Edge Cloud Networks Research Articles

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

Efficient Data Sharing Scheme With Fine-Grained Access Control and Integrity Auditing in Terminal–Edge–Cloud Network

Efficient Data Sharing Scheme With Fine-Grained Access Control and Integrity Auditing in Terminal–Edge–Cloud Network

Neural quantile optimization for edge–cloud networking

We seek the best traffic allocation scheme for the edge–cloud networking subject to SD-WAN architecture and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge–cloud networking topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.

Efficient resource allocation for IoT applications in mobile edge computing via dynamic request scheduling optimization

This study investigates the challenges of task scheduling and resource allocation in an ultra-dense edge cloud (UDEC) network incorporating micro (macro) base stations and different equipment users (UE) under 5G technology. By utilizing multiple access interference (MAI), we enable multiple access capabilities to the UEs under the Non-Orthogonal Multiple Access (NOMA) protocol. To accommodate the dynamic nature of user tasks, we propose a two-level scheduling framework. At the upper level, the non-convex, non-linear power allocation problem for mobile users is formulated and solved using the Inertia Weight Particle Swarm Optimization (IW-PSO) algorithm to minimize the transmission energy consumption. Simultaneously, at the lower level, the joint task offloading and resource allocation problem is formulated as MINP and solved with the binary Particle Swarm Optimization (PSO) algorithm to find an optimal schedule that maximizes the response rate and the overall welfare of the system while minimizing the energy consumption. In this study, various sensitivity analyses are conducted for different parameters, including the number of mobile users, channel power, and request profile parameters, including workload and request size. Comparative analyses with other methods from recent literature consistently reveal the comparable performance of the proposed framework and confirm its effectiveness in addressing the challenges of dynamic task scheduling.

Trust Mechanism-Based Multi-Tier Computing System for Service-Oriented Edge-Cloud Networks

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. However, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this paper. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%-21.87% and reducing cost by 18.8%-50.32%.

Bi-LSTM/GRU-based anomaly diagnosis for virtual network function instance

In edge–cloud networks, Network Function Virtualization (NFV) technology provides users with high-performance Virtual Network Functions (VNFs) to replace dedicated hardware devices. In this paper, we focus on the VNF instance (VNFI) anomaly diagnosis (AD). In large-scale production data centers, VNFI anomalies are inevitable, causing degradation in network service performance, while accurate VNFI AD can minimize the losses. The AD task can be abstracted as a multi-classification problem, where the VNFI state is classified as normal or a specific fault type. Existing studies have not proposed mature VNFI AD systems and feature selection criteria, and the accuracy of AD tasks can still be improved. To address these gaps, we first design the Distributed Online Anomaly Diagnosis and Broadcasting (DOADB) system, providing online VNFI AD daemons, offline parameter training, and anomaly broadcasting. Then, we establish VNFI feature selection criteria and construct a compelling VNFI feature dataset containing simulated and real-time data for each VNFI. Based on the DOADB system and feature dataset, we adopt the Bi-LSTM and Bi-GRU networks to realize the AD multi-classification algorithm, leveraging the bidirectional network structure and long-term memory to enhance AD accuracy. Experimental results demonstrate that the proposed system and algorithm can provide effective AD daemons for VNFIs and outperform existing algorithms in both accuracy and macro F1-score metrics.

Students health physique information sharing in publicly collaborative services over edge-cloud networks

Data privacy is playing a vital role while facing the digital life aspects. Today, the world is being extensively inter-connected through the internet of things (IoT) technologies. This huge interconnectivity is bringing very wonderful capabilities for improving the quality of life (QoL) with itself, for instance, in distributed healthcare. On the other hand, there are new challenges in the interconnectivity per use. One of the most challenging issues of IoT use in social systems and digital life is secure, trustable, and reliable interactions over IoT networks such that safety, security, and privacy in both aspects of cyber and physical worlds for humankind should be planned and controlled.Due to the less physical activity of most people in the current world, fitness and aerobic sports are now an important need at any age to help them keep healthy in their cyber-physical life, specifically, for the younger student that are still in the growth ages. However, these sport activities need to be monitored seriously and closely to not put their life in danger. Herewith, healthcare services through IoT is becoming more applicable. Therefore, health information privacy for athletes is now a hot topic of investigation as we present the topic here. We propose an IoT-based physique healthcare system considering private information sharing for athletes based on data hiding at the edge of a collaborative system. The proposed system pays attention to the key factors of healthcare IoT infrastructure but it is bringing its new suggestions for more safety. Moreover, many evaluations based on different kinds of healthcare data are provided.

IoT workload offloading efficient intelligent transport system in federated ACNN integrated cooperated edge-cloud networks

Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods.

Policy network-based dual-agent deep reinforcement learning for multi-resource task offloading in multi-access edge cloud networks

Policy network-based dual-agent deep reinforcement learning for multi-resource task offloading in multi-access edge cloud networks

DRL-Based Service Function Chain Edge-to-Edge and Edge-to-Cloud Joint Offloading in Edge-Cloud Network

In this paper, we study service function chain (SFC) offloading in the edge-cloud network. Two offloading options are available for fully-loaded edge nodes: edge to edge (E2E) offloading and edge to cloud (E2C) offloading. Both E2E offloading and E2C offloading have been optimized in existing research, and Deep Reinforcement Learning (DRL) methods were adopted to achieve excellent performances. However, DRL-based SFC E2E and E2C joint offloading is still a research gap. In this paper, we propose a DRL-based SFC E2E and E2C joint offloading optimization algorithm for the edge-cloud network to maximize the utilization efficiency of edge resources. Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is applied to the optimization problem. The simulation results indicate that the proposed algorithm has excellent convergence performance and improves the utilization efficiency of edge resources in edge-cloud network scenarios of diverse scales.

Dynamic offloading strategy for computational energy efficiency of wireless power transfer based MEC networks in industry 5.0

Wireless power transfer (WPT) has emerged as a promising solution for delivering services to low-power Internet of Things (IoT) devices in a demand-driven manner. In this work, we consider the Wireless power-enabled Hybrid Mobile Edge Cloud (WPHMEC) network, which utilizes a dynamic offloading strategy (partial and binary) to maximize computational efficiency while minimizing device energy consumption. To address this challenge, we formulate a convex optimization problem to maximize the number of computational bits and minimize the energy consumption of the IIoT devices in the WPT-based MEC system for Industry 5.0 applications. To achieve this goal, we employ a reformulation approach based on block coordinate descent (BCD) to formulate an optimization problem that addresses the nonconvexity of the problem and propose a solution approach using the Karush–Kuhn–Tucker (KKT) conditions. To validate the effectiveness of the proposed scheme, extensive simulations are carried out using the Matlab software to evaluate the system’s performance and components. The results demonstrate that optimal resource allocation maximizes energy efficiency and enhances computational resource utilization, making WPHMEEC an ideal choice for addressing the evolving demands of Industry 5.0 applications.

DPS: Dynamic Pricing and Scheduling for Distributed Machine Learning Jobs in Edge-Cloud Networks

5G and Internet of Things stimulate smart applications of edge computing, such as autonomous driving and smart city. As edge computing power increases, more and more machine learning (ML) jobs will be trained in the edge-cloud network, adopting the parameter server (PS) architecture. Due to the distinct features of the edge (low-latency and the scarcity of resources), the cloud (high delay and rich computing capacity) and ML jobs (frequent communication between workers and PSs and unfixed runtime), existing cloud job pricing and scheduling algorithms are not applicable. Therefore, how to price, deploy and schedule ML jobs in the edge-cloud network becomes a challenging problem. To solve it, we propose an auction-based online framework DPS. DPS consists of three major parts: job admission control, price function design and scheduling orchestrator. DPS dynamically prices workers and PSs based on historical job information and real-time system status, and decides whether to accept the job according to the deployment cost. DPS then deploys and schedules accepted ML jobs to pursue the maximum social welfare. Through theoretical analysis, we prove that DPS can achieve a good competition ratio and truthfulness in polynomial time. Large-scale simulations and testbed experiments show that DPS can improve social welfare by at least <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula>, compared with benchmark algorithms in today&#x0027;s cloud system.

A Q-Learning-Based Load Balancing Method for Real-Time Task Processing in Edge-Cloud Networks

Edge computing has emerged as a promising solution to reduce communication delays and enhance the performance of real-time applications. However, due to the limited processing power of edge servers, it is challenging to achieve efficient task processing. In this paper, we propose a Q-learning-based load-balancing method that optimizes the distribution of real-time tasks between edge servers and cloud servers to reduce processing time. The proposed method is dynamic and adaptive, taking into account the constantly changing network status and server usage. To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are conducted in an Edge-Cloud network environment. The simulation results demonstrate that the proposed method significantly reduces processing time compared to traditional static load-balancing methods. The Q-learning algorithm enables the load-balancing system to dynamically learn the optimal decision-making strategy to allocate tasks to the most appropriate server. Overall, the proposed Q-learning-based load-balancing method provides a dynamic and efficient solution to balance the workload between edge servers and cloud servers. The proposed method effectively achieves real-time task processing in edge computing environments and can contribute to the development of high-performance edge computing systems.

Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach

Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile devices to edge or cloud servers. However, Task offloading may result in increased energy consumption and delays, and the decision to offload the task is dependent on various factors such as time-varying radio channels, available computation resources, and the location of devices. As edge-cloud computing is a dynamic and resource-constrained environment, making optimal offloading decisions is a challenging task. This paper aims to optimize offloading and resource allocation to minimize delay and meet computation and communication needs in edge-cloud computing. The problem of optimizing task offloading in the edge-cloud computing environment is a multi-objective problem, for which we employ deep reinforcement learning to find the optimal solution. To accomplish this, we formulate the problem as a Markov decision process and use a Double Deep Q-Network (DDQN) algorithm. Our DDQN-edge-cloud (DDQNEC) scheme dynamically makes offloading decisions by analyzing resource utilization, task constraints, and the current status of the edge-cloud network. Simulation results demonstrate that DDQNEC outperforms heuristic approaches in terms of resource utilization, task offloading, and task rejection.

Confidential Computing in Edge- Cloud Hierarchy

Confidential Computing in Edge- Cloud Hierarchy

Reveal: Robustness-aware VNF placement and request scheduling in edge clouds

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal–dual based algorithm with a competitive ratio of 1−ϵ,O(log1/ϵ) where ϵ∈(0,1). Experiment and simulation results show that Reveal can achieve better performance and robustness than other alternatives.

OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things

Edge computing has gained momentum in recent years, and can provide more immediate analysis of streaming video data. However, the edge devices often lack the computing capabilities (processing power, memory) to guarantee reasonable performance (e.g., accuracy, latency, throughput) for complex video analytics tasks. To alleviate this critical problem, the prevalent trend is to offload some video analytics tasks from the edge devices to the cloud. However, existing offloading approaches fail to consider the dynamic nature of the video analytical tasks (e.g., varying encoding format for different video content) and are unable to adapt system dynamics (e.g., varying workload between the edge and the cloud). To overcome the limitation of existing approaches, we develop an edge-cloud offloading performance model based on the concept of hierarchical queues. The resource constraints (e.g., computing capacity and network bandwidth) of each edge nodes and dynamic edge-cloud network conditions are used to parameterize the performance model. Since finding optimal solutions for the performance model is NP-hard, we develop a two-stage gradient-based algorithm and compare it with some state-of-the-art (SOTA) solutions (e.g., FastVA, DeepDecision, Hill Climbing). Experiments have shown our performance model's advantages and the stability of the proposed offloading approach given different systems (edge-cloud) and video analytics application dynamics.

Jointly optimized resource allocation for SDN control and forwarding planes in edge-cloud SDN-based networks

Today’s resource allocation strategies for edge-cloud network based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) architectures often make a decision of placing resource based on network locations, i.e. edge or core cloud. Unfortunately, these strategies are not optimally tailored for a growing class of Internet-of-Things (IoT) services whose traffic is designated to go through a Virtual Network Functions (VNF) service chain. Without considering the communication between SDN control plane and forwarding plane, inappropriate SDN controllers could be selected for switches, which may result in a significant setup delay of the whole service chain. In addition, the fluctuate demand of IoT services requires the entire system, that is composed of controllers, forwarding elements, i.e. switches, and physical machines, being stable during service provisioning. In this paper, we jointly address two issues, i.e. SDN controller-switch assignment and VNF placement, both are high-complexity problems. We formulate the joint optimization problem of dynamically provisioning resource for SDN controllers and VNFs in a long run. We then apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problems and employ exponentiated gradient ascent (EGA) method to find a near-optimal solution. Extensive simulation and experiments show that our approach helps to optimize the system cost up to 11 ∼ 40% depending on service demand and network size.

Correlation Anomaly Detection With Multiple Primary Attributes in Collaborative Device–Edge–Cloud Network

Anomaly detection is playing an increasingly important role in Internet of Things applications since anomalous events may cause some damage to the physical–social environment monitored by different kinds of smart object devices. In some cases, the occurrence of an anomalous event is caused by the fusion impact derived from several primary monitoring factors. Considering this, we propose a novel anomaly detection mechanism for the events with multiple decisive primary attributes in a collaborative device–edge–cloud architecture, in which a propagation and influence-based correlation is further explored in the edge layer for improving the detection efficiency. During the detection process, multiple primary attributes first cooperate to detect an anomaly in the edge layer in advance. If an anomaly occurs in the subregion managed by an edge device, social-aware interaction relationships between edge devices are further integrated to give a guidance on the detection of correlative anomaly in neighbor subregions. The cloud further analyzes the primary attributes information and the interaction relationship to determine the secondary attributes that are helpful in identifying the final anomaly. A large number of experiments show that our method is superior to the alternative methods in terms of energy consumption, detection time, and accuracy.

Cost-Minimized Computation Offloading of Online Multifunction Services in Collaborative Edge-Cloud Networks

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.