Propelled by recent advances in Mobile Edge Computing (MEC) and the Internet of Things (IoT), the digital twin technique has been envisioned as a de-facto driving force to bridge the virtual and physical worlds through creating digital portrayals of physical objects. In virtue of the flourishing of edge intelligence and abundant IoT data, data-driven modelling facilitates the implementation and maintenance of digital twins, where simulations of physical objects are usually performed based on Deep Neural Networks (DNNs). A significant advantage of adopting digital twins is to enable decisive prediction on the behaviours of objects in near future without waiting for that really happen. To provide accurate predictions, it is vital to keep each digital twin synchronized with its physical object in real-time. However, it is challenging to maintain the real-time synchronization between a digital twin and its physical object due to the dynamics of physical objects and sensing data drift over time, i.e., the live data from a physical object diverge from the model training data of its digital twin. To address this critical issue, continual learning is a promising solution to retrain models of digital twins incrementally. In this paper, we investigate digital twin synchronization issues via continual learning in an MEC environment, with the aim to maximize the total utility gain, i.e., the enhanced model accuracy. We study two novel optimization problems: the static digital twin synchronization problem per time slot and the dynamic digital twin synchronization problem for a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the static digital twin synchronization problem when the problem size is small; otherwise, we develop a randomized approximation algorithm at the expense of bounded resource violations for it. We also devise a deterministic approximation algorithm with guaranteed performance for a special case of the static digital twin synchronization problem. We thirdly consider the dynamic digital twin synchronization problem by proposing an efficient online algorithm for it. Finally, we evaluate the performance of the proposed algorithms for continuous digital twin synchronization through simulations. Simulation results show that the proposed algorithms are promising, outperforming counterpart benchmarks by no less than 13.2%, in terms of the total utility gain.

Mobile-edge computing (MEC) has received wide attention recently due to its efficacy in alleviating the computation stress of mobile devices (MDs), which is realized by offloading workloads from MD users to nearby edge servers (ESs). Prior work has studied related task offloading and scheduling problems and proposed many approaches. However, none of these approaches considers the reliability issue in MEC systems which may suffer soft errors during task execution as well as bit errors during task offloading simultaneously. Targeting optimization on a multi-user MEC system, in this article we investigate the task offloading and scheduling problem of maximizing system quality of experience (QoE) under a certain reliability requirement. With the consideration of the combinatorial nature of this problem, we propose to decompose the original problem into i) a task-to-ES assignment problem with fixed task offloading decision, for satisfying system reliability constraint, ii) a computing resource allocation problem with fixed task offloading and assignment decisions, for maximizing system QoE, and iii) a task offloading optimization problem to find the best offloading decision that achieves the maximum QoE under the reliability constraint using our task assignment and resource allocation methods. In order to solve these sub-problems, we further design a reliability-aware task-to-ES assignment algorithm, a QoE-optimum resource allocation algorithm, and a binary particle swarm optimization based task offloading algorithm. We perform extensive simulations and testbed experiments to validate the efficacy of the proposed scheme. Simulation and testbed results show that the proposed scheme greatly outperforms four benchmark approaches and it achieves up to 63.2% and 43.1% increase in the average QoE (quantified by offloading utility), respectively.

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

As a key technology of 5G, network function virtualization enables each monolithic service to be divided into microservices, facilitating their deployment and management in edge environments. One of the most critical issues in 5G is how to support dynamically arriving mission-critical services with low-latency and high-reliability requirements in distributed edge environments. However, most existing works focus on how to provide reliable services without considering latency, and their heuristics struggle to cope with high-dimensional constraints and complex environments with heterogeneous infrastructure and services. In this paper, we propose a SafeDRL algorithm to resource-efficiently support these dynamically arriving services while meeting their reliability and latency requirements. Specifically, we first formulate the problem as an integer nonlinear programming and prove its NP-hardness. To tackle this problem, our SafeDRL algorithm captures delayed rewards in dynamic environments by reinforcement learning, and corrects constraint violations with high-quality feasible solutions based on expert intervention, and prunes unnecessary backup instances for optimality. The algorithm is proved to have a bounded approximation ratio in general cases. Extensive trace-driven simulations show that, compared with the state-of-the-art solution, SafeDRL can save resource costs by up to 49.32% and improve the service acceptance ratio by up to 55% with acceptable execution time.

Traditional traffic classification methods struggle to identify emerging network traffic due to the need for model retraining, which hampers the real-time response of deployed edge devices. Furthermore, emerging network traffic samples are often scarce, traditional methods often treat a session as a single image, thereby overlooking essential structural features. These factors can result in poor generalization ability of the trained model. To overcome these challenges, we propose ConViTML (Convolutional Vision Transformer-based Meta-Learning), a real-time end-to-end network traffic classification framework that employs meta-learning to avoid model retraining. We propose a novel feature extraction network, Convolutional Visual Transformer (ConViT), merging Convolutional Neural Network (CNN) and Visual Transformer (ViT). ConViT can directly extract low-dimensional discriminative features containing basic and structural features of the session, which is vital for improving detection accuracy and accelerating convergence in a data-scarce environment. Furthermore, we employ a Packet-based Relation Network (PRN) to analyze the matching degree of support samples and query samples. Therefore, accurate classification in novel traffic identification tasks can be achieved with just a few labeled samples, eliminating extensive data collection and labeling operations. Finally, we replace various feature extractors and compare our approach with the classic meta-learning framework Relation Network (RelationNet). Extensive experimental results demonstrate that ConViTML outperforms others with various performance indicators.

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution.