Web 3.0-Enabled Microservice Re-Scheduling for Heterogenous Resources Co-Optimization in Metaverse-Integrated Edge Networks

As Connected Autonomous Vehicles (CAVs) emerge as a cornerstone of future transportation systems, Autonomous Mobility on Demand (AMoD) platforms stand to revolutionize urban mobility by mitigating traffic congestion and enhancing passenger comfort and convenience. However, existing research primarily addresses either vehicle dispatch using real-world datasets to estimate the time cost for each CAV to its destination, or emphasizes cooperative motion planning of the CAVs for obstacle avoidance. Moreover, these studies often overlook the integration of the following two aspects: CAV destination assignments and cooperative motion planning in AMoD systems. To bridge this gap, our proposed CoDriveVis offers an integrated platform that integrates microscopic motion planning and control with macroscopic vehicle scheduling and dispatching. CoDriveVis leverages CARLA's perception capabilities to produce multimodal outputs such as LiDARgenerated point clouds, images, and videos from diverse perspectives, which can inform traffic system operations, especially when coupled with large multimodal models (LMMs). Additionally, it features the Bird's Eye View (BEV) image generation to visualize vehicle dispatching and request distribution across the map. The scheduling integration of the platform is streamlined through organized dictionaries for CAVs and passenger requests, while its control modes support both position transition and throttle/steering inputs. CoDriveVis also facilitates destination changes for updating the coordination of CAVs. Experimental deployment of representative methods within CoDriveVis demonstrates its efficacy and practicality as a robust simulation platform for advancing AMoD systems. The code is available at https://github.com/henryhcliu/CoDriveVis.git.

Previously proposed streaming protocols using broadcast or multicast are able to deliver multimedia files on-demand with required server bandwidth that grows much slower than linearly with request rate, or with the inverse of client start-up delay. The same efficiencies can be achieved for network bandwidth if delivery is over a true broadcast channel. This paper considers the required network bandwidth for on-demand streaming over multicast delivery trees. We consider both simple canonical delivery trees, and more complex cases in which delivery trees are constructed using both existing and new algorithms for randomly generated network topologies and client site locations. Results in this paper quantify the potential savings from use of multicast trees that are configured to minimize network bandwidth rather than the latency to the content server. Further, we determine the network bandwidth usage of particular immediate service and periodic broadcast on-demand streaming protocols. The periodic broadcast protocol is able to simultaneously achieve close to the minimum possible network and server bandwidth usage.

Nowadays, short video feed has attracted billions of mobile users all around the world to interact with content effortlessly, yielding an explosive growth of short video commerce. Typically, users watch full-screen short videos of a few seconds one-by-one in a watch-list generated by recommender systems, skipping those they are not interested in. However, the recommender system at the cloud makes a user-interest-specific decision mostly based on the users' behavior data collected within the application itself (e.g., users' view history), without examining the lower-layer network and communication statistics. When the playback choked due to the limited network bandwidth, the user will probably skip the video, leading to a waste of bandwidth and degradation of the user's quality of experience (QoE). Meanwhile, the excessive number of user requests to video contents raises a heavy computational load and communication cost for the recommender system at the cloud to determine which videos to be recommended and delivered to each user in a real-time manner. The advance of edge computing provides a promising avenue of deploying edge nodes with caches (e.g., household devices) beyond cloud and edge servers, such that the recommender system in the cloud can place popular video contents closer to client users, and meanwhile the contents are delivered to client users with good network condition. In this paper, we propose CFP, a cross-layer recommender system for short video streaming with fine-grained preloading technique at the network edge. CFP jointly optimizes the recommendation effect of the video application and the content preloading efficiency under various network conditions at the network edge. CFP takes a two-stage approach: the cloud server first seeks to perform edge-wise instead of user-interest-specific recommendation with neural collaborative filtering recommender, preloading a list of candidate videos to edge nodes, and each edge node, deploying the GRU with attention, then delivers the proper video contents to the client user device according to the user's preference. Trace-driven emulations demonstrate the efficiency of the proposed CFP scheme.

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

Industrial internet of things (IIoT) enables edge computing technology to provide communication between the machines that produce a large amount of data and locate at the edge network. A task scheduling is implemented in the edge node. Furthermore, the real-time data can achieve with the lowest latency that allowed by the edge node near the edge network. However, a mobile machine such as an autonomous guided vehicle can interfere in this situation. Because the vehicle also needs service by the edge node. Over that, quality of service (QoS) performance can decrease. Therefore, this paper deploys an unmanned aerial vehicle (UAV) as an edge node to provide service to the edge network through optimizing the trajectory of UAV, where the edge network request task using a Deep Q-Network (DQN) Learning. The result shows that using machine learning, notably the DQN algorithm, can increase the number of the machine that can be provided service. Subsequently, the real-time data can achieve either the interrupt occurs at the edge node.

An interactive video-on-demand (VoD) system allows users to access video services, such as movies, electronic encyclopedia, interactive games, and educational videos from video servers on a broadband network. This paper develops a performance evaluation tool for the system design. In particular, a user activity model is developed to describe the usage of system resources, i.e., network bandwidth and video server usage, by a user as it interacts with the service. In addition, we allow batching of user requests, and the effect of such batching is captured in a batching model. Our proposed queueing model integrates both the user activity and the batching model. This model can be used to determine the requirements of network bandwidth and video server and, hence, the trade-off in communication and storage costs for different system resource configurations.

As an extension of cloud computing, fog computing at the edge of networks provides low latency, location awareness, and real-time interactions. At the same time, millimeter wave (mmWave) communications are able to provide directional multi-gigabit transmission rates with large available bandwidth. Based on the user mobile trajectories in a region, several activity hotspots that users pass by frequently can be obtained. By caching popular content at the edge nodes near the hotspots, users can download the cached content directly at a short distance, and the user experience can be significantly improved. Considering multiple hotspots in a region, how to efficiently schedule the transmission for the caching at edge nodes becomes a key problem. In this paper, we focus on the problem of mobility aware transmission scheduling for caching at edge nodes near hotspots, and utilize multi-hop relaying and concurrent transmissions to achieve better performance. After formulating the optimal scheduling problem as a stochastic nonlinear mixed integer program, we propose a mobility aware caching scheduling scheme, called MHRC (Multi-Hop Relaying based Caching), where multi-hop D2D paths are established for edge nodes, and concurrent transmissions are exploited in the scheduling of caching at edge nodes. Extensive performance evaluation demonstrates MHRC achieves more than 1x higher expected cached data amount compared with state-of-the-art schemes.

Today’s advancements in IoT devices and edge computing platforms have given rise to new scenarios enabling context-aware applications in extremely interconnected environments. To promote the standardization of these platforms the European Telecommunications Standards Institute (ETSI) proposed the Multi-access Edge Computing (MEC) standard, enabling the execution of cloud-like services at the network edge. In this work, we propose the design of a novel MEC-compliant architecture that leverages underutilized far-edge resources to enlarge MEC edge node computational capacity and enhance service availability in highly mobile networks. Our approach allows far-edge devices to participate in a negotiation process embodying a rewarding system while addressing resource volatility as these devices join and leave the edge node resource infrastructure. Furthermore, we developed an original simulation framework to replicate the proposed architecture by using vehicle resources as far-edge devices. Its primary purpose is to demonstrate the viability and flexibility of our proposal, as well as to investigate novel application scenarios using real-world datasets. Our preliminary results show the feasibility and effectiveness of our proposal when using vehicular-based virtual resources in realistically simulated 5G networks.

[EN] Salient object detection (SOD) from raw sensor images in the edge networks can effectively speed up the decision-making process in the complex environments, because it simulates the mechanism of human attention to identify salient objects from images. The success of supervised deep learning approaches have been widely proved SOD field. However, the imbalanced and limited training data at each edge device pose a huge challenge for us to deploy deep learning methods in the edge computing environments. In this article, we propose a cloud-edge distributed augmented semi-supervised learning architecture for SOD over the edge networks. The framework consists of two components: the base classification networks are employed in different edge nodes, and the reverse augmented network is employed in cloud. First, the base classification networks are trained with data from edge nodes while the reverse augmented network is trained with the whole data. Then, we concatenate each base classification network with reverse augmented network, thus the latter network can help the training of former network. Finally, we integrate the outputs of all base classification network to generate the pseudo-labels, which are used for semi-supervised learning of the augment network. We demonstrated a convincing performance of our semi-supervised learning framework on four bench-marked data-sets. These results show that our augmented semi-supervised learning framework can outperform other optimization strategies on deep learning for the edge computing.

SummaryWith the advent of the Internet of things era, cloud computing platforms will face the challenges of massive equipment requirements for access, massive data, insufficient bandwidth, and high power consumption. Edge computing, as a new technology, makes it possible to stream media over the edge network. However, many problems related to file sharing among edge nodes and to the files provided by cloud servers exist. We propose a novel replica placement strategy for mobile media streaming in edge computing (RPME) to address the aforementioned problem. First, we introduce a multilevel replica placement model in the RPME. In the RPME, we acquire the user information and the user‐item rating matrix when the user requests the media data. At the server, we cluster the users according the user information, update the user‐item rating matrix, and then generate a replica recommendation sequence. For when the server submits the replica recommendation sequence to the edge node considering the constraints of the edge node storage capacity and the limitations on the service capacity of the requested replica, we propose an effective replica placement mechanism in the RPME. To show the benefits of the RPME, we also present several experiments to prove its validity.

Current mobile networks are facing dramatic growth in wireless traffics due to the prosperity of streaming media services. Cooperative edge caching, enabling multiple edge nodes to cache and share contents by exploiting the spatial/temporal user request differentiation, is regarded as a promising method to enhance Quality of Experience (QoE). However, frequent content sharing between BSs consumes operation cost such as the usage of cross-edge bandwidth and energy consumption. Therefore, new challenges incurred by performance-cost trade-off arise. In this paper, we propose a user preference-aware content caching and migration (PACM) scheme for video content delivery in a cost-efficient edge network. In this scheme, the dynamic user request preference and the long-term content migration cost budget are considered for content placement and delivery. To navigate a good performance-cost trade-off, we formulate the content caching and migration to be a long-term optimization problem. Then, the Lyapunov optimization method is used to decompose the problem into a series of real-time optimizations. As the decomposed problem is NP-hard, we design a novel collective reinforcement learning (CRL) algorithm that can realize online efficient decision-making by interacting with training experience. Simulation results show that the CRL algorithm has a high convergence rate and the proposed scheme can achieve quasi-optimal performance in terms of user-perceived latency, cache hit rate, and video stalling rate.

Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized.

<span lang="EN-US">Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput.</span>

User mobility support is relevant for smart applications in edge network infrastructures. Machine Learning-based solutions have been taking advantage of hardware customisation to improve their performance. In this context, attaching Field Programmable Gate Array (FPGA) technology into edge nodes could offer tailored hardware for ML applications at the edge. This work proposes hardware customisation at the edge network to improve the performance of ML-based solutions. In that scenario, mobile users could configure the hardware and apply it to different edge nodes. Mobile users can use customised hardware to execute applications at the edge in a performance-enhanced environment by carrying hardware settings.

Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.