Research

19 pages, 474 KiB

Open AccessArticle

TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities

by Ahmad Naseem Alvi, Bakhtiar Ali, Mohamed Saad Saleh, Mohammed Alkhathami, Deafallah Alsadie and Bushra Alghamdi

Appl. Sci. 2023, 13(23), 12799; https://doi.org/10.3390/app132312799 - 29 Nov 2023

Cited by 1 | Viewed by 629

Abstract

Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on [...] Read more.

Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on IoTs that are composed of sensor networks with limited processing and computing capabilities and are connected with different types of networks. Due to limited computational capability, IoT sensor nodes require more time to compute different tasks and are required to offload some tasks to remotely placed cloud servers for task execution. Fog nodes are preferred over the cloud as they are placed in close access to IoT nodes distributed in different networks. Different types of networks make it more vulnerable to malicious attacks. Malicious nodes offload complex and high computing tasks to fog nodes to compromise their performance and create delays in the computing tasks of legitimate nodes. In addition, fog nodes even after removing the malicious nodes are unable to process all the legitimate tasks within a specific time frame. In this work, a Trust-based Efficient Task Execution Scheme (TETES) is proposed for fog node that scrutinizes the offloaded tasks sent by the malicious nodes and efficiently execute most of the trusted tasks within a stipulated time cycle. The simulated results show that TETES execute more offloaded tasks as compared to well-known First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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23 pages, 560 KiB

Open AccessArticle

Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models

by Prem Rajendran, Sarthak Maloo, Rohan Mitra, Akchunya Chanchal and Raafat Aburukba

Appl. Sci. 2023, 13(23), 12577; https://doi.org/10.3390/app132312577 - 22 Nov 2023

Viewed by 1170

Abstract

As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web [...] Read more.

As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web Services (AWS), with a specific focus on their suitability for deploying object-detection algorithms. The analysis covers both quantitative metrics—encompassing upload and download times, throughput, and inference time—and qualitative assessments like cost effectiveness, machine learning resource availability, deployment ease, and service-level agreement (SLA). Through the deployment of the YOLOv8 object-detection model, this study measures these metrics on both platforms, providing empirical evidence for platform evaluation. Furthermore, this research examines general platform availability and information accessibility to highlight differences in qualitative aspects. This paper concludes that Azure excels in download time (average 0.49 s/MB), inference time (average 0.60 s/MB), and throughput (1145.78 MB/s), and AWS excels in upload time (average 1.84 s/MB), cost effectiveness, ease of deployment, a wider ML service catalog, and superior SLA. However, the decision between either platform is based on the importance of their performance based on business-specific requirements. Hence, this paper ends by presenting a comprehensive comparison based on business-specific requirements, aiding stakeholders in making informed decisions when selecting a cloud platform for their machine learning projects. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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17 pages, 3884 KiB

Open AccessArticle

Exploring Performance Degradation in Virtual Machines Sharing a Cloud Server

by Hamza Ahmed, Hassan Jamil Syed, Amin Sadiq, Ashraf Osman Ibrahim, Manar Alohaly and Muna Elsadig

Appl. Sci. 2023, 13(16), 9224; https://doi.org/10.3390/app13169224 - 14 Aug 2023

Viewed by 1039

Abstract

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is [...] Read more.

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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33 pages, 5332 KiB

Open AccessArticle

Fault-Prone Software Requirements Specification Detection Using Ensemble Learning for Edge/Cloud Applications

by Fatin Nur Jannah Muhamad, Siti Hafizah Ab Hamid, Hema Subramaniam, Razailin Abdul Rashid and Faisal Fahmi

Appl. Sci. 2023, 13(14), 8368; https://doi.org/10.3390/app13148368 - 19 Jul 2023

Cited by 1 | Viewed by 1212

Abstract

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the [...] Read more.

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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18 pages, 1227 KiB

Open AccessArticle

SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair

by Abdulwahab Ali Almazroi, Misbah Liaqat, Rana Liaqat Ali and Abdullah Gani

Appl. Sci. 2023, 13(11), 6564; https://doi.org/10.3390/app13116564 - 28 May 2023

Viewed by 1587

Abstract

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, [...] Read more.

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, and accurate illness monitoring. Powered wheelchairs (PW) with sensors, computers, and other connected assistive technologies are called smart wheelchairs. Smart wheelchairs with sensing abilities are intended to offer universal connectivity using cloud and edge computing technology. Numerous outstanding people were impacted by paralyzing phenomena, including Stephen Hawking and Max Brito. The issue of legitimacy is one of the most important difficulties in e-health applications, because of how sensitive the technology is, and this needs to be appropriately handled. To safeguard the data transport, usage, and interchange between sensor nodes/smart wheelchairs and servers, e-health applications require an authentication method. As all conversations use wireless channels, e-health apps are exposed to various vulnerabilities. Additionally, the IoT has limited computational and power capacity limitations. To combat the various security risks, the present research offers a user authentication technique that is efficient and ensures anonymity. The suggested method creates a safe connection for the authorized entity and forbids unauthorized entities from accessing the Internet of Things sensor nodes. The suggested approach has lower communication and computation overheads than the traditional techniques, making it more effective. In addition, the security verification of the presented protocol is scrutinized through AVISPA. The proposed scheme, on average, requires only 12.4% more computation cost to execute. Compared to the existing approaches, the suggested protocol’s extra computational cost can be compensated for by its enhanced security, while the suggested method’s communication cost is 46.3% smaller. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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19 pages, 3464 KiB

Open AccessArticle

LFDC: Low-Energy Federated Deep Reinforcement Learning for Caching Mechanism in Cloud–Edge Collaborative

by Xinyu Zhang, Zhigang Hu, Meiguang Zheng, Yang Liang, Hui Xiao, Hao Zheng and Aikun Xu

Appl. Sci. 2023, 13(10), 6115; https://doi.org/10.3390/app13106115 - 16 May 2023

Cited by 1 | Viewed by 1060

Abstract

The optimization of caching mechanisms has long been a crucial research focus in cloud–edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings. Deep reinforcement learning (DRL), with its ability to perceive the environment and develop intelligent policies [...] Read more.

The optimization of caching mechanisms has long been a crucial research focus in cloud–edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings. Deep reinforcement learning (DRL), with its ability to perceive the environment and develop intelligent policies online, has been widely employed for designing caching strategies. Recently, federated learning, when combined with DRL, has been in gaining popularity for optimizing caching strategies and protecting data training privacy from eavesdropping attacks. However, online federated deep reinforcement learning algorithms face high environmental dynamics, and real-time training can result in increased training energy consumption despite improving caching efficiency. To address this issue, we propose a low-energy federated deep reinforcement learning strategy for caching mechanisms (LFDC) that balances caching efficiency and training energy consumption. The LFDC strategy encompasses a novel energy efficiency model, a deep reinforcement learning mechanism, and a dynamic energy-saving federated policy. Our experimental results demonstrate that the proposed LFDC strategy significantly outperforms existing benchmarks in terms of energy efficiency. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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21 pages, 4296 KiB

Open AccessArticle

WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow

by Shaonan Li, Yongqiang Xie, Zhongbo Li, Jin Qi, Junjie Xie and Zexin Yan

Appl. Sci. 2023, 13(10), 5867; https://doi.org/10.3390/app13105867 - 10 May 2023

Viewed by 1251

Abstract

The exponential growth of video data in networks has led to video flow occupying a significant proportion of network traffic, causing congestion and poor service quality. To address this issue, it is crucial to quickly offload data and ensure high-quality service for users, [...] Read more.

The exponential growth of video data in networks has led to video flow occupying a significant proportion of network traffic, causing congestion and poor service quality. To address this issue, it is crucial to quickly offload data and ensure high-quality service for users, especially in the context of cloud-edge collaboration. We propose a strategy for collaborative data offloading between cloud and edge computing, analogous to water flow (WFO). When users simultaneously access the same data from the same data source, WFO can serve more users within the limited bandwidth of the cloud while maintaining the quality of service. WFO creates a water flow-like data link between nodes to enable data offloading, using multiple nodes in collaboration to offload data for a single node. Experimental results show that compared with typical methods, such as fair-queue and first-come-first-served, WFO can significantly reduce the data offloading delay, guarantee service quality, and effectively reduce network congestion. Moreover, the number of service nodes can be as numerous as possible. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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15 pages, 4020 KiB

Open AccessArticle

Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF

by Amin Sadiq, Hassan Jamil Syed, Asad Ahmed Ansari, Ashraf Osman Ibrahim, Manar Alohaly and Muna Elsadig

Appl. Sci. 2023, 13(8), 4700; https://doi.org/10.3390/app13084700 - 07 Apr 2023

Viewed by 2914

Abstract

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides [...] Read more.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes’ scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target’s service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes. Full article

(This article belongs to the Special Issue Edge and Cloud Computing Systems and Applications)

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