关键词： Virtual machine placement   Container consolidation   Mobile edge computing   Similarity   Spectral property  

摘要： An optimized container consolidation is a key factor in Mobile Edge Computing. It is a challenging problem as its complexity is NP-hard. In addition to structural properties of a network which reveal nodes features and their connectivity, we believe that the spectral properties of correlation between rows of similarity matrix of the network could be more beneficial in placement algorithm. Most existing literature focus on reducing energy consumption in cloud or edge environments based on structural properties of the given network. However, they undermine the concept of locality which could affect excessive network traffic. Our proposed method utilizes structural and spectral properties of the network to solve the problem. We expected to improve locality factor and link reduction as well as reducing the problem space by utilizing a centroid-based approach and a queue. Our proposed algorithm preserves topology architecture without consuming more energy in comparison to the existing popular algorithms.

关键词： Streaming media   Quality of experience   Bit rate   Channel allocation   Bandwidth   Wireless communication   Resource management   Mobile edge computing   video streaming   resource allocation   deep reinforcement learning  

摘要： This letter investigates a video streaming system enabled by mobile edge computing (MEC), where we aim to maximize the user quality of experience (QoE) under dynamic mobile environments, through user association, bitrate adaptation and bandwidth allocation. To tackle this hybrid optimization problem, we first decompose it into discrete inter-frame and continuous intra-frame problems. In further, an in-depth analysis of intra-frame bandwidth allocation is conducted, guiding the design of a modified equal bandwidth allocation (MEBA) algorithm. Furthermore, we adopt a deep reinforcement learning (DRL) approach to cope with inter-frame user association and bitrate adaptation in dynamic mobile environments, while embedding MEBA in the DRL algorithm to jointly tackle the hybrid problem. Simulations are finally conducted to verify the superiority of the proposed method under the dynamic environment.

关键词： Mobile edge computing   Task offloading   Task scheduling   Particle swarm optimization   Genetic algorithm  

摘要： Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18-31% and can be applied to scenarios with tight task deadlines.

关键词： micro-service composition   micro-services   mobile edge computing   multi-objective optimization  

摘要： As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro-services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro-services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro-services constructed by ignoring the processing capabilities of different micro-services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro-services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro-service composition. In order to improve edge resource utilization and user experience on micro-service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro-services composition problem in MEC as a constrained multi-objective optimization problem. Then, a micro-service composition optimization method M3C combining graph search and branch-and-bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

关键词： blockchain   consensus algorithm   mobile edge computing   reputation mechanism  

摘要： Mobile edge computing (MEC) promotes the development and popularity of Internet of Things (IoT) devices with higher connectivity and ultra-low latency through network topology optimization and real-time data analysis. With the exponential growth of data traffic generated by IoT devices, it is essential to prevent edge servers (ESs) from overloading through efficient task scheduling to ensure the quality of service (QoS). However, the lack of trust and incentive mechanisms between ESs deployed and managed by different infrastructure providers makes it challenging to perform peer-offloaded tasks. Moreover, to meet the low-latency requirement of many IoT applications, the execution efficiency of the task scheduling scheme in MEC needs to be improved. To address the above challenges systematically, a QoS-driven task scheduling scheme with blockchain in MEC named SharpEdge is proposed. In SharpEdge, ESs publish tasks with rewards and then select the most reliable executors through a historical performance-based reputation mechanism to perform peer offloading. The performance of the executor will be recorded into the blockchain after the task is completed. In addition, a concurrent consensus mechanism using sharding technology is designed, improving task scheduling efficiency. We implement SharpEdge based on Hyperledger Fabric and verify its performance in a simulated MEC environment.

关键词： computing offloading   deep reinforcement learning   internet of things   mobile edge computing  

摘要： In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency-sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large-scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large-scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end-edge-cloud" along with the corresponding time-overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy.

关键词： Mobile edge computing   Deep reinforcement learning   Parallel exploration   Asynchronous training   Offloading decision  

摘要： In mobile edge computing (MEC), randomly offloading tasks to edge servers (ES) can cause wireless devices (WD) to compete for limited bandwidth resources, leading to overall performance degradation. Reinforcement learning can provide suitable strategies for task offloading and resource allocation through exploration and trial-and-error, helping to avoid blind offloading. However, traditional reinforcement learning algorithms suffer from slow convergence and a tendency to get stuck in suboptimal local minima, significantly impacting the energy consumption and data timeliness of edge computing task unloading. To address these issues, we propose Parallel Exploration with Asynchronous Training-based Deep Reinforcement Learning (PEATDRL) algorithm for MEC network offloading decisions. Its objective is to maximize system performance while limiting energy consumption in an MEC environment characterized by time-varying wireless channels and random user task arrivals. Firstly, our model employs two independent DNNs for parallel exploration, each generating different offloading strategies. This parallel exploration enhances environmental adaptability, avoids the limitations of a single DNN, and addresses the issue of agents getting stuck in suboptimal local minima due to the explosion of decision combinations, thereby improving decision performance. Secondly, we set different learning rates for the two DNNs during the training phase and trained them at various intervals. This asynchronous training strategy increases the randomness of decision exploration, prevents the two DNNs from converging to the same suboptimal local solution, and improves convergence efficiency by enhancing sample utilization. Finally, we examine the impact of different parallel levels and training step differences on system performance metrics and explain the parameter choices. Experimental results show that the proposed method provides a viable solution to the performance issues caused by slow converge

关键词： Mobile edge computing  

摘要： In the 5G era and beyond, it is favorable to deploy latency-sensitive and reliability-aware services on edge computing networks in which the computing and network resources are more limited compared to cloud and core networks but can respond more promptly. These services can be composed as Service Function Chains (SFCs) which consist of a sequence of ordered Virtual Network Functions (VNFs). To achieve efficient edge resources allocation for SFC requests and optimal profit for edge service providers, we formulate the SFC placement problem in an edge environment and propose a multi-agent Reinforcement Learning (RL) scheme to address the problem. The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions, such as path selection, VNF configuration, and VNF deployment. Simulation results show our model can improve the profit of edge service providers by 12% compared with a heuristic solution. © 2024, CC BY.

关键词： internet of things   mobile edge computing   reinforcement learning   resource allocation   snake swarm optimization  

摘要： Mobile edge computing has become popular in the past few years as a means of creating computing resources close to end-user nodes at the network edge. Nodes-end users-demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor-based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

关键词： Mobile edge computing   multiple objective optimization   object detection   task assignment   video analytics   Mobile edge computing   multiple objective optimization   object detection   task assignment   video analytics  

摘要： In this article, we investigate the optimal configuration and dependence-aware task assignment for multitask edge video analytics. Multitask video analytics involves multiple objects in video frames and multiple dependent tasks, resulting in existing video configuration and task assignment scheme for single-task unsuitable to this scenario. This article aims to efficiently assign dependent tasks to multiple collaborative edge nodes with appropriate video configuration, to achieve low latency while maintain accuracy. First, we conduct extensive experiments on real-world video data sets. The results reveal that the impact of resolution on the detection accuracy varies among different sizes of objects. Moreover, the computing and communication load of dependent tasks varies along the time due to the dynamic video content. Based on the experimental results, we propose a threshold-based downsampling strategy for large objects, aiming at minimizing the transmission latency while guaranteeing task analytic accuracy. In addition, the number of objects and workload of subsequent tasks turn out to be highly correlated, the computation and transmission demands of tasks can be thus estimated for each video chunk. Then, a heuristic dependence-aware task assignment algorithm is proposed to achieve minimum completion time of dependent tasks. Experimental results demonstrate that the proposed scheme can effectively reduce the execution time of multiple tasks while guaranteeing the analytic accuracy, outperforming the state-of-the-art benchmarks.