关键词： Mobile edge computing  

摘要： End-edge-cloud computing improves efficiency and reduces latency by allocating mobile device tasks between edge nodes and cloud servers. However, task allocation and resource pricing in complex multi-user, multi-task environments remain challenging. This paper proposes an end-edge-cloud task offloading strategy based on blockchain technology, utilizing Stackelberg game theory for resource pricing (BMSGRP). We designed a multi-level Stackelberg game model with cloud servers as the leaders, edge servers as the sub-leaders, and mobile devices as the followers. By proving the monotonicity of the overall system benefit model, we derived the unique equilibrium solution. Then, we use blockchain technology to construct a decentralized resource transaction ledger, recording the task offloading and resource pricing results of various devices. We employ the an improved consensus mechanism based on reputation scoring (mRS-DBFT) to ensure the security and transparency of data and transactions. Finally, we evaluated the performance of this strategy through simulation experiments across various scenarios. The results indicate that our method significantly improves system efficiency compared with local task execution and random offloading. © 1975-2011 IEEE.

关键词： Blockchain   Deep reinforcement learning   Industrial Internet   Mobile edge computing   Resource allocation optimization  

摘要： Blockchain theory and its key technologies are developing rapidly, and the industrial internet combined with blockchain technology is driving the realization of safe and reliable comprehensive connectivity in multiple fields. In this paper, we propose a resource optimization allocation method for industrial internet that integrates edge computing and blockchain to reduce the task computing energy consumption and computational overhead of the system while improving the efficiency of the consensus process. This optimization problem is constructed as a Markov decision process, and a deep reinforcement learning algorithm is used to solve the optimal resource scheduling strategy under a single edge node. The effectiveness of the proposed resource optimization allocation method for industrial internet fusing edge computing and blockchain is verified by simulation validation. The method is able to obtain better and smoother convergence under the premise of harvesting high total rewards, effectively reduces the computational energy consumption and computational overhead of the device, and at the same time effectively improves the consensus efficiency of the blockchain. © 2025 The Author(s).

关键词： Mobile edge computing  

摘要： The service modules of the traditional Mobile Edge Computing (MEC) are difficult to deploy, extend, and maintain in real networks because of the highly sophisticated systems. To promote the generalization, openness, and flexibility of the network edge environment, an increasing number of studies are exploring the integration of microservices with MEC. However, the existing work usually treats microservice deployment and request routing as two separate issues, ignoring the interaction between them. Therefore, this paper focuses on the joint optimization of microservice deployment and request routing in the multi-edge cloud scenarios. We establish a problem model for minimizing the average response latency, considering the transmission of requests across edge clouds. Then, in view of the complexity of the scene, this paper proposes a joint training strategy of microservice deployment and request routing based on deep reinforcement learning and Best Fit Decreasing algorithm. The algorithm takes the change of microservice deployment scheme as the action of the agent, introduces the Best Fit Decreasing algorithm to construct request routing based on the deployment scheme, and calculates rewards using the complete joint microservice deployment and request routing scheme for subsequent network training. Finally, experimental results show that the proposed algorithm can effectively reduce the response time delay and system running power compared with other algorithms. © 2004-2012 IEEE.

关键词： Mobile edge computing  

摘要： We investigate the problem of supporting Industrial Internet of Things user equipments (IIoT UEs) with specific intents (i.e., requested quality of service (QoS)) and random traffic arrivals. We propose a centralized dynamic scheduler for time-frequency resources based on deep reinforcement learning (DRL) to efficiently allocate communication resources among IIoT UEs. The proposed scheduler leverages a DRL framework to adapt to the dynamic changes in the wireless communication system and traffic arrivals. Moreover, we introduce a graph-based reduction scheme to reduce the state and action space of the DRL framework to allow fast convergence and a better learning strategy. Simulation results demonstrate the effectiveness of the proposed intelligent scheduler in guaranteeing the expressed intents of IIoT UEs compared to several traditional scheduling schemes, such as round-robin, semi-static, and heuristic approaches. The proposed scheduler also outperforms the contention-free and contention-based schemes in maximizing the number of successfully computed tasks. © 2015 IEEE.

关键词： Mobile edge computing  

摘要： In this letter, we propose an energy-efficient split learning (SL) framework for fine-Tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a Cut lAyer and computing Resource Decision (CARD) algorithm is developed to minimize training delay and energy consumption. Simulation results demonstrate that the proposed approach reduces the average training delay and server's energy consumption by 70.8% and 53.1%, compared to the benchmarks, respectively. © 2019 IEEE.

关键词： Mobile edge computing  

摘要： In order to solve the problems of high packet loss rate, low configuration balance and long time in traditional resource allocation methods, a resource allocation method of distributed wireless network based on mobile edge computing is proposed. The mobile edge computing is used to select the distributed wireless network service nodes, so as to determine the constraints such as task unloading, resource allocation, task execution delay, transmission rate, etc., build the distributed wireless network resource allocation objective function, and use the genetic-annealing algorithm to solve the objective function. The optimal solution is the optimal distributed wireless network resource allocation scheme. The experimental results show that the average packet loss rate of this method is 3.06%, the resource allocation balance of distributed wireless network varies from 0.96 to 0.96, and the average allocation time is reduced by 2.12 s and 1.19 s respectively compared with the two experimental comparison methods. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Mobile edge computing  

摘要： With the rapid proliferation of mobile devices, a growing number of intelligent applications are being deployed at the network edge, placing immense strain on the processing capabilities of edge computing. Therefore, resourceconstrained edge servers frequently experience overload due to highly dynamic workloads. To address this, one approach involves forwarding user requests to the cloud or other edge servers, albeit at the cost of increased transmission latency. Alternatively, dynamic scaling of edge clusters can be employed to enhance processing capacity, thereby mitigating latency but at the expense of additional service configuration and hosting expenses. By integrating their complementary benefits, we study the joint optimization problem of dynamic scaling and request routing within a multi-edge cluster collaborative framework, which fully exploits cluster resources to manage the temporal and spatial varying edge workloads. This collaborative framework aims to minimize overall request latency while satisfying an acceptable time-averaged budget cost. However, the complex coupling between scaling and routing decisions, along with the uncertainty of future system information (e.g., user request workloads) impedes the derivation of an optimal offline policy over the long term. Thus, considering the different decision granularities, we employ the two-timescale Lyapunov optimization technique to decouple the original problem into a series of independent online optimization problems with the current system state. In particular, we make cluster scaling decisions in each large timescale and request routing decisions in each small timescale. Given that the decoupled large-timescale subproblems involve NP-hard mixed-integer linear programming, we design an edge resource-aware greedy rounding algorithm to efficiently produce approximate optimal solutions. Finally, both rigorous theoretical analysis and extensive trace-driven evaluations demonstrate the superiority of our p

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Contextual adaptation   feature engineering   mobile edge computing   predictive analytics   split learning   Contextual adaptation   feature engineering   mobile edge computing   predictive analytics   split learning  

摘要： The rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast scale and detailed content. To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). ESDDM combines multiple data streams to better understand consumer electronics systems beyond the normal Machine Learning (ML) capabilities. ESDDM's integration with Split Learning (SL) protects data by lessening transmission risks and avoiding central cloud storage while keeping sensitive information secure on user devices and delivering better system performance. The prediction results from ESDDM show its strength with a low Mean Squared Error (MSE) of 0.267, which reveals its capability to reshape the MEC domain while creating business possibilities and better customer outcomes.

关键词： Mobile edge computing  

摘要： Given billions of mobile users, the environmental impact of edge computing is significant. To address this, future applications need to execute computations on a green component which is fueled by renewable energy sources. However, because of the intermittent nature of the renewable energy sources, the carbon intensity of computing components can significantly vary with location and time of use. This poses a new challenge for edge applications – deciding when and where to run computations across consumer devices at the edge and servers in the cloud. Such scheduling decisions become more complicated with the amortization of the rising embodied emissions and stochastic runtime variance. This work proposes GreenScale, an intelligent execution scaling engine that accurately selects the carbon-optimal execution target for edge applications in different runtime environments. Our evaluation with three representative categories of applications (i.e., AI, Game, and AR/VR) demonstrate that the carbon emissions of the applications can be reduced by 35.2%, on average, with GreenScale. © 2014 IEEE.