关键词： Many-objective optimization   mobile edge computing   resource allocation   task offloading  

摘要： Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. Alow-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

关键词： Mobile edge computing  

摘要： Smart mobility is emerging, addressing heterogeneous scenarios with high impact on technology infrastructures, solutions, and people. Safety and availability are mandatory, forcing the design of new reliable services for localization, health monitoring of the user, maintenance of vehicle, and protection of the environment. This paper proposes a container-based microservice approach to the edge computing in IoT smart mobility scenarios. Since smart mobility backends must manage a large heterogeneity of applications, the proposed approach is promising with respect to the classical solutions (based on "monolithic hardware+software" devices), from the point of view of flexibility, upgradability, security, scalability, and reliability. A demo use case, based on industry-grade hardware and Docker, has been realized and multiple implementations of the same services have been executed in parallel, showing strong independence between them. Moreover, average delays of less than 10 ms are obtained, confirming the usability in several smart mobility (and smart city) applications. © 2023, Ada-Europe. All rights reserved.

关键词： mobile edge computing   directed acyclic graphs   deep reinforcement learning   soft actor-critic   Markov decision process   task offloading  

摘要： Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network. However, several critical challenges, including offloading methods, network dynamics, resource diversity, and server decision-making, remain open. Regarding offloading, most conventional approaches have neglected or oversimplified multi-MEC server scenarios, fixating on single-MEC instances. This myopic focus fails to adapt to computational offloading during MEC server overload, rendering such methods sub-optimal for real-world MEC deployments. To address this deficiency, we propose a solution that employs a deep reinforcement learning-based soft actor-critic (SAC) approach to compute offloading and facilitate MEC server decision-making in multi-user, multi-MEC server environments. Numerical experiments were conducted to evaluate the performance of our proposed solution. The results demonstrate that our approach significantly reduces latency, enhances energy efficiency, and achieves rapid and stable convergence, thereby highlighting the algorithm's superior performance over existing methods.

关键词： Mobile edge computing   battery saving   wireless power transfer   full duplex   Stackelberg game  

摘要： Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

关键词： Mobile edge computing   Smart factory   Task caching   Task offloading  

摘要： In the application scenario of a smart factory, the computing tasks of the smart devices are intensive, and these tasks are sensitive to delay. The smart devices need to offload their tasks to an edge server for execution. However, the computing resources of edge servers are limited, and a workload imbalance will tend to arise among multiple edge servers. To solve these problems, we propose a joint optimization scheme of task caching and offloading based on task scenario awareness, which decouples the joint optimization problem into two subproblems: task caching and task offloading. These two subproblems are solved using a task-scenario-aware bilateral auction algorithm, which enables an edge server to adapt its task caching strategy to the task scenario of the smart devices. Experimental results show that this paper's scheme reduces the task execution time by up to 21.5% compared to existing schemes and has better load balancing effects.

关键词： Double deep Q network   Partial offloading   Karush-Kuhn-Tucker   Mobile edge computing   Wireless node  

摘要： The 5G Wireless Environments have huge data transmission;therefore, there is an increase in the requests for computational tasks from Intelligent Wireless Mobile Nodes. This computational capability leads to high reliability and low latency in a 5G network. Mobile edge computing (MEC) allows end systems with constrained computing capacity to handle computationally demanding tasks and offer accurate alternatives. The MEC server's physical position is nearer to WN than other servers, which satisfies the demands for low latency and excellent dependability. To overcome the issues of existing work, such as low latency, offloading and task scheduling, the proposed method provides efficient results. In this work for job scheduling, Multi-agent Collaborative Deep Reinforcement Learning based Scheduling Algorithm with a double deep Q network (DQN) is used in the MEC system. To minimize the total Latency in Wireless Nodes, it uses Karush-Kuhn-Tucker (KKT) approach. This approach provides the optimum solutions to the partial and complete offloading tasks. The double deep Q network (DQN) reduces energy consumption and offers better convergence Between the Wireless Nodes. Compared to traditional algorithms like DeMDRL and BiDRL, the proposed MDRL-DDQN demonstrates superior performance.

关键词： Reliability   Servers   Measurement   Cloud computing   Clustering algorithms   Low latency communication   Costs   Network function virtualization   mobile edge computing   latency   reliability  

摘要： Mobile edge computing allows efficient cloud servicing by building small-scale cloud infrastructures at the network edge close to end users. The cloud servers implement virtual network functions in a flexible manner without dedicated middleboxes. In such a network design, latency and reliability are two key performance metrics which are not always aligned and a tradeoff can appear between them. In this paper, we identify three key factors that impact the existence of a tradeoff such as the number of services, the number of functions the services require and the existence of redundancy in the implementation of functions. Our main contribution is indicating exactly whether a tradeoff can exist in each combination of these three factors. In case a tradeoff exists, we find guarantees on the performance in a non-optimized metric of an optimal solution to the other metric. We also study the tradeoff when each service should be served in a particular order. We also propose a function placement method that jointly optimizes both latency and reliability. Lastly, we evaluate our analysis in an experimental setting.

关键词： satellite edge computing   mobile edge computing   task offloading   data-dependent constraint   multi-objective optimization  

摘要： By enabling a satellite network with edge computing capabilities, satellite edge computing(SEC) provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network.

关键词： Mobile edge computing   unmanned aerial vehicle   computation offloading   deep reinforcement learning   digital twin  

摘要： With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

关键词： Mobile edge computing   Resource allocation   Task offloading   Double weighted support vector transfer regression   Flow direction algorithm   Smart city  

摘要： Mobile edge computing (MEC) technology is gaining more attention in smart cities due to its powerful computation capability. However, there arise complications related to security and privacy while transmitting and processing raw data to other cloud or MEC servers. This makes the users unwilling to update their private information on the cloud servers. To tackle this issue, we proposed a novel approach for optimal task scheduling and resource allocation processes in this paper. The proposed 'double-weighted support vector transfer regression based flow direction (DSTR-FD) approach' resolves the issues of resource management of edge servers and makes optimal task offloading decisions with minimized energy consumption. Here, the model parameters such as weight functions, regularization parameters, and kernel parameters of the DSTR network are tuned using the flow direction (FD) algorithm. The proposed method thus provides better data privacy without sharing the original data with other servers along with minimizing the utilization of energy in the Internet of Things (IoT). The efficiency of the proposed DSTR-FD approach is evaluated by comparing its results with other states of art methods. The simulation experiments illustrate that the proposed DSTR-FD approach effectively minimizes the energy utilization of all IoT devices.