关键词： Mobile edge computing  

摘要： The large size of DNNs poses a significant challenge for deployment on devices with limited resources, such as mobile, edge, and IoT platforms. To address this issue, a distributed inference framework can be utilized. In this framework, a small-scale DNN (initial layers) is deployed on mobile devices, a larger version on edge devices, and the full DNN on the cloud. Samples with low complexity (easy) can be processed on mobile, those with moderate complexity (medium) on edge devices, and high complexity (hard) samples on the cloud. Given that the complexity of each sample is unknown in advance, the crucial question in distributed inference is determining the sample complexity for appropriate DNN processing. We introduce a novel method named DIMEC-DC, which leverages the Data Cartography approach initially proposed for enhancing DNN generalization. By employing data cartography, we assess sample complexity. DIMEC-DC aims to boost accuracy while considering the offloading costs from mobile to edge/cloud. Our experimental results on GLUE datasets, covering a variety of NLP tasks, indicate that our approach significantly lowers inference costs by more than 43% while maintaining a minimal accuracy drop of less than 0.5% compared to performing all inferences on the cloud. © 2024, CC BY.

关键词： Deep Q-network   Transmission learning   Mobile edge computing   Task uninstallation   Reinforcement learning  

摘要： Traditional task uninstallation methods are difficult to cope with the continuous changes in network environment and device status, therefore a more intelligent solution is urgently needed. This article studied the optimization method of task uninstallation in mobile edge computing (MEC) environment, and studied the optimization method of task uninstallation in combination with improved deep Q-learning and transmission learning (TL). Firstly, it provided an overview of the basic concepts and optimization problems of task uninstallation in MEC environments, and then delved into the application of improved deep Q-learning in task uninstallation optimization, namely the application of deep Q-network (DQN) in task uninstallation optimization. Next, this article designed the TL-DQN method by combining DQN and TL. This method utilizes the DQN algorithm to construct a model that can handle complex states and action spaces. Meanwhile, by introducing TL, the model can quickly transfer knowledge and achieve adaptive optimization of tasks in new environments, thereby improving the generalization ability and efficiency of the decision-making process. Experiments have shown that TL-DQN performs better in convergence speed compared to other algorithms. For delay sensitive users, the delay growth of TL-DQN algorithm is relatively slow. When the task volume reached 10, the delay of TL-DQN was 7.97 s, which was 1.99 s lower than reinforcement learning. When the number of unloading tasks increased to 500, the energy consumption of TL-DQN was 554 J, which was 282 J lower than the energy consumption required by the task uninstallation method constructed by reinforcement learning algorithms. The task uninstallation method studied in this article has achieved significant results in reducing latency, improving energy efficiency, and adapting to network dynamic changes, bringing users a more stable and reliable service experience.

关键词： Mobile edge computing  

摘要： Edge computing is considered a key paradigm for supporting real-time applications over 5G networks, as hosting applications at the network edge can substantially reduce delays. A significant fraction of real-time applications over 5G are expected to be highly mobile applications. However, one challenge with hosting mobile applications on the network edge is ensuring that users continue to get low latency as they move across different locations. This requires the support to handover clients to different edge sites with negligible application delays. However, many edge applications are stateful and can experience significant downtime during state migration over 5G. This paper addresses the problem of enabling stateful mobile edge applications in 5G networks. We first identify the key architectural issues and then propose a new system design, EdgeWarp, that mitigates delays during mobility through proactive application state migration. To enable this, we extend the existing edge data stores with the design of a novel two-step application state synchronization protocol, that leverages the early prediction of the target edge host. Additionally, EdgeWarp prioritizes the handover of latency-sensitive edge applications by communicating their latency requirements to the 5G control plane at the beginning of a data session. Our evaluation with real edge applications shows up to a 15.4× reduction in application downtime under mobility. We have made our anonymized code publicly accessible here. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing   Network function parallelization   Service function chain   Demand uncertainty   Deep reinforcement learning  

摘要： The orchestration of Service Function Chains (SFCs) in Mobile Edge Computing (MEC) becomes crucial for ensuring efficient service provision, especially under dynamic and uncertain demand. Meanwhile, the parallelization of Virtual Network Functions (VNFs) within an SFC can further optimize resource usage and reduce the risk of deadline violations. However, most existing works formulate the SFC orchestration problem in MEC with deterministic demands and costly runtime resource reprovisioning to handle dynamic demands. This paper introduces a Robust Deadline-aware network function Parallelization framework under Demand Uncertainty (RDPDU) designed to address the challenges posed by unpredictable fluctuations in user demand and resource availability within MEC networks. RDPDU to consider end-to-end latency for SFC assembly by modeling load- dependent processing latency and load-independent propagation latency. Also, RDPDU formulates the problem assuming uncertain demand by Quadratic Integer Programming (QIP) to be resistant to dynamic service demand fluctuations. By discovering dependencies between VNFs, the RDPDU effectively assembles multiple sub-SFCs instead of the original SFC. Finally, our framework uses Deep Reinforcement Learning (DRL) to assemble sub-SFCs with guaranteed latency and deadline. By integrating DRL into the SFC orchestration problem, the framework adapts to changing network conditions and demand patterns, improving the overall system's flexibility and robustness. Experimental evaluations show that the proposed framework can effectively deal with demand fluctuations, latency, deadline, and scalability and improve performance against recent algorithms.

关键词： Mobile edge computing  

摘要： The rapid expansion of IoT devices and their real-time applications have driven a growing need for edge computing. To meet this need, efficient and secure solutions are required for running such applications on resource-constrained devices with limited power, CPU, and memory. Unikernel, with its minimalistic design and application-specific approach, offers a promising alternative to traditional virtualization and container technologies in these environments. The existing research does not thoroughly examine the feasibility of using unikernel for edge computing. This paper investigates the potential of unikernel for ARM-powered edge computing by evaluating the performance and efficiency of three prominent unikernel systems—OSv, Nanos, and Unikraft—against Docker container. We experiment with real-world edge computing applications and utilize key metrics such as boot time, execution time, memory usage, CPU overhead, and network performance to determine how unikernel performs under the constraints of edge devices. Our findings reveal the potential advantages of unikernel in terms of reduced resource consumption and faster startup times while highlighting areas where they may need further optimization for edge deployment. This study provides valuable insights for researchers and practitioners considering unikernel as a lightweight, efficient solution for edge computing on ARM architectures. © 2024, CC BY.

关键词： Edge server placement   Mobile edge computing   Graph clustering   Graph convolutional network  

摘要： With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing.

关键词： Ultra dense network   Mobile edge computing   Dynamic task allocation   Internet of things  

摘要： 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 (IWPSO) 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.

关键词： Mobile edge computing   Computing migration   Physical layer security(PLS)   Interval many-objective optimization   Evolutionary algorithm  

摘要： In this paper, we study secure computing migration scenarios in uncertain environments with the presence of multiple malicious eavesdroppers (MEs). Specifically, when edge servers (ESs) execute tasks delivered by smart devices (SDs), SDs may move beyond the coverage of ESs, and computing migration (CM) of unfinished tasks is required to ensure service continuity. There is a risk of privacy leakage during task migration, and MEs use colluding eavesdropping to eavesdrop on the migrated tasks, and we consider eavesdropping on the associated tasks through data sharing among MEs to improve the eavesdropping efficiency. For eavesdropping in MEs, we achieve eavesdropping strikes using cooperative interference by jammers, which benefit by providing jamming services. In addition, uncertain computational scenarios directly affect the efficiency of task execution, and we consider the uncertainty factor in the malicious eavesdropping environment. To this end, this paper proposes the secure computational migration of associative privacy in uncertain environments (SCMAPUE) model, which transforms uncertainties into interval parameters, and optimizes the five objectives of migration delay, maximum completion time, energy consumption, load balancing and migration reliability to achieve efficient task execution and reliable migration. Aiming at the model characteristics, this paper designs an interval many-objective evolutionary algorithm for reliable migration (IMaOEA-RM), which employs a condition-based interval confidence strategy and a multi-access secure migration selection strategy to improve the convergence of the algorithm, and utilizes a dual-migration crossover strategy in order to adjust the jammer partners and improve the population diversity. Simulation results show that our proposed IMaOEA-RM algorithm can provide a more reliable and efficient migration scheme than existing algorithms.

关键词： Mobile edge computing   Non-orthogonal multiple access   Cognitive radio network   Deep reinforcement learning   Soft actor-critic  

摘要： This paper investigates resource allocation in cognitive radio (CR) based mobile edge computing (MEC) systems under time-varying channels while considering non-orthogonal multiple access (NOMA) technique. In contrast to prior research on NOMA-MEC, we introduce a generalized user grouping scheme, which makes the secondary users (SUs) have the flexibility to participate in multiple NOMA groups simultaneously, enabling the partial offloading of their task data to multiple MEC servers. To tackle the non-convex long-term energy minimization problem, we propose a decomposition based soft actor-critic (DB-SAC) approach combining conventional convex optimization with deep reinforcement learning (DRL) algorithms, which divides the problem into a joint communication and computation resource allocation subproblem and an offloading ratios optimization subproblem. The former one is further divided into a series of local computation resource and offloading energy minimization problems, addressed through theoretical analysis and convex optimization techniques. For the latter subproblem, we adopt the SAC algorithm to dynamically allocate offloading ratios for SUs. Simulation results showcase that when compared to the conventional non-generalized user grouping scheme, the generalized user grouping scheme can achieve remarkable energy savings of up to 87.5%.

关键词： Near field communication   Mobile edge computing   Multi-agent reinforcement learning   Resource optimization  

摘要： In the next-generation communication system, near-field communication (NFC) is a key enabler of contactless transactions, including mobile payments, ticketing, and access control. With the growing demand for contact- less solutions, NFC technology will play a pivotal role in enabling secure and convenient payment experiences across various sectors. In contrast, Internet of Things (IoT) devices such as phones' Point of Sale (PoS) constitute limited battery life and finite computational resources that act as a bottleneck to doing the authentication in a minimal amount of time. Because of this, it garnered considerable attention in both academic and industrial realms. To overcome this, in this work we consider the Multiple Mobile Edge Computing (MEC) as an effective solution that provides extensive computation to PoS connected to it. To address the above, this work considers the PoS-enabled multi-MEC network to guarantee NFC communication reliably and effectively. For this, we formulate the joint optimization problem to maximize the probability of successful authentication while minimizing the queueing delay by jointly optimizing the computation and communication resources by utilizing a multi-agent reinforcement learning optimization approach. Through extensive simulations based on real-world scenarios, the effectiveness of the proposed approach was demonstrated. The results demonstrate that adjusting the complexity and learning rates of the model, coupled with strategic allocation of edge resources, significantly increased authentication success rates. Furthermore, the optimal allocation strategy was found to be crucial in reducing latency and improving authentication success by approximately 9.75%, surpassing other approaches. This study highlights the importance of resource management in optimizing MEC systems, paving the way for advancements in establishing secure, efficient, and dependable systems within the Internet of Things framework.