This article belongs to the Special Issue Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet)

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