关键词： Task analysis   Resource management   Optimization   Costs   Load management   Collaboration   Cloud computing   Computation offloading   deep reinforcement learning   mobile edge computing   resource allocation   task migration  

摘要： Internet-of-Things (IoT) has played a critical role in developing sustainable smart cities and emerging numerous latency-sensitive IoT applications. Mobile edge computing (MEC) federation has the capability to incorporate a transparent resource management approach, which enables the sharing and utilization of MEC services from edge infrastructure providers (EIPs) and provides agile access services to mobile devices (MDs). In this paper, we investigate the joint optimization problem of computation offloading, task migration, and resource allocation in the MEC federation. The objective is to minimize the weighted sum of latency and energy consumption while maintaining load balancing under the constraint of the long-term migration cost budget of EIPs. To address the problem, we decompose it into two sub-problems: 1) the MDs clustering sub-problem and 2) the sub-problem of joint computation offloading, task migration, and resource allocation. First, an MDs clustering matching (MDCM) algorithm is proposed to cluster the MDs in edge servers (ESs) according to the differences in channel gains. Afterward, the second sub-problem is simplified by the Lyapunov optimization technique, and then we propose a Transformer-based mobility prediction model and a decentralized deep deterministic policy gradient (DDPG)-based framework to solve it. Extensive simulation results demonstrate the cost-efficiency of the proposed algorithm.

关键词： Internet of vehicle   Mobile edge computing   Cloud computing   Resource allocation   Offloading strategy  

摘要： Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud- edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

关键词： Mobile edge computing   Truthfulness   Resource allocation   Algorithm design  

摘要： We address the problem of resource allocation with multi-attribute demand considering heterogeneous servers in mobile edge computing (MEC). A mobile device (MD) may be within the coverage area of multiple access points and can get resources from any server that can establish direct communication. However, the configurations of heterogeneous servers are different, so the demand for each MEC server is different. We tackle this problem and consider multi-attribute demand, where each MD is assigned exactly one of multiple demands. We formulate the allocation problem in an auction-based setting to provide server scalability. We propose a family of greedy mechanisms to solve the resource allocation problem. However, each MD is self-interested and may try to manipulate the system to maximize its utility. We show our mechanisms are truthful, and they drive the system into an equilibrium where no MD can declare an untrue value to obtain higher utility. We also analyze the approximation ratios of our greedy mechanisms. Experimental results demonstrate that the greedy mechanisms obtain the near-optimal allocation in a short period while benefiting MDs and edge cloud providers.

关键词： Mobile Edge Computing   Smart Manufacturing System   Offloading Decision   E-CARGO   Dynamic Weights   Resource Contention Mitigation  

摘要： With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E- CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

关键词： Deep reinforcement learning   LEO satellites   mobile edge computing   mobile edge computing   proximal policy optimization   proximal policy optimization   mobile edge computing   proximal policy optimization   spatiot- emporal load  

摘要： Satellite edge computing, as an extension of ground edge computing, is a key technology for providing computing services by deploying resources on low earth orbit (LEO) satellites. However, the temporal and spatial differences in population density and economic levels may lead to unbalanced computation workloads on LEO satellites. Considering the mobility and limited resources inherent in LEO satellites, effectively utilizing the LEO satellite network to meet global competitive demands for task offloading becomes challenging. Therefore, in this paper, we propose an adaptive task offloading approach with spatiotemporal load awareness, named ATO-SLA, in satellite edge computing, aiming to optimize users' perceived-delay and energy consumption. Specifically, to avoid LEO satellite overload, we first introduce the spatiotemporal load factor, formally modelling the spatiotemporal load-aware task offloading problem. Then, the Markov decision process is employed to structure the task offloading decision process. Afterwards, we propose a task offloading algorithm based on proximal policy optimization strategy to adaptively solve the problem. Finally, experimental results demonstrate that ATO-SLA achieves a lower average delay and average energy consumption compared with other approaches.

关键词： Mobile edge computing   Truthfulness   Resource allocation   Energy consumption   Mechanism design  

摘要： Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.

关键词： Mobile edge computing   PPO algorithm   Task offloading   GPS-free   Deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) has gained significant attention in recent years due to its capability of providing computation and storage resources closer to end-users. However, existing research has focused on addressing MEC resource optimization problems with explicit location information and has not considered the challenges posed by users who refuse to grant system access to Global Positioning System (GPS) information. To address this issue, this paper models the location-protected MEC problem as an Integer Non-Linear Problem (INLP) and proposes a novel mechanism to cater to various types of users while optimizing both computation time and service latency. This problem is proposed to be solved by decoupling it into a path recommendation problem and a task offloading problem. For different user types, a Proximal Policy Optimization (PPO) algorithm is proposed to recommend user paths. By following these paths, multi-user interference is minimized. Then, the Deep Q-Network (DQN) algorithm is employed to determine MEC offloading strategies to solve the task offloading problem. Experimental results demonstrate that the algorithm outperforms related approaches across multiple dimensions, including computation time, service latency, and stability. Additionally, the proposed mechanism effectively addresses the offloading challenge for privacy-conscious users in edge computing scenarios. The proposed mechanism has several potential applications, such as resource optimization in mobile cloud computing, the Internet of Things (IoT), and smart cities. It also addresses optimization problems for various types of users in mobile edge computing technology.

关键词： Streaming media   Stochastic processes   Video recording   Quality assessment   Performance evaluation   Delays   Visual analytics   Mobile edge computing   video offloading   mobility   video quality   stochastic program   sample average approximation   clustering   multi-armed bandit   online learning  

摘要： Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many camera-equipped mobile devices, such as surveillance drones, require realtime video analytics, encompassing tasks like object detection and action recognition. These devices, restricted by limited resource constraints, need to offload videos to Mobile Edge Computing (MEC) to simultaneously optimize video analytics performance and minimize delay. However, MEC is facing significant challenges in providing efficient video offloading solutions, especially due to uncertainties caused by dynamic device mobility and the associated trade offs in selecting video quality for offloading. Offloading high-quality videos enhances video analytics performance, such as object detection accuracy. Yet, as a mobile device relocates, lower video quality or serving by a different MEC cloudlet may be required (triggering a service migration) to maintain a satisfactory service performance. In this paper, we study the Video Offloading Problem (VOP) in MEC to address these challenges. We propose two uncertainty-aware approaches that model the uncertainties in the environment to solve VOP. Our first approach, focusing on system side, is based on Two-stage Stochastic Program and proposing a unique clustering-based Sample Average Approximation to effectively solve TSP-VOP. The second approach, focusing on device side, employs an online learning algorithm based on a multi-armed bandit to learn and select the optimal offloading solution online. Through extensive experiments, we show that our proposed approaches significantly enhance video offloading decisions, with high video quality and reduced service migration costs under uncertain device mobility, compared to other benchmarks.

关键词： Mobile edge computing  

摘要： Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwidth, latency, jitter, and data packet loss rate. However, most existing QoS datasets, such as the common WS-Dream dataset, focus mainly on static QoS metrics of network services and ignore dynamic attributes such as time and geographic location. This means they should have detailed the mobile device’s location at the time of the service request or the chronological order in which the request was made. However, these dynamic attributes are crucial for understanding and predicting the actual performance of network services, as QoS performance typically fluctuates with time and geographic location. To this end, we propose a novel dataset that accurately records temporal and geographic location information on quality of service during the collection process, aiming to provide more accurate and reliable data to support future QoS prediction in mobile edge environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile deep vision systems play a vital role in numerous scenarios. However, deep learning applications in mobile vision scenarios face problems such as tight computing resources. With the development of edge computing, the architecture of edge clouds has mitigated some of the issues related to limited computing resources. However, it has introduced increased latency. To address these challenges, we designed CloudEye which consists of Fast Inference Module, Feature Mining Module and Quality Encode Module. CloudEye is a real-time, efficient mobile visual perception system that leverages content information mining on edge servers in a mobile vision system environment equipped with edge servers and coordinated with cloud servers. Proven by sufficient experiments, we develop a prototype system that reduces network bandwidth usage by 69.50%, increases inference speed by 24.55%, and improves detection accuracy by 67.30%. © 2024, CC BY.