Traditional Cloud Computing Research Articles

V2X Communication Computing Resource Collaboration Technology Based on Deep Reinforcement Learning

In traditional cloud computing, data needs to be transferred from distributed data sources to remote cloud data centers for high-performance computing. However, tasks often experience network transmission delays of hundreds of milliseconds or more from data sources to cloud data centers, which does not meet the need for low latency in V2X (Vehicle to Everything) systems. On the basis of the existing heterogeneous task scenario, this paper proposes a communication computing resource collaboration technology scheme based on heterogeneous task, which can meet the requirements of delay and cost when facing tasks with different delay requirements and different divisibility. This scheme uses the communication computing resources of the roadside unit and the service vehicle, so that the computing task of the task vehicle can be completed within the delay requirement and the cost is minimal. We use the deep reinforcement learning method to study, and finally the performance of the proposed scheme is verified by simulation.

Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm

Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability, Fog Device Deployment (FDD) must effectively consider two crucial factors: connectivity and coverage. Network connectivity relies on FDD, determining the physical network topology, while coverage determines the accessibility of the Internet of Things (IoT) or edge devices. Both these objectives significantly impact the network performance and guarantee the network's Quality of Service (QoS). However, determining an optimal FDD method that reduces computation and communication overhead, and provides high network connectivity and coverage, is challenging. In this work, we propose an FDD algorithm that effectively connects the Fog devices for internal communication and covers maximum edge devices to entertain the requests. Firstly, FDD is formulated as a multi-objective optimization problem and then, an emerging metaheuristic Jaya Algorithm (JA) is applied to optimize the multi-objective function. The suitability of the JA, for the FDD problem, is substantiated by its rapid convergence and better computational complexity when contrasted with other contemporary population-based algorithms. In conclusion, the performance of the proposed method is assessed across a spectrum of benchmark-generated instances, each reflecting distinct Fog scenarios. The experimental outcomes showcase the proposed method's remarkable promise, especially when compared against state-of-the-art methodologies.

Functions as a service for distributed deep neural network inference over the cloud‐to‐things continuum

AbstractThe use of serverless computing has been gaining popularity in recent years as an alternative to traditional Cloud computing. We explore the usability and potential development benefits of three popular open‐source serverless platforms in the context of IoT: OpenFaaS, Fission, and OpenWhisk. To address this we discuss our experience developing a serverless and low‐latency Distributed Deep Neural Network (DDNN) application. Our findings indicate that these serverless platforms require significant resources to operate and are not ideal for constrained devices. In addition, we archived a 55% improvement compared to Kafka‐ML's performance under load, a framework without dynamic scaling support, demonstrating the potential of serverless computing for low‐latency applications.

Empowering Edge Intelligence: A Comprehensive Exploration of Fog Computing in the Era of Real-time Data Processing and the Internet of Things

Fog computing, a paradigm that extends cloud computing closer to the edge of the network, has emerged as a transformative solution to address the growing demands of latency-sensitive applications and the massive influx of data from the Internet of Things (IoT). Unlike traditional cloud computing, which centralizes data processing and storage in distant data centers, fog computing leverages a distributed architecture, bringing computation, storage, and networking resources closer to the end-users and devices. This abstract explores the key concepts, principles, and advantages of fog computing. By distributing computing resources across a continuum from the cloud to the edge, fog computing minimizes latency, enhances efficiency, and optimizes network bandwidth. The seamless integration of edge devices into the computing infrastructure facilitates real-time processing of data, enabling timely decision-making and improved user experiences in applications ranging from autonomous vehicles to smart cities. Furthermore, the abstract delves into the architectural components of fog computing, including fog nodes, gateways, and the interaction with cloud resources. The dynamic nature of fog environments allows for scalable and flexible deployments, catering to the diverse requirements of modern applications. Security and privacy concerns are also addressed, emphasizing the need for robust mechanisms to protect data integrity and user confidentiality in decentralized computing environments. As the digital landscape continues to evolve, fog computing emerges as a pivotal enabler of edge intelligence, empowering organizations to harness the benefits of real-time data processing and analysis at the edge of the network. The adoption of fog computing signifies a paradigm shift in the way we approach data management and processing, opening new avenues for innovation and efficiency in the era of the Internet of Things. Keywords: IOT, Fog Computing, Autonomous Vehicles.

Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Edge computing has emerged as an attractive alternative to traditional cloud computing by utilizing processing, network, and storage resources close to end devices, such as Internet of Things (IoT) sensors. Edge computing is still in its infancy, and resource provisioning and service scheduling remain research concerns. Kubernetes is a container orchestration tool in distributed environments. Proactive auto-scaling techniques in Kubernetes improve utilization by allocating resources based on future workload prediction. However, prediction models run on central cloud nodes, necessitating data transfer between edge and cloud nodes, which increases latency and response time. We present FedAvg-BiGRU, a proactive auto-scaling method in edge computing based on FedAvg and multi-step prediction by a Bidirectional Gated Recurrent Unit (BiGRU). FedAvg is a technique for training machine learning models in a Federated Learning (FL) model. FL reduces network traffic by exchanging only model updates rather than raw data, relieving learning models of the need to store data on a centralized cloud server. In addition, a technique for determining the number of Kubernetes pods based on the Cool Down Time (CDT) concept has been developed, preventing contradictory scaling actions. To our knowledge, our work is the first to employ FL for proactive auto-scaling in cloud and edge computing. The results demonstrate that the FedAvg-BiGRU method has a slightly higher prediction error than the load centralized processing mode, although this difference is not statistically significant. At the same time, it reduces the amount of data transmission between the edge nodes and the cloud server.

A lightweight attribute-based signcryption scheme based on cloud-fog assisted in smart healthcare.

In the environment of big data of the Internet of Things, smart healthcare is developed in combination with cloud computing. However, with the generation of massive data in smart healthcare systems and the need for real-time data processing, traditional cloud computing is no longer suitable for resources-constrained devices in the Internet of Things. In order to address this issue, we combine the advantages of fog computing and propose a cloud-fog assisted attribute-based signcryption for smart healthcare. In the constructed "cloud-fog-terminal" three-layer model, before the patient (data owner)signcryption, it first offloads some heavy computation burden to fog nodes and the doctor (data user) also outsources some complicated operations to fog nodes before unsigncryption by providing a blinded private key, which greatly reduces the calculation overhead of resource-constrained devices of patient and doctor, improves the calculation efficiency. Thus it implements a lightweight signcryption algorithm. Security analysis confirms that the proposed scheme achieves indistinguishability under chosen ciphertext attack and existential unforgeability under chosen message attack if the computational bilinear Diffie-Hellman problem and the decisional bilinear Diffie-Hellman problem holds. Furthermore, performance analysis demonstrates that our new scheme has less computational overhead for both doctors and patients, so it offers higher computational efficiency and is well-suited for application scenarios of smart healthcare.

Attribute identification based IoT fog data security control and forwarding.

As Internet of Things (IoT) applications continue to proliferate, traditional cloud computing is increasingly unable to meet the low-latency demands of these applications. The IoT fog architecture solves this limitation by introducing fog servers in the fog layer that are closer to the IoT devices. However, this architecture lacks authentication mechanisms for information sources, security verification for information transmission, and reasonable allocation of fog nodes. To ensure the secure transmission of end-to-end information in the IoT fog architecture, an attribute identification based security control and forwarding method for IoT fog data (AISCF) is proposed. AISCF applies attribute signatures to the IoT fog architecture and uses software defined network (SDN) to control and forward fog layer data flows. Firstly, IoT devices add attribute identifiers to the data they send based on attribute features. The ingress switch then performs fine-grained access control on the data based on these attribute identifiers. Secondly, SDN uses attribute features as flow table matching items to achieve fine-grained control and forwarding of fog layer data flows based on attribute identifiers. Lastly, the egress switch dynamically samples data flows and verifies the attribute signatures of the sampled data packets at the controller end. Experimental validation has demonstrated that AISCF can effectively detect attacks such as data tampering and forged matching items. Moreover, AISCF imposes minimal overhead on network throughput, CPU utilization and packet forwarding latency, and has practicality in IoT fog architecture.

A Blockchain-Assisted Intelligent Edge Cooperation System for IoT Environments With Multi-Infrastructure Providers

While edge computing has the potential to offer low-latency services and overcome the limitations of traditional cloud computing, it presents new challenges in terms of trust, security, and privacy (TSP) in IoT environments. Cooperative edge computing (CEC) has emerged as a solution to address these challenges through resource sharing among edge nodes. However, for multi-infrastructure providers, incentive and trust mechanisms among edge nodes are crucial technical issues that must be addressed alongside system latency and reliability to meet performance requirements. In this paper, we propose a blockchain-assisted intelligent edge cooperation system (BIECS) to systematically solve these issues. By leveraging blockchain technology, we construct trust among edge nodes and employ an incentive mechanism for resource sharing among multi-infrastructure providers. We formulate the system performance optimization as a multi-objective joint optimization problem and solve it efficiently through a two-stage strategy for selecting edge nodes. We first design an improved Long Short Term Memory (LSTM) model for resource prediction and then select edge nodes for executing offloaded tasks and handling the corresponding blockchain process related to each task execution. To evaluate the performance of BIECS, we implement the system based on Hyperledger Fabric and design extensive experiments. Our proposed system achieves better performance in terms of system delay, throughput, and resource utilization compared to state-of-the-art schemes for edge cooperation.

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

A Deep Anomaly Detection System for IoT-Based Smart Buildings

In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution.

Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

A Multimodel Edge Computing Offloading Framework for Deep-Learning Application Based on Bayesian Optimization

With the rapid development of Internet of Things (IoT), data generated by IoT devices are also increasing exponentially. The edge computing has alleviated the problems of limited network and transmission delay when processing tasks of IoT devices in traditional cloud computing. And with the popularity of deep learning, more and more terminal devices are embedded with AI (Artificial Intelligence) processors for higher processing capability at the edge. However, the problems of Deep Learning task offloading in heterogeneous edge computing environment have not been fully investigated. In this paper, a multi-model edge computing offloading framework is proposed, using NVIDIA Jetson edge devices (Jetson TX2, Jetson Xavier NX, and Jetson Nano) and GeForce RTX GPU servers (RTX3080 and RTX2080) to simulate the edge computing environment, and make binary computational offloading decisions for face detection tasks. We also introduce a Bayesian Optimization algorithm, namely MTPE (Modified Tree-structured Parzen Estimator), to reduce the total cost of edge computation within a time slot including response time and energy consumption, and ensure the accuracy requirements of face detection. In addition, we employ the Lyapunov model to obtain the harvesting energy between time slots to keep the energy queue stable. Experiments reveal that MTPE algorithm can achieve the globally optimal solution in fewer iterations. The total cost of multi-model edge computing framework is reduced by an average of 17.94% compared to a single-model framework. In contrast to the Double Deep Q-Network (DDQN), our proposed algorithm can decrease the computational consumption by 23.01% for obtaining the offloading decision.

Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions

Fast-growing smart city applications, such as smart delivery, smart community, and smart health, are generating big data that are widely distributed on the internet. IoT (Internet of Things) systems are at the centre of smart city applications, as traditional cloud computing is insufficient for satisfying the critical requirements of smart IoT systems. Due to the nature of smart city applications, massive IoT data may contain sensitive information; hence, various privacy-preserving methods, such as anonymity, federated learning, and homomorphic encryption, have been utilised over the years. Furthermore, limited concern has been given to the resource consumption for data privacy-preserving in edge computing environments, which are resource-constrained when compared with cloud data centres. In particular, differential privacy (DP) has been an effective privacy-preserving method in the edge computing environment. However, there is no dedicated study on DP technology with a focus on smart city applications in the edge computing environment. To fill this gap, this paper provides a comprehensive study on DP in edge computing-based smart city applications, covering various aspects, such as privacy models, research methods, mechanisms, and applications. Our study focuses on five areas of data privacy, including data transmitting privacy, data processing privacy, data model training privacy, data publishing privacy, and location privacy. In addition, we investigate many potential applications of DP in smart city application scenarios. Finally, future directions of DP in edge computing are envisaged. We hope this study can be a useful roadmap for researchers and practitioners in edge computing enable smart city applications.

Partitioning multi-layer edge network for neural network collaborative computing

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition.

Optimizing Performance in Mobile Applications with Edge Computing

In the ever-evolving landscape of mobile computing, the performance and responsiveness of mobile applications are critical factors influencing user satisfaction and engagement. Traditional cloud-based approaches, while effective, often suffer from latency issues due to the distance data must travel between the user’s device and the cloud servers. This latency can significantly impact the user experience, especially in applications requiring real-time data processing and responsiveness. Edge computing has emerged as a promising solution to address these challenges by bringing computational resources closer to the end user. Edge computing decentralizes data processing by distributing computational tasks across a network of local nodes, or "edges," which are closer to the source of data. This architecture reduces the distance data must travel, thereby decreasing latency and improving application performance. The integration of edge computing in mobile applications allows for faster data processing, real-time analytics, and improved overall user experience. This paper explores the optimization of mobile application performance through the implementation of edge computing strategies. It begins with an overview of edge computing principles, highlighting its benefits over traditional cloud computing, including reduced latency, improved bandwidth efficiency, and enhanced data privacy. The paper then delves into various use cases where edge computing can be particularly beneficial for mobile applications, such as augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) applications. By examining these use cases, the paper illustrates how edge computing can address specific performance challenges and enhance the functionality of mobile apps.

A Secure Recommender System Model for Service Placement in Wireless Networks

Edge or fog computing are being used to reduce latency between end devices and traditional cloud computing. The latest developments that have improved the hardware aspects of wireless sensors and mobile networks, gave the opportunity for other enhancements if such technologies are integrated between them. Regarding this enhanced heterogeneous environment, the approach of placing services needs to reconsider the security aspects. We have developed a baseline method of a solution which fulfills the cybersecurity requirements converging with the proposed multi-tier integrated model, that enhances the K-means algorithm with added processing steps supplying the criteria of the CIA triad.

Delay reduction in MTC using SDN based offloading in Fog computing.

Fog computing (FC) brings a Cloud close to users and improves the quality of service and delay services. In this article, the convergence of FC and Software-Defined-Networking (SDN) has been proposed to implement complicated mechanisms of resource management. SDN has suited the practical standard for FC systems. The priority and differential flow space allocation have been applied to arrange this framework for the heterogeneous request in Machine-Type-Communications. The delay-sensitive flows are assigned to a configuration of priority queues on each Fog. Due to limited resources in the Fog, a promising solution is offloading flows to other Fogs through a decision-based SDN controller. The flow-based Fog nodes have been modeled according to the queueing theory, where polling priority algorithms have been applied to service the flows and to reduce the starvation problem in a multi-queueing model. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about 80%, 65%, and 60%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks

With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quality of experience (QoE) of the UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes.

ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

Since the beginning of the 21st century, computer network technology has been developing at a rapid speed. Many things that people thought were remote or even impossible 10 years ago can now become a reality, such as AR. AR is augmented reality. After simulating computer-generated text, image, 3D models, music, video, and other virtual information and applied to the real world, the two kinds of information complement each other, so as to achieve the “enhancement” of the real world. AR is augmented reality, a new field that is currently emerging. Using AR technology can add a realistic and virtual atmosphere to the real environment, giving users a better visual experience. Edge computing is a more efficient algorithm based on cloud computing. Traditional cloud computing, which requires all terminal devices to go from the cloud to the edge, consumes more energy and time, while using edge computing is more efficient. Applying edge computing to AR can render videos and pictures in real time, giving users a better experience. This paper is aimed at studying the ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality and using this technology to propose and design an AR computing edge model. And compared with the traditional AR technology, the final experimental results show that the use of ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality can reduce the minimum delay time by 1/3 compared with traditional cloud computing. The signal-to-noise ratio of the fused animation effect is significantly increased. Compared with the traditional gray value fusion, it only needs 1/2 of the number of calculations to achieve the same fusion effect.