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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 October 2024) | Viewed by 10977

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Special Issue Editors

Dr. Ce Li

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Guest Editor

School of Mechanical Electronic and Information Engineering, China University of Mining & Technology, Beijing 100083, China
Interests: big data; cloud computing; machine learning; computer vision

Dr. Bei Guan

E-Mail Website
Guest Editor

Institute of Software, Chinese Academy of Science, Beijing 100190, China
Interests: big data analytics; cyber security analytics; threat intelligence analytics; operating system virtualization; VM scheduling in cloud computing; system security; knowledge graph

Special Issue Information

Dear Colleagues,

Cloud computing and big data are driving progress in computer science and information technologies. This poses new challenges when understanding and solving complex problems in different disciplinary fields, such as engineering, applied mathematics, computational biology, social networks, finance, business, education, transportation and telecommunications. Cloud/edge/fog computing has become a platform for the consumption and delivery of scalable services in the field of IT services to share resources in many emerging applications, such as augmented and virtual realities (AR/VR), as well as intelligent and autonomous systems. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results in the field of cloud computing and big data from design, service, and theory to its practical use.

Areas relevant to cloud computing and big data include, but are not limited to, the following:

System architecture for cloud/edge/fog computing;
Core service design and implementation in the cloud, edge and fog;
Connectivity, storage and computation in the cloud;
Big data analytics and applications, algorithms, libraries and systems;
Applications of data science, artificial intelligence, machine learning and deep learning in big data;
Big data visualization in the cloud;
Security, privacy and ethics issues related to the cloud and big data;
Power, energy and resource management.

Dr. Ce Li
Dr. Bei Guan
Guest Editors

Manuscript Submission Information

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Keywords

cloud/edge/fog computing
big data
data analysis
data mining
machine learning
artificial intelligence
deep learning
high-performance computing
parallel and distributed algorithms
innovation service design
practical platform implementation

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Published Papers (5 papers)

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Research

68 pages, 39961 KiB

Open AccessArticle

A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment

by Thalita Vergilio, Ah-Lian Kor and Duncan Mullier

Appl. Sci. 2023, 13(23), 12635; https://doi.org/10.3390/app132312635 - 23 Nov 2023

Cited by 2 | Viewed by 1670

Abstract

The field of cloud computing has witnessed tremendous progress, with commercial cloud providers offering powerful distributed infrastructures to small and medium enterprises (SMEs) through their revolutionary pay-as-you-go model. Simultaneously, the rise of containers has empowered virtualisation, providing orchestration technologies for the deployment and management of large-scale distributed systems across different geolocations and providers. Big data is another research area which has developed at an extraordinary pace as industries endeavour to discover innovative and effective ways of processing large volumes of structured, semi-structured, and unstructured data. This research aims to integrate the latest advances within the fields of cloud computing, virtualisation, and big data for a systematic approach to stream processing. The novel contributions of this research are: (1) MC-BDP, a reference architecture for big data stream processing in a containerised, multi-cloud environment; (2) a case study conducted with the Estates and Sustainability departments at Leeds Beckett University to evaluate an MC-BDP prototype within the context of energy efficiency for smart buildings. The study found that MC-BDP is scalable and fault-tolerant across cloud environments, key attributes for SMEs managing resources under budgetary constraints. Additionally, our experiments on technology agnosticism and container co-location provide new insights into resource utilisation, cost implications, and optimal deployment strategies in cloud-based big data streaming, offering valuable guidelines for practitioners in the field. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Applications)

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16 pages, 2229 KiB

Open AccessArticle

A Discrete Prey–Predator Algorithm for Cloud Task Scheduling

by Doaa Abdulmoniem Abdulgader, Adil Yousif and Awad Ali

Appl. Sci. 2023, 13(20), 11447; https://doi.org/10.3390/app132011447 - 18 Oct 2023

Cited by 3 | Viewed by 1885

Abstract

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers’ tasks concerns allocating clients’ tasks to providers’ resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey’s maximum surviving value and the predator’s minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey’s maximum surviving value and the predator’s minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Applications)

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27 pages, 8019 KiB

Open AccessArticle

An Active File Mode Transition Mechanism Based on Directory Activation Ratio in File Synchronization Service

by Mingyu Lim

Appl. Sci. 2023, 13(10), 5970; https://doi.org/10.3390/app13105970 - 12 May 2023

Viewed by 1359

Abstract

In this paper, we propose an active file mode change mechanism in which the file synchronization system of cloud storage automatically changes files in a directory of a client to the online or local mode by considering tradeoff between local storage usage and file access time according to directory activation ratio. When the directory activation ratio rises above a certain threshold, the proposed active file mode change mechanism selects online mode files in this directory based on file access delay time and local storage usage and changes them to the local mode to reduce file access delay of active IoT clients. When the directory activation ratio falls below the threshold, the active file mode change mechanism selects the local mode files based on the last access time and local storage usage and changes them to the online mode to increase available local storage. Experimental results show that the proposed active file mode change mechanism can control when and how much the client can reduce and increase the local storage usage and the file access delay by changing file mode parameters according to the requirements of various IoT devices. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Applications)

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14 pages, 1534 KiB

Open AccessArticle

Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows

by Mohammed Alaa Ala’anzy, Mohamed Othman, Emad Mohammed Ibbini, Odai Enaizan, Mazen Farid, Yousef A. Alsaaidah, Zulfiqar Ahmad and Rania M. Ghoniem

Appl. Sci. 2023, 13(4), 2644; https://doi.org/10.3390/app13042644 - 18 Feb 2023

Cited by 5 | Viewed by 2033

Abstract

Distributed computing services in cloud environments are easily accessible to end users. These services are delivered to end users via a subscription-based model. The “infrastructure as a service” (IaaS) cloud model is one of the best cloud environment models for running data- and computing-intensive applications. Real-world scientific applications are the best examples of data and computing intensiveness. For their implementation, scientific workflow applications need high-performance computational resources and a large volume of storage. The workflow tasks are linked based on computational and data interdependence. Considering the high volume and variety of scientific workflows (SWs), the resources of the IaaS cloud model require managing energy efficiently and without failure or loss. Therefore, in order to address the issues of power consumption and task failure for real-world SWs, this research work proposes a replication-based dynamic energy-aware resource provisioning (R-DEAR) strategy for SWs in an IaaS cloud environment. The proposed strategy, R-DEAR, is a resource- and service-provisioning strategy that implements a replication-based fault-tolerant and load-balancing mechanism. The proposed R-DEAR strategy schedules the tasks of a scientific workflow with a replication-based fault-tolerant mechanism. The proposed R-DEAR strategy also manages the power consumption of IaaS cloud resources dynamically through a load-sharing process. Simulation results show that the proposed R-DEAR strategy reduces energy consumption, execution cost, and execution time by 9%, 15%, and 18%, respectively, as compared with the existing state-of-the-art strategy. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Applications)

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21 pages, 3418 KiB

Open AccessArticle

MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing

by Vamsheedhar Reddy Pillareddy and Ganesh Reddy Karri

Appl. Sci. 2023, 13(2), 1101; https://doi.org/10.3390/app13021101 - 13 Jan 2023

Cited by 17 | Viewed by 2962

Abstract

Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. During resource allocation, the time and cost of execution are significant issues in the cloud-computing environment, which can potentially degrade the service quality that is provided to end users. This study proposes a method focusing on makespan, average utilization, and cost. The authors propose a task’s dynamic priority for workflow scheduling using MONWS, which uses the min-max algorithm to minimize the finish time and maximize resource utilization by calculating the dynamic threshold value for scheduling tasks on virtual machines. When the experimental results were compared to existing algorithms, MONWS achieved a 35% improvement in makespan, an 8% increase in maximum average cloud utilization, and a 4% decrease in cost. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Applications)

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