关键词： Computation offloading   Edge orchestration   EdgeCloudSim   Internet of things   Mobile edge computing   Offloading decision   Processing optimization  

摘要： The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version. © 2024 Institute of Advanced Engineering and Science. All rights reserved.

关键词： computation offloading   data offloading   delay   energy consumption   execution time   Femtolet   MCC   MEC   mobile cloud computing   mobile edge computing   QoE   QoS   quality of experience   quality of service  

摘要： Mobile cloud computing (MCC) is the recent technology that to provide execution of different mobile applications. Mobile devices are flexible and portable. The key disadvantages of mobile devices are limited battery life, lower bandwidth and insufficient storage capabilities. MCC reduces the drawbacks of mobile devices. The resolve the execution delay of mobile devices mobile edge computing (MEC) concept was proposed. Offloading is an effective method to save execution time and energy both. Code or Application Offloading allows executing a task in Cloud instead of Mobile Device itself. This paper presents a comprehensive discussion on different methodologies and the apparatus used for offloading of code or application into cloud. This paper also highlights on merits and demerits of different offloading techniques. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Autonomous aerial vehicles   Multi-access edge computing   Edge computing   Reconfigurable intelligent surfaces   Resource management   Trajectory optimization   Urban areas   Ground support   Energy management   Energy consumption   Lyapunov methods   Lagrangian functions   Stability analysis   Simulation   Unmanned aerial vehicle (UAV) communication   mobile edge computing   reconfigurable intelligent surface   trajectory optimization   resource allocation  

摘要： In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable intelligent surface (RIS) is proposed in this paper. The goal of this system is to minimize energy consumption by considering various factors such as user transmission power, intelligent reflector phase shift matrix, UAV trajectory, computational resource allocation and the stability of task queues. By applying the Lyapunov optimization theory, the original non-convex problem is decomposed into a series of optimization-related sub-problems. Additionally, it is possible to obtain the closed solution to the original problem through Lagrange dual, Karush-Kuhn-Tucker (KKT) conditions, and mixed differential genetic evolution algorithm (MDGEA). As indicated by the simulation results, the proposed RIS-assisted UAV edge computing system is more effective in reducing energy consumption for the system than traditional UAV mobile edge computing on the basis of maintaining queue stability.

关键词： Mobile edge computing  

摘要： With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms. © 2024 Inderscience Enterprises Ltd.

关键词： Mobile edge computing   resource allocation   resource allocation   mobile edge server   mobile edge server   edge device   edge device   edge device  

摘要： Mobile Edge Computing (MEC) has the potential to provide computational resources to edge devices. The Edge device (Ed) does not have enough computational resources, so it offloads its tasks to nearby Mobile Edge Servers (MES), while sometimes offloading tasks to MES causes MES to overload. The overload state of MES is when the data occupies all the CPU cores and memory of MES, so MES rejects requests for data offloading from other Eds. In this research, we have considered a multi-server, multi-device scenario, for which we have proposed an algorithm called CPU Memory Mobility Resource Allocation (CMMRA) that does not reject Ed from providing computational resources and encourages them to migrate the task to another MES in case the current MES is overloaded. The algorithm increases the service rate of a communication area, the rejection rate of the devices is also reduced, the total processing time of MES in this research is less as compared to others, and the migration in our proposed algorithm is greater than others. As we consider the mobility of devices and many devices are requesting computational resources, if the current MES is busy, it migrates the task to an underloaded MES for computation.

关键词： Task analysis   Cloud computing   Delays   Simulated annealing   Optimization   Computer architecture   Servers   Scheduling   Online services   Online task scheduling   simulated annealing   mobile edge computing   task offloading  

摘要： This paper introduces SATS, an innovative online task scheduling strategy tailored for hierarchical Mobile Edge Computing (MEC) architectures. Leveraging a Simulated Annealing-based approach, SATS extends the applicability of this method beyond offline scheduling, demonstrating its efficacy in real-time task allocation. Crucially, the paper underscores the pivotal role of accurate service request predictions in enhancing SATS' performance. Through a comparative analysis of three prediction models, neutral, conservative, and optimistic, the paper reveals that SATS achieves optimal outcomes when paired with a conservative predictor, which deliberately overestimates service request volumes. In fact, employing this predictor yields significantly higher acceptance rates and reduced processing times. Remarkably, SATS, coupled with a conservative predictor, achieves an acceptance ratio within a mere 5% deviation from an ideal scenario where the frequency of service request arrivals is known in advance, maintaining consistency across various experimental scenarios.

关键词： Mobile edge computing   integrity   data right confirmation   consensus   merkle tree  

摘要： As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

关键词： Mobile edge computing  

摘要： Edge computing operates between the cloud and end users and strives to provide low-latency computing services for simultaneous users. Redundant use of multiple edge nodes can reduce latency, as edge systems often operate in uncertain environments. However, since edge systems have limited computing and storage resources, directing more resources to some computing jobs will either block the execution of others or pass their execution to the cloud, thus increasing latency. This paper uses the average system computing time and blocking probability to evaluate edge system performance and analyzes the optimal resource allocation accordingly. We also propose blocking probability and average system time optimization algorithms. Simulation results show that both algorithms significantly outperform the benchmark for different service time distributions and show how the optimal replication factor changes with varying parameters of the system. © 1993-2012 IEEE.

关键词： game training platform   virtual reality technology   mobile edge computing   genetic algorithm  

摘要： The purpose of paper is to solve the problems of difficulty in learning and low user satisfaction in the design process of the game 3D training platform. This paper used mobile edge computing and VR technology to study the construction of the game 3D modelling simulation training platform. By using mobile edge computing, users can experience faster games by improving the running speed of the 3D game training platform. The experiment proved that the game training platform built by mobile edge computing and VR technology can improve the user's game level. User A scored an average of 8.02 points, 5.22 points and 6.89 points higher than User B for 10 games of Game Q, Game W and Game P. At the same time, it can also improve the satisfaction rate of platform users. Using mobile edge computing and VR to build a game training platform can improve users' game level and users' satisfaction with the platform.

关键词： Autonomous aerial vehicles   Optimization   Resource management   Task analysis   6G mobile communication   Wireless networks   Base stations   Edge computing   Reconfigurable intelligent surfaces   Computation offloading   mobile edge computing   reconfigurable intelligent surfaces   resource allocation   unmanned aerial vehicle  

摘要： The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.