A Server Migration Method Using Q-Learning with Dimension Reduction in Edge Computing

ABSTRACTWith the rapid development of intelligent scenarios such as intelligent transportation and urban perception, delay‐sensitive and computationally intensive applications continue to grow, especially in tasks such as anomaly detection based on machine learning, which puts higher requirements on the real‐time processing capabilities and resource scheduling efficiency of edge computing systems. Mobile edge computing (MEC), as a key supporting architecture, plays a core role in ensuring quality of service (QoS). On one hand, the occupation and release of server resources during task processing lead to dynamic changes in system resources within edge computing networks. Since system resources are often difficult to effectively replenish, services that are well adapted to the current time point may fail to accommodate new service requests at subsequent time points. On the other hand, due to the heterogeneous nature of tasks, resource consumption varies significantly across different task processing, causing some servers to easily become overloaded and unable to meet the processing demands of new tasks continuously. To tackle the challenges presented by the intensified dynamic changes in edge server resources due to task heterogeneity and the difficulty of processing new task requests under high‐load conditions in edge computing scenarios, we first devise a collaborative scheduling and offloading strategy for heterogeneous tasks across multiple edge servers. A task sorting mechanism and priority algorithm based on time groups and score values are designed. Then, with the optimization objective of minimizing task processing latency, a regional resource optimization algorithm based on Deep‐Q‐Network (DQN) is proposed to enable the effective processing of tasks. Finally, extensive experimental results show that this strategy can effectively achieve edge node load balancing, significantly reduce system processing delay, improve overall resource utilization, and has good heterogeneous task adaptability, which is suitable for multiple intelligent scene requirements including anomaly detection.

Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation

Joint edge caching and dynamic service migration in SDN based mobile edge computing

Online Learning to parallel offloading in heterogeneous wireless networks

Convolutional neural network (CNN)-based object detection is a key technology to enable autonomous mobile vision applications on mobile end devices such as smart phones and drones. With the advance of edge computing technology, a prevalent solution is to offload the computation-intensive CNN inference tasks to edge networks for fast and accurate object detection. However, a single edge server may not be powerful enough to ensure fast and accurate object detection due to its resource constraints. In this paper, we propose a multi-edge assisted fast object detection framework, MASS, to further reduce the object detection latency while maintaining the detection accuracy. In MASS, the CNN model is divided into two parts, namely the Head part and the Tail part. The Head part is executed locally and the Tail part is further split into multiple subtasks, which can be offloaded to multiple edge servers for parallel execution. First, we propose a method to select the optimal parallel entry point to separate the Head part from the Tail part. Then, an adaptive subtask generation and offloading strategy is proposed to divide the Tail part into multiple subtasks and offload these subtasks to multiple heterogeneous edge servers. Besides, we also propose a uniformly sampled zero-padding scheme to reduce the communication cost among edge servers when executing these subtasks in parallel. We implement MASS in a testbed with four edge servers and evaluate its performance, where the experimental results show that MASS can reduce object detection latency by up to $64.83%$, while the detection accuracy degradation is less than $3%$.

In recent years, research has been conducted on dynamic map systems, which are information and communication platforms that manage vehicle sensor information and run applications. However, there is a concern about scalability in the dynamic map system, which operates on a cloud on the Internet, when the number of vehicles that transmit and receive sensor information increases. Therefore, it is considered that the application load can be distributed by distributing multiple edge servers geographically and having the information managed in the cloud by the edge servers. However, the edge server that controls the vehicle information and the edge server that receives the data do not always match depending on the actual radio wave conditions. Therefore, some applications are difficult to manage, such as intersection collision danger warning and merging arbitration when edge servers are assigned in the same way as base stations that receive data from vehicles. Such an application should receive all vehicle data for the target road area. Therefore, we divide the area on the road where a vehicle travels as “lane section ID” and assign an edge server to each lane section ID on the basis of that area. In addition, we implemented a dynamic map system that connects vehicles and edge servers by linking multiple edge servers. This enables the edge server to aggregate vehicle data without being affected by radio traffic conditions. We evaluated the scalability of the dynamic map system and verified the effectiveness of the load balancing mechanism using multiple edge servers.

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

Natural gas hydrate is an important energy source. Therefore, it is extremely important to provide a clear imaging profile to determine its distribution for energy exploration. In view of the problems existing in conventional migration methods, e.g., the limited imaging angles, we proposed to utilize an amplitude-preserved one-way wave equation migration based on matrix decomposition to deal with primary and multiple waves. With respect to seismic data gathered at the Chilean continental margin, a conventional processing flow to obtain seismic records with a high signal-to-noise ratio is introduced. Then, the imaging results of the conventional and amplitude-preserved one-way wave equation migration methods based on primary waves are compared, to demonstrate the necessity of implementing amplitude-preserving migration. Moreover, a simple two-layer model is imaged by using primary and multiple waves, which proves the superiority of multiple waves in imaging compared with primary waves and lays the foundation for further application. For the real data, the imaging sections of primary and multiple waves are compared. We found that multiple waves are able to provide a wider imaging illumination while primary waves fail to illuminate, especially for the imaging of bottom simulating reflections (BSRs), because multiple waves have a longer travelling path and carry more information. By imaging the actual seismic data, we can make a conclusion that the imaging result generated by multiple waves can be viewed as a supplementary for the imaging result of primary waves, and it has some guiding values for further hydrate and in general shallow gas exploration.

Edge Computing (EC) has been proposed as a promising approach to fulfil the requirements of high bandwidth and ultra-latency of mobile applications. However, existing researches on resource allocation only consider the computing resource of mobile devices and EC servers, while ignored the constraint of the networking resources once spreading applications among multiple EC servers via wireless and wired networks. Fibre-Wireless access networks (FiWi) combine the huge bandwidth of optical fibre networks and flexible access of wireless networks to address the above issue and bridge the coexistence of multiple EC servers. They propose a Virtualisation-based Architecture converging EC over FiWi (VAECFW) to centralise control and allocate networking and computing resources for serving requested services. In addition, they study the problem of resource allocation of EC over FiWi and propose two algorithms Revenue-based Virtual Network Embedding (R-VNE) and Balanced Central Processing Unit Resource Allocation with Virtual Network Embedding (BCRA-VNE). Simulation experiments show that the services acceptance ratio is increased about 35% under their proposed architecture, and the average service requests bandwidth utilisation of R-VNE is increasing from 50 to 66%, and the BCRA-VNE is from 37 to 48%. The two algorithms not only achieve higher revenue but also get better profit rate.

Mobile edge computing (MEC) is powerful in alleviating the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design within a three-tier integrated sensing, communication, and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC servers or the cloud server. To minimize the execution latency, we jointly optimize the transmit beamforming vectors and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrate the superiority of the proposed mechanism in terms of convergence and task execution time reduction, compared with the state-of-the-art two-tier ISCC framework.

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self‐disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single‐edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

Wireless-powered mobile edge computing (WP-MEC) is a new network computing paradigm, which integrates with the advantages of wireless power transfer and mobile edge computing. In WP-MEC, the time of wireless power transfer is a key factor affecting the performance of network system when the harvest-then-offload protocol is employed. If the time of wireless power transfer is too short, the user cannot harvest enough energy; If it is too long, the task uploading time left for the user is insufficient. Both result in numerous user tasks being discarded. To tackle this problem, an optimization algorithm for the time of wireless power transfer based on differential evolution is proposed. The hybrid mutation operator and perturbation based binomial crossover operator are designed for this algorithm. These two improvements effectively enhance the optimization performance of DE, conducive to find the optimal time of wireless power transfer. Moreover, the micro-population is introduced into this algorithm in order to improve the optimization efficiency. Finally, the performance of this algorithm is verified through the existing computation completion ratio maximization model under the WP-MEC scenario with multiple edge servers. Numerical results show that the computation offloading scheme incorporating this algorithm can achieve a higher computation completion rate of user tasks than the benchmark schemes. This proves that the proposed algorithm can find a better wireless power transfer time than the benchmark schemes and is an effective wireless power transfer time optimization scheme.

Federated edge cloud (FEC) is an edge cloud environment where multiple edge servers in a single administrative domain collaborate together to provide real-time services. This environment reduces the possibility of violating the quality of service (QoS) requirements of target services by locating delay-sensitive services at nearby edge servers instead of deploying them on the cloud. However, as the number of edge servers increases, the amount of energy consumed by servers and network switches also increases. This creates another challenge for how to schedule delay-sensitive services over FEC, while minimizing the total energy consumption and reducing the QoS violation of a service at the same time. This paper proposes an energy-efficient service scheduling algorithm in FEC. The proposed algorithm is based on an observation that as the number of edge servers along the service path is reduced, the total energy consumption can be minimized. Traditional approaches place services using their maximum traffic requirements to ensure QoS without considering the actual traffic change. In contrast, the proposed algorithm schedules them with actual traffic requirements to increase the number of services co-located in a single server. This maximizes the consolidation of services in a single server and thus minimizes the energy consumption. Moreover, when edge servers are overloaded, the proposed algorithm reconfigures the service path such that service migration overhead and energy consumption are minimized while guaranteeing the QoS requirements of services. The simulation results show that the proposed algorithm improves energy efficiency by up to 21% and lowers the service violation rate by up to 80% against existing approaches.