Edge Computing Server Research Articles

A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.

A Software Architecture Model to Manage Heterogeneous Data using Edge Computing in 5G Environment

With the improvement of 5G communication service, the need for edge computing has become more apparent. The Mobile Edge Computing service (MEC) has become one of the best solutions for mobile User Equipment (UE) data processing. MEC provides more context-aware local processing of UE's data. Meanwhile, the MEC needs to manage the heterogeneous nature of UEs and the type of data format they provide. Therefore, a Heterogeneous Data Service Bus is developed to manage different types of data formats and organize them in efficient manner. We propose a software architecture model to address the management of data heterogeneity within computing edges. The architecture discussion showcases its applicability and proper handling of heterogeneous data in the Edge Computing Environment.

Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.

Enhanced whale optimization algorithm for dependent tasks offloading problem in multi-edge cloud computing

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape.

A deep reinforcement learning resource allocation strategy for integrated sensing, communication and computing

The growing number of sensor nodes requires higher computational and communication resources to meet the needs of more complex applications as increasingly sophisticated mobile communications and networks evolve. As energy and time are necessary to request these resources, Edge Computing(EC) has become a key enabler to solve energy and time consumption problems in the field of Integrated Sensing, Communication, and Computing (ISCC). Computation offloading is an important technology, and Edge devices (EDs) can be used to reduce latency and energy consumption by offloading tasks to EC servers. However, the transmission also causes latency and energy consumption, so this paper proposes a DDORS scheme to solve this problem by decomposing the original problem into two subproblems and using convex optimization and DRL to obtain optimal solutions to reduce the weighted total cost of completing tasks in terms of latency and energy consumption. The simulation results indicate that the proposed DDORS may drastically reduce costs when compared to various baseline schemes.

Risk Assessment Edge Contract for Efficient Resource Allocation

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

End-to-end network slicing for edge computing optimization

User-centric services proliferated by the smart devices is getting more demanding and characteristically diversified. Fall-risk assessment, augmented reality and similar services coexist in a shared heterogeneous setting. To meet the diversified and often conflicting requirements of the services, the physical network is decomposed into virtual slices. In order to address the optimal network slicing problem for various service types with different performance requirements, this study proposes a Mixed Integer Programming (MIP) model. This optimization model aims to satisfy the demands of the services and provide complete isolation among them through virtual resources reservation, including both networking and computation. Additionally, a heuristic implementation NESECS (NEtwork Slicing for Edge Computing Services) is proposed as an efficient solution for the cases where the optimization tools remain inadequate. The performance of the proposed solutions is evaluated with an extensive set of experiments. The obtained results indicate that the proposed optimization model is capable of providing optimal or near optimal solutions for small network instances, and NESECS algorithm can provide good solutions for larger instances by eliminating the time complexity.

Enabling Efficient and Distributed Access Control for Pervasive Edge Computing Services

Enabling Efficient and Distributed Access Control for Pervasive Edge Computing Services

Joint Service Caching and Computing Resource Allocation for Edge Computing-Enabled Networks

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

Deep Reinforcement Learning Based Cooperative Partial Task Offloading and Resource Allocation for IIoT Applications

Compared with traditional cloud computing schemes, edge computing provides an effective solution towards meeting real-time, security and reliability demands of compute-intensive and delay-sensitive Industrial Internet of Things (IIoT) applications. However, many existing studies only optimize end-edge-cloud cooperative task offloading, and neglect the optimization of the communication and computation resource allocation. In this paper, the cooperative partial task offloading and resource allocation (CPTORA) framework is designed, which jointly considers cooperation among various IIoT devices, local edge computing servers (ECSs), non-local ECSs, and cloud computing servers. Then, considering the complex dynamics and unpredictability in IIoT environments, the joint optimization problem is modeled as a constrained Markov decision process. Furthermore, we propose an improved soft actor-critic-based CPTORA (ISAC-CPTORA) algorithm, to make task offloading and resource allocation decisions for each IIoT device. This algorithm innovatively introduces the idea of distributional reinforcement learning to the soft actor-critic, which can effectively reduce Q-value overestimations or underestimations. Meanwhile, this algorithm employs the prioritized experience replay to enhance its learning efficiency. Extensive laboratory experiments indicate that our CPTORA framework and ISAC-CPTORA algorithm efficiently decrease the total system costs, in contrast to various baseline frameworks and algorithms.

EdgeDrones: Co-scheduling of drones for multi-location aerial computing missions

Low altitude platform (LAP) unmanned aerial vehicles (UAVs), also called drones, are currently being exploited by Edge computing (EC) systems to execute complex resource-hungry use cases, such as virtual reality, smart cities, autonomous vehicles, etc., by attaching portable edge devices on them. However, a typical drone has limited flight time, coupled with the resource-constrained attached edge device, which can jeopardize aerial computing missions if they are not holistically taking into consideration. Moreover, the fundamental challenge is how to co-schedule multi-drone among multi-location where EC services are needed, such that drones are scheduled to maximize the utility from the activities while meeting computing resource and flight time constraints. Therefore, for a given fleet of drones and tasks across disjointed target locations in a city, we derive a machine learning (ML) linear regression model that estimates these tasks resource requirement and execution time. Leveraging this estimation values, we jointly consider each drone’s flight time availability and its attached edge device resource capacity, and formulate a novel Multi-Location Capacitated Mission Scheduling Problem (MLCMSP) that selects suitable drones and co-schedules their flight routes with the least total distance to visit and execute tasks at the target locations. Then, we show that faster scheduling and execution of complex tasks at each location, while considering the inter-task dependencies is important to achieve effective solution for our MLCMSP. Hence, we further propose EdgeDrones, a variant bin-packing optimization approach through gang-scheduling of inter-dependent tasks that co-schedules and co-locates tasks tightly so as to achieve faster execution time, as well as to fully utilize available resources. Extensive experiments on Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) show that EdgeDrones achieves up to 73% higher resource utilization, up to 17.6 times faster executions, and up to 2.87 times faster flight travel time compared to the baseline approaches.

A Parallel Intelligence-Driven Resource Scheduling Scheme for Digital Twins-Based Intelligent Vehicular Systems

Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.

Green Edge Intelligence for Smart Management of a FANET in Disaster-Recovery Scenarios

Disruption of the ground communication infrastructure in emergency scenarios makes post-disaster rescue operations very complicate. This paper proposes to use edge intelligence to support rescue operators in managing these emergency scenarios. A set of Unmanned Aerial Vehicles (UAV), organized as Flying Ad-Hoc network (FANET), autonomously takeoff and land to provide emergency operators with edge computing services. A charging station for batteries is supplied by a renewable-energy generator. The FANET Controller applies model-based Reinforcement Learning to decide how many UAVs have to take off, according to the current edge-computing service requests and the power availability, and a forecast of them. The optimal management policy has to provide the necessary level of edge-computing avoiding wide use of satellite channels in a short-time horizon during low green-energy generation and high service request periods. Results highlight that the optimal policy is an efficient modification of the greedy one, i.e., the policy enabling the takeoff of all the necessary UAVs without being care of challenging events in the future. A deep analysis has revealed that the level of modification depends on the combination of the edge-computing service request and the green power availability.

Deep Q-Learning-Based Dynamic Network Slicing and Task Offloading in Edge Network

Recently, Edge Computing (EC) has become a promising enabler to support emerging applications in 5G mobile networks by offloading compute-intensive tasks from devices to proximate EC servers. Meanwhile, Network Slicing (NS) aims to provide service subscribers (SSs) with dedicated network resources based on virtualization techniques so that the service requirements can be guaranteed. The combination of EC and NS can efficiently utilize dynamic network resources at edge networks while improving the Quality of Service (QoS) of SSs. In this paper, we aim to jointly address the problem of dynamic slice scaling and task offloading from the perspective of profit of service providers (SPs) in the multi-tenant EC system. Specifically, we propose a Deep Q-Learning (DQL) based network slicing framework to dynamically reconfigure the scale of radio and computing resources of a slice reserved for a target SP. Then, by exploiting alternative optimization, we proposed a low-complexity algorithm to optimize the real-time offloading ratio and resource allocation policy of slice requests from SSs. To further verify our proposed framework, we have implemented the network slicing testbed with Docker container and conducted a series of experiments based on a real-world traffic dataset and a sample Augmented Reality (AR) application.

Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN

Edge computing (EC) has greatly facilitated the deployment of networked services with fast responses and low bandwidth, by deploying computing and storage at the network edge which is closer to the data sources. However, it is challenging to have the EC servers gain security protections like those in centralized data centers, making them more vulnerable to security attacks, especially distributed denial-of-service (DDoS) attacks. Existing detection approaches relying on the feedback from EC servers under the attacks can incur high bandwidth costs and service performance degradation. In this paper, we propose a new framework CoWatch for collaborative prediction and detection of DDoS Attacks in EC scenarios. Based on the distributed software-defined networking (SDN) architecture, CoWatch can collaboratively predict the DDoS attacks towards the EC servers and detect the attack flows in time. To efficiently filter the suspicious flows in distributed SDN, we devise an optimal threshold model by balancing the trade-off between collaboration efficiency and prediction effectiveness. We also explore and build on the LSTM model to design an algorithm for collaborative prediction and detection of DDoS Attacks. Experiment results on a number of datasets demonstrate the promising performance of CoWatch in effectiveness and efficiency.

Joint Service Quality Control and Resource Allocation for Service Reliability Maximization in Edge Computing

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Energy-Efficient Task Offloading of Edge-Aided Maritime UAV Systems

This paper considers the autonomous detecting and tracking task of unmanned aerial vehicles (UAVs) in the maritime environment. Due to the high computational complexity of the image-processing task, the strict latency requirement of the tracking task, and the shortage of batteries and computational capability of UAVs, the UAVs can offload the computing-intensive task to the edge computing server (ECS) to reduce the task latency and energy consumption. Even though, the large latency and energy consumption brought by some high-definition image-processing task still make some visual tracking difficult to perform. We first investigate the impact of video frame resolution on computing task size and detection accuracy, and further on task latency and energy consumption. In addition, we optimize the transmission power, the local CPU frequency, the offloading rate of UAVs, and the bandwidth allocation between UAVs. We model the UAVs tracking system as an energy consumption optimization problem with the constraint of task latency. Although the proposed optimization problem is complex, we transform the optimization problem into several subproblems with decoupling and rigorous mathematical proof. By combining the convex optimization with the genetic algorithm, an offloading algorithm with low complexity and good performance is obtained, which is verified by comparing with some benchmark offloading strategies in the numerical results.

A Cooperative Defense Framework Against Application-Level DDoS Attacks on Mobile Edge Computing Services

Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of services. Thus, we explore the elasticity of security defense among MEC nodes by proposing a COoperative DEfense ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODE</i> ) framework for MEC, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODE4MEC</i> . CODE4MEC aims to adapt to traffic changes by coordinating container-carried defensive resources among cooperative MEC nodes in an automatic way. Towards this aim, we propose four control plane functions to enable a life-cycle management for CODE4MEC, namely, CODE triggering, scheduling, coordination and releasing. However, an effective CODE4MEC requires non-trivial algorithmic schemes, in particular for CODE scheduling and coordination functions. We thus design an online combinatorial auction mechanism for real-time CODE scheduling, and prove a tighter performance bound relative to prior arts. As for CODE coordination, a flow-based traffic and context information coordination scheme is proposed to enable classical defense schemes to work properly and efficiently. Finally, using a combination of real testbed and simulation evaluations, we validate the effectiveness of CODE4MEC.

DSOS: A Distributed Secure Outsourcing System for Edge Computing Service in IoT

Edge computing can help the resource-constrained Internet of Things (IoT) devices to perform some complex tasks. Edge computing has many advantages, such as the distributed architecture, low interaction latency, and good resilience. It can provide fast response and reliable service for the IoT applications. There exists an important application in the edge computing environment is to outsource the computationally intensive problems to nearby edge servers, which has become a research hotspot in the area of industry and academia. In this article, we propose a distributed and secure system DSOS for seeking the least squares solution to the overdetermined system of linear equations (OSLE) with the assistance of multiple nearby noncolluding edge servers. Solving this type of linear equations is one of the most common problems for statistics and data mining in IoT. In our system, the coefficient matrix is divided into multiple blocks according to rows. The different blocks are distributed to the different edge servers. All edge servers can help the user to obtain the correct solution to the OSLE by the interactive computation. Our system can ensure the private information about input parameters and final results is not leaked to the participating edge servers. In addition, the mutual verification between the edge servers can ensure the validity of the final results. The experimental evaluations show that the designed system outperforms the existing ones in terms of fast response between the edge server and the user, low computation overload on the edge server side, and high efficiency on the user side.