关键词： Dynamic decision-making   Dynamic optimization algorithm   Energy efficient offloading   Genetic algorithm   Hill climbing   Mobile cloud computing   Mobile edge computing   Mobile energy consumption   PSO   Scalability analysis   Statistical analysis   Task offloading   Workload analysis  

摘要： The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to resource limitations and security concerns inherent to these devices. In response, this paper introduces a Dynamic Energy-Efficient Offloading Algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption, and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and comparative analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： deep learning   mobile edge computing   user admission control   resource scheduling  

摘要： It is widely recognized that the future wireless networks are able to efficiently slice heterogeneous resources to provide customized services for various use cases. However, it is challenging to meet the diverse requirements of ever-growing applications, especially the stringent requirements of numerous delay-sensitive and/or computation-intensive applications. To tackle this challenge, we should not only consider user admission control to cope with resource limitations, but also make resource management more intelligent and flexible to meet diverse service needs. Taking advantages of mobile edge computing(MEC)and network slicing, in this paper, we propose deep edge slicing(DES),to jointly optimize user admission control and resource scheduling with the aim of minimizing the system cost while guaranteeing multitudinous quality-of-service (QoS) requirements. Specifically, we first apply a deep reinforcement learning approach to select the optimal set of access users with different service requests for maximizing resource *** a deep learning algorithm is employed to predict traffic data for allocating the communication and computing resources to different slices in advance. Finally, we realize the dynamic scheduling of heterogeneous resources by solving the optimization problem of minimizing the system cost. Simulation results demonstrate that DES can greatly reduce the system cost compared to other benchmarks.

关键词： Service offloading   Edge computing   Fog computing   Mobile edge computing   Cloud computing   Artificial intelligence  

摘要： The present and next-generation networks of service offloading present exciting issues. The traditional network optimisation techniques still require rigorous heuristic tuning to obtain a good result. Traditional methods employ data as an input and produce solutions that are close to ideal. These methods only operate with tiny networks and exhibit exponential computation times. Thus, we are driven to understand the behaviour of conventional optimisation methods while boosting service quality and meeting next-generation applications. The Optimization problem in Virtual Mobile Edge Computing using Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading (TD3PG-ICO). Optimal Stopping Theory is used in virtual mobile edge computing for the best service offloading (OST). To support the use case, a service offloading protocol is also described. We use Software Defined Networking (SDN) and Network Function Virtualization (NFV) ideas to manage and virtualise network components. To deal with dense Internet of Things (IoT) networks, a TD3PG-OST-based offloading is therefore suggested. There are extensive analyses and comparisons with cutting-edge methods. Results demonstrate the effectiveness of the proposed methods in terms of networking, resource utilisation, and service offloading.

关键词： 5G core network   downtime   mobile edge computing   service migration  

摘要： Seamless cross-edge migration for real-time rendering applications is challenging. The strong interactive nature of real-time rendering applications demands a downtime lower than 15 ms to achieve an imperceptible migration. Existing methods based on virtual machine migration and container migration suffer from unpleasant downtime brought by dirty page retransmission-induced repeated memory data copy and the shared storage failure-induced extensive disk data copy. In this article, we propose Cloud-assisted Service Migration (CSM) which leverages cloud-edge collaboration to achieve seamless service migration for real-time rendering applications. CSM improves service migration user experience in three folds: First, it introduces a dual rendering mechanism to bypass the peer-to-peer data copy and compresses the freezing stage. Second, a user equipment-centric session switch mechanism is proposed to save time by well coordinating application session switches and 5G user plane session switches. Third, a smooth switching mechanism is leveraged to prevent unpleasant frame flickers during session switching. We implement CSM in edge-rendering multiplayer games and deploy it on a 5G test bed with a full-stack user plane protocol stack. The evaluation results show that CSM can reduce downtime to < 14 ms and the service migration process is user imperceptible.

关键词： mobile edge computing   task offloading   multi-queue joint optimization   Lyapunov optimization   deep reinforcement learning  

摘要： With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users' long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions.

关键词： Task analysis   Servers   Resource management   Processor scheduling   Optimal scheduling   Computational efficiency   Energy harvesting   Mobile edge computing   asynchronous computing   user scheduling   wireless power transfer  

摘要： In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.

关键词： Task analysis   Servers   Mobile handsets   Approximate computing   Real-time systems   5G mobile communication   Performance evaluation   Mobile edge computing   resource allocation   testbed   computer-vision applications   convex optimizations  

摘要： Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique to prolong battery lifetime and enhance the computational capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server that assists users in executing computation-intensive tasks via offloading. Exploiting approximate computing in MEC, we can trade the output accuracy over a subset of offloading data instead of the entire dataset. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, aiming to optimize the trade-off between energy consumption and latency. Due to the mixed-integer nature of this problem, we employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems-namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of the Lagrangian multiplier associated with the primal problem;and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

关键词： Streaming media   Backhaul networks   Satellites   Quality of experience   Satellite broadcasting   Bandwidth   Servers   Video streaming   mobile edge computing   super-resolution   satellite backhaul  

摘要： Using satellite communications as backhaul links facilitates extending network coverage to unconnected areas. However, providing high-quality video streaming service via satellite backhaul is not economical. This paper presents SatSR, a mobile edge computing (MEC)-based super-resolution (SR)-enhanced adaptive on-demand video streaming system for mobile networks with satellite backhauls. Particularly, SR-based video quality enhancement is integrated into the video streaming process, so that low-quality videos with small sizes can be transmitted by satellite links and then enhanced to be high-quality. Meanwhile, SatSR offloads computation-intensive SR processing from user equipment (UE) to the MEC server to relieve UEs' computation burden and speed up the SR processing. Specifically, the framework and the operation process of SatSR are designed first. Then, to mitigate the impact of SR processing delay, a pipelined mechanism is proposed, which can coordinate the video transmission and SR-based enhancement efficiently. Furthermore, an SR scale factor adaptation algorithm based on deep reinforcement learning is proposed to cope with the fluctuation of communication links. Finally, a system prototype and a chunk-level simulator of SatSR are built, respectively. The experiments results validate that SatSR outperforms baselines significantly, including both the UE-based SR-enhancement video streaming scheme and the traditional bitrate adaptation based video streaming scheme.

关键词： Emergency logistics   Resource scheduling   Cloud computing   Emergency supplier distribution   Mobile edge computing   Vehicle  

摘要： In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio.

关键词： Streaming media   Servers   Bit rate   Quality of experience   Backhaul networks   Bandwidth   Adaptive systems   Mobile edge computing   adaptive bitrate streaming   reinforcement learning   content caching   content popularity  

摘要： Mobile Edge Computing (MEC) has become an important concept in modern video communication and broadcasting scenarios to address varied user expectations in an ever-evolving network environment. It has been observed that by preventing redundant access to the Origin Server through backhaul links, caching popular video content in MEC servers minimizes network congestion. Designing an efficient caching mechanism in the MEC server is challenging. To maintain a decent QoE (Quality of Experience) for end-users, we must consider diverse parameters like content popularity, network conditions, etc. In this work, we have proposed a QoE-aware Adaptive BitRate (ABR) caching mechanism at the MEC server using Reinforcement Learning (RL). The proposed model predicts the content popularity of the video and the most preferred video quality for the end-users of a Base Station. In this work, an efficient caching mechanism is devised in the MEC server to provide a decent QoE among the end-users. The primary goal of our RL-based framework is to increase the cache hit rate and reduce the backhaul load while maintaining a satisfactory QoE. Experimental results demonstrate that the proposed model, which emphasizes on video quality, video quality switching, and cache hit rate, outperforms state-of-the-art caching algorithms in terms of the overall QoE reward.