Journal of Grid Computing最新文献

The emergence of blockchain technology has seen applications increasingly hybridise cloud storage and distributed ledger technology in the Internet of Things (IoT) and cyber-physical systems, complicating data management in decentralised applications (DApps). Because it is inefficient for blockchain technology to handle large amounts of data, effective on-chain and off-chain data management in peer-to-peer networks and cloud storage has drawn considerable attention. Space reservation is a cost-effective approach to managing cloud storage effectively, contrasting with the demand for additional space in real-time. Furthermore, off-chain data replication in the peer-to-peer network can eliminate single points of failure of DApps. However, recent research has rarely discussed optimising on-chain and off-chain data management in the blockchain-enabled IoT (BIoT) environment. In this study, the BIoT environment is modelled, with cloud storage and blockchain orchestrated over the peer-to-peer network. The asynchronous advantage actor-critic algorithm is applied to exploit intelligent agents with the optimal policy for data packing, space reservation, and data replication to achieve an intelligent data management strategy. The experimental analysis reveals that the proposed scheme demonstrates rapid convergence and superior performance in terms of average total reward compared with other typical schemes, resulting in enhanced scalability, security and reliability of blockchain-IoT networks, leading to an intelligent data management strategy.

In the internet of things, machine learning at the edge of cloud continuum is developing rapidly, providing more convenient services for design developers. The paper proposes a lidar target detection method based on scene density-awareness network for cloud continuum. The density-awareness network architecture is designed, and the context column feature network is proposed. The BEV density attention feature network is designed by cascading the density feature map with the spatial attention mechanism, and then connected with the BEV column feature network to generate the ablation BEV map. Multi-head detector is designed to regress the object center point, scale size and direction, and loss function is used for active supervision. The experiment is conducted on Alibaba Cloud services. On the validation dataset of KITTI, the 3D objects and BEV objects are detected and evaluated for three types of objects. The results show that most of the AP values of the density-awareness model proposed in this paper are higher than other methods, and the detection time is 0.09 s, which can meet the requirements of high accuracy and real-time of vehicle-borne lidar target detection.

In recent times, edge computing has arisen as a highly promising paradigm aimed at facilitating resource-intensive Internet of Things (IoT) applications by offering low-latency services. However, the constrained computational capabilities of the IoT nodes present considerable obstacles when it comes to efficient task-scheduling applications. In this paper, a nature-inspired coati optimization-based energy-aware task scheduling (CO-ETS) approach is proposed to address the challenge of efficiently assigning tasks to available edge devices. The proposed work incorporates a fitness function that effectively enhances task assignment optimization, leading to improved system efficiency, reduced power consumption, and enhanced system reliability. Moreover, we integrate blockchain with AI-driven task scheduling to fortify security, protect user privacy, and optimize edge computing in IoT-based environments. The blockchain-based approach ensures a secure and trusted decentralized identity management and reputation system for IoT edge networks. To validate the effectiveness of the proposed CO-ETS approach, we conduct a comparative analysis against state-of-the-art methods by considering metrics such as makespan, CPU execution time, energy consumption, and mean wait time. The proposed approach offers promising solutions to optimize task allocation, enhance system performance, and ensure secure and privacy-preserving operations in edge computing environments.

Accounting informatization is a crucial component of enterprise informatization, significantly impacting operational efficiency in accounting and finance. Advances in information technology have introduced automation techniques that accelerate the processing of accounting information cost-effectively. Integrating artificial intelligence, cloud computing, and edge computing is pivotal in streamlining and optimizing these processes. Traditionally, accounting informatization relied on system servers and local storage for data processing. However, the era of big data necessitates a shift to cloud computing frameworks for efficient data storage and processing. Despite the advantages of cloud storage, concerns arise regarding data security and the substantial data transactions between the cloud and source devices. To address these challenges, this research proposes a novel algorithm, Heterogeneous Distributed Deep Learning with Data Offloading (DDLO) algorithm. DDLO leverages the synergy between edge devices and cloud computing to enhance data processes. Edge computing enables rapid processing of large volumes of data at or near the data collection sites, optimizing day-to-day operations for enterprises. Furthermore, machine learning algorithms at edge devices enhance data processing efficiency, augmenting the computing environment's overall performance. The proposed DDLO algorithm fosters a hybrid machine learning approach for computing joint tasks and multi-tasking in accounting informatization. It enables dynamic resource allocation, allowing selected data or model updates to be offloaded to the cloud for complex tasks. The algorithm's performance is rigorously evaluated using key metrics, including computing time, offloading time, accuracy, and cost levels. By capitalizing on the strengths of edge computing, cloud computing, and artificial intelligence, the DDLO algorithm effectively addresses accounting informatization challenges. It empowers enterprises to process vast amounts of accounting data efficiently and securely while improving overall operational efficiency. Regarding time, using terasort in tasks offloading using DDLO consumes less milliseconds 0t 33 ms which is lesser than other techniques.

The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi–Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO.

The surge in computing demands of onboard devices in vehicles has necessitated the adoption of mobile edge computing (MEC) to cater to their computational and storage needs. This paper presents a task offloading strategy for mobile edge computing based on collaborative roadside parking cooperation, leveraging idle computing resources in roadside vehicles. The proposed method establishes resource sharing and mutual utilization among roadside vehicles, roadside units (RSUs), and cloud servers, transforming the computing task offloading problem into a constrained optimization challenge. To address the complexity of this optimization problem, a novel Hybrid Algorithm based on the Hill-Climbing and Genetic Algorithm (HHGA) is proposed, combined with the powerful Simulated Annealing (SA) algorithm. The HHGA-SA Algorithm integrates the advantages of both HHGA and SA to efficiently explore the solution space and optimize task execution with reduced delay and energy consumption. The HHGA component of the algorithm utilizes the strengths of Genetic Algorithm and Hill-Climbing. The Genetic Algorithm enables global exploration, identifying potential optimal solutions, while Hill-Climbing refines the solutions locally to improve their quality. By harnessing the synergies between these techniques, the HHGA-SA Algorithm navigates the multi-constraint landscape effectively, producing robust and high-quality solutions for task offloading. To evaluate the efficacy of the proposed approach, extensive simulations are conducted in a realistic roadside parking cooperation-based Mobile Edge Computing scenario. Comparative analyses with standard Genetic Algorithms and Hill-Climbing demonstrate the superiority of the HHGA-SA Algorithm, showcasing substantial enhancements in task execution efficiency and energy utilization. The study highlights the potential of leveraging idle computing resources in roadside parking vehicles to enhance Mobile Edge Computing capabilities. The collaborative approach facilitated by the HHGA-SA Algorithm fosters efficient task offloading among roadside vehicles, RSUs, and cloud servers, elevating overall system performance.

The number of task demands created by smart terminals is rising dramatically because of the increasing usage of industrial Internet technologies in intelligent production lines. Speed of response is vital when dealing with such large activities. The current work needs to work with the task scheduling flow of smart manufacturing lines. The proposed method addresses the limitations of the current approach, particularly in the context of task scheduling and task scheduling flow within intelligent production lines. This study concentrates on solving the multi-objective task scheduling challenge in intelligent manufacturing by introducing a task scheduling approach based on job prioritization. To achieve this, a multi-objective task scheduling mechanism was developed, aiming to reduce service latency and energy consumption. This mechanism was integrated into a cloud-edge computing framework for intelligent production lines. The task scheduling strategy and task flow scheduling were optimized using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). Lastly, thorough simulation studies evaluate Multi-PSG, demonstrating that it beats every other algorithm regarding job completion rate. The completion rate of all tasks is greater than 90% when the number of nodes exceeds 10, which satisfies the real-time demands of the related tasks in the smart manufacturing processes. The method also performs better than other methods regarding power usage and maximum completion rate.

The diagnosis of the cardiovascular disease relies heavily on the automated classification of electrocardiograms (ECG) for arrhythmia monitoring, which is often performed using machine learning (ML) algorithms. However, current ML algorithms are typically deployed using cloud-based inferences, which may not meet the reliability and security requirements for ECG monitoring. A newer solution, edge inference, has been developed to address speed, security, connection, and reliability issues. This paper presents an edge-based algorithm that combines continuous wavelet transform (CWT), and short-time Fourier transform (STFT), in a hybrid convolutional neural network (CNN) and Long Short-Term Memory (LSTM) model techniques for real-time ECG classification and arrhythmia detection. The algorithm incorporates an STFT CWT-based 1D convolutional (Conv1D) layer as a Finite Impulse Response (FIR) filter to generate the spectrogram of the input ECG signal. The output feature maps from the Conv1D layer are then reshaped into a 2D heart map image and fed into a hybrid convolutional neural network (2D-CNN) and Long Short-Term Memory (LSTM) classification model. The MIT-BIH arrhythmia database is used to train and evaluate the model. Using a cloud platform, four model versions are learned, considered, and optimized for edge computing on a Raspberry Pi device. Techniques such as weight quantization and pruning enhance the algorithms created for edge inference. The proposed classifiers can operate with a total target size of 90 KB, an overall inference time of 9 ms, and higher memory use of 12 MB while achieving up to 99.6% classification accuracy and a 99.88% F1-score at the edge. Thanks to its results, the suggested classifier is highly versatile and can be used for arrhythmia monitoring on various edge devices.

The orchestration of cloud computing infrastructures is challenging, considering the number, heterogeneity and dynamicity of the involved resources, along with the highly distributed nature of the applications that use them for computation and storage. Evidently, the volume of relevant monitoring data can be significant, and the ability to collect, analyze, and act on this data in real time is critical for the infrastructure’s efficient use. In this study, we introduce a novel methodology that adeptly manages the diverse, dynamic, and voluminous nature of cloud resources and the applications that they support. We use knowledge graphs to represent computing and storage resources and illustrate the relationships between them and the applications that utilize them. We then train GraphSAGE to acquire vector-based representations of the infrastructures’ properties, while preserving the structural properties of the graph. These are efficiently provided as input to two unsupervised machine learning algorithms, namely CBLOF and Isolation Forest, for the detection of storage and computing overusage events, where CBLOF demonstrates better performance across all our evaluation metrics. Following the detection of such events, we have also developed appropriate re-optimization mechanisms that ensure the performance of the served applications. Evaluated in a simulated environment, our methods demonstrate a significant advancement in anomaly detection and infrastructure optimization. The results underscore the potential of this closed-loop operation in dynamically adapting to the evolving demands of cloud infrastructures. By integrating data representation and machine learning methods with proactive management strategies, this research contributes substantially to the field of cloud computing, offering a scalable, intelligent solution for modern cloud infrastructures.