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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented. [ABSTRACT FROM AUTHOR]

The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile devices for computation-intensive mobile applications via supporting computation offloading efficiently. However, the limitation of wireless resources and computational resources of edge servers often becomes the bottlenecks to realizing the developments of MEC. In order to address the computation offloading problem in the time-varying wireless networks, the offloading decisions and the allocation of radio and computation resources need to be jointly managed. Traditional optimization methods are challenging to deal with the combinatorial optimization problem in complex real-time dynamic network environments. Therefore, we propose a deep reinforcement learning (DRL)-based optimization approach, named DRJOA, which jointly optimizes offloading decisions, computation, and wireless resources allocation. The optimization algorithm based on DRL has the advantages of fast solving speed and strong generalization ability, which makes it possible to solve combinatorial optimization problems online. Simulation results show that our proposed DRJOA in this study dramatically outperforms the benchmark methods for offloading decisions and system utility. [ABSTRACT FROM AUTHOR]

The emergence of Mobile Edge Computing (MEC) not only provides low-latency computing services for the User Equipment (UE), but also extends the battery life of the UE. However, the computational resources of MEC servers are usually limited, and how to efficiently offload UE's task and allocate the resources of MEC servers has become a research hotspot in MEC. In this paper, we develop an improved arithmetic optimization algorithm (IAOA) to optimize the convergence speed and convergence accuracy of the arithmetic optimization algorithm. Then a task offloading algorithm based on IAOA is designed to reduce the cost of offloading tasks in the framework including a single MEC server and multi-UE. The proposed algorithm jointly optimizes the task offloading strategy of the UEs and the resource allocation of the MEC server, meanwhile, models the weighted sum of delay and energy consumption as the system cost, with the goal of minimizing the system cost while satisfying the delay and energy consumption constraints of the tasks. Simulation results show that the proposed algorithm can effectively reduce the system cost and achieve a performance improvement of up to 20% compared with the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Mobile edge computing requires more and more high-performance servers, resulting in increased energy consumption. As an effective means to reduce energy consumption, virtual machine placement (VMP) has been widely studied. In the edge computing environment, as the number of terminal device requests continues to increase, the scale of VMP becomes larger and larger, and existing research algorithms may take a long time to converge. The reason is that as the number of VMs increases, the search space of the policy becomes larger and the agent needs to interact with the environment for a longer time to make the best decision. In addition, existing research methods only consider reducing energy consumption, rarely consider the response latency of virtual machines, and almost ignore the dynamic changes of the edge environment. To overcome these drawbacks, we propose a virtual machine placement algorithm based on meta-reinforcement learning, which consists of an inner and outer loop. The inner loop designs a deep reinforcement learning algorithm combined with the order exchange and migration mechanism to generate the best decision, and the outer loop provides meta-strategy parameters for the inner loop based on meta-learning to accelerate the convergence capability of the inner loop, thereby obtaining efficient virtual machine placement decisions quickly from a new environment. Through simulation experiments, we demonstrate that our approach effectively reduces the energy consumption of the edge server and the response latency of VMs at different problem sizes compared to the three baseline algorithms. At the same time, it quickly adapts to the new environment with only a small number of gradient updates. [ABSTRACT FROM AUTHOR]

With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper. [ABSTRACT FROM AUTHOR]

In mobile edge computing environment, due to resources constraints of edge devices, when user locations continue changing, the network will be delayed or interrupted, which affects the quality of user's service access. Previous studies have shown that deploying multiple microservice instances with the same function on multiple edge servers through container technology can solve this problem. However, how to choose the optimal microservice instance from multiple servers in a cloud-edge hybrid environment needs to be further investigated. This paper studies the selection of microservices problem based on the dynamic and heterogeneous characters of the cloud-edge collaborative environment, which is defined as a microservice selection and scheduling optimization problem (MSSP) to minimize users' service access delay. To cope with the complexity of cloud-edge collaborative environment and improve learning efficiency, MSSP is regarded as a Markov decision-making process, a Deep Deterministic Policy Gradient algorithm for microservice selection called MS_DDPG is then proposed to solve this problem, and the microservice selection strategy experience pool is established in MS_DDPG. Performance evaluations of MS_DDPG based on a real dataset and some synthetic dataset have been conducted, and the results show that MS_DDPG outperforms the other three baseline algorithms. In terms of average access delay, MS_DDPG is reduced by 23.82%. We also validate the performance of MS_DDPG by increasing the number of user requests, and the results also show that MS_DDPG obtains better performance in scalability. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users' quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users' preferences while estimating the popularity of content. Knowing users' preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users' preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In recent times, the Internet of Things and big data analytics have become a hot research topic in mobile edge computing (MEC) desires wide-ranging research works for intelligent decision making. In this view, this paper designs an intelligent outlier detection with machine learning empowered big data analytics (IODML-BDA) for MEC. The proposed model involves an adaptive synthetic sampling-based outlier detection technique to eradicate the existence of outliers. Besides, the oppositional swallow swarm optimization (OSSO) based feature selection technique is used to choose an effective set of features. Finally, long short-term memory based classification model is employed to identify different class labels. The design of OSSO algorithm for feature selection with ADASYN technique for big data analytics show the novelty of the work. A comprehensive experimental analysis is carried out on GPS trajectories, movement prediction, water treatment plant, hepatitis, and Twitter datasets to confirm the experimental results. The experimentation outcomes pointed out that the proposed IODML-BDA model achieves the higher accuracy of 0.9735, 0.9816, 0.9798, 0.9896, and 0.9912 on the applied datasets. [ABSTRACT FROM AUTHOR]

Mobile edge computing requires more and more high-performance servers, resulting in increasing energy consumption. As a well-established way to reduce energy consumption, virtual machine placement can be utilized to optimize the cost of wide-deployment of servers. However, traditional strategies tend to focus on single indicators, there are few existing research taking time delay limitation into account while solving energy consumption problems. In this paper, we propose a brand new method to settle the problems listed above, which is able to reduce the placement time of virtual machine and energy consumption. First, considering the excellent performance of bat swarm algorithm in NP-hard problem, we introduced the second order oscillation factor to avoid premature convergence, and combined the order exchange and migration local search technology. we proposed the OEMBA algorithm, which integrates underutilized servers to save energy. Subsequently, an improved Long Short-Term Memory model is utilized to fasten the placement of virtual machines and reduce latency based on historical data. Our results indicate that the improved learning model can save energy consumption and reduce placement latency. [ABSTRACT FROM AUTHOR]

Several applications have emerged with the proliferation of mobile devices to provide communication, learning, social networking, entertainment, and community computing services. Such applications include augmented reality, online gaming, and other real-time applications that need higher computational resources. These applications, executing on mobile devices, often need to access external computing resources and offload the application tasks to the cloud or mobile edge computing (MEC) servers. However, delivering task offloading results to the users in the MEC environment is a challenge, certainly when user mobility is high. Sub-optimal server selection at the offloading stage increases latency, energy consumption and deteriorates both quality of experience and quality of service. Existing techniques proposed in the literature handle computation offloading and mobility management separately. Without considering the real-time mobility factors, the solutions produced are sub-optimal. Some solutions exist to manage mobility, but they involve higher time complexity. We consider the user mobility in offloading decisions and present a lightweight mobility prediction and offloading (LiMPO) framework that offloads the compute-intensive tasks to the predicted user location using artificial neural networks with less complexity. In addition, we propose a multi-objective genetic algorithm based server selection technique that jointly optimizes latency and energy consumption while improving the resource utilization of MEC servers. The performance of the proposed framework is compared with two other techniques task-assignment with optimized mobility and dynamic mobility-aware offloading algorithm for edge computing. The simulation results show that LiMPO outperforms the others by latency reduction, energy efficiency, and enhanced resource utilization. [ABSTRACT FROM AUTHOR]

Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client's proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992. [ABSTRACT FROM AUTHOR]

Given the problems with a centralized cloud and the emergence of ultra-low latency applications, and the needs of the Internet of Things (IoT), it has been found that novel methods are needed to support centralized cloud technology. Mobile edge computing is one of the solutions to mitigate these challenges. In this paper, we study task caching at Device to Device (D2D)-assisted network edge. In the proposed scheme, we predict the possibility of re-requesting tasks in the future using convolutional neural networks (CNN). Based on this predicted possibility, the number of last requests, and the number of cached versions of this task type in the neighbors, in addition to the characteristics of a task itself, including the required cache volume and processing resources, we rank the tasks using the proposed Multi-Criteria Task Ranking using Predicted requests (MCTRP) scheme and select the best replacement option in the cache of each Mobile User Equipment (MUE). The proposed scheme has proved to be of considerable benefit in terms of reducing delay and energy consumption and improving the utility of MUEs. [ABSTRACT FROM AUTHOR]

The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks. [ABSTRACT FROM AUTHOR]

Today, Mobile Cloud Computing (MCC) alone can no longer respond to the increasing volume of data and satisfy the necessary delays in real-time applications. In addition, challenges such as security, energy consumption, storage space, bandwidth, lack of mobility support, and lack of location awareness have made this problem more challenging. Expanding applications such as online gaming, Augmented Reality (AR), Virtual Reality (VR), metaverse, e-health, and the Internet of Things (IoT) have brought up new paradigms for processing big data. Some of the paradigms that have emerged in the last decade are trying to alleviate cloud computing problems jointly. Mobile Edge Computing (MEC) and Fog Computing (FC) are the most critical techniques that serve the IoT. One of the common points of the above paradigms is the offloading of IoT tasks. This paper reviews machine learning-based computation offloading mechanisms in the edge and fog environment. This review covers three significant areas of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discuss various performance metrics, tools, and case studies and analyze their advantages and disadvantages. We systematically elaborate on open issues and research challenges that are crucial for the next decade. [ABSTRACT FROM AUTHOR]

Due to the limitation of computing resources and storage resources, mobile edge computing cannot cope with the massive data generated by the Industrial Internet of Things (IIoT). However, traditional mobile cloud computing has rich computing resources. Therefore, through the construction of cloud computing and edge computing collaborative system, high bandwidth and low latency network services for the Internet of things can be provided. Based on Lyapunov optimization theory, the resource allocation and power consumption in cloud-edge collaborative system are investigated in this paper. Firstly, a cloud-edge collaboration architecture is proposed, then by establishing the dynamic queue model of cloud computing server and edge computing server, and combining with the system power function to form a drift plus penalty function framework, the problem is reduced to a constrained optimization problem. Finally, the offloading algorithm based on congestion is given. The simulation results show that the proposed optimization scheme can effectively reduce the overall power consumption and congestion of cloud-edge collaborative system. [ABSTRACT FROM AUTHOR]

As a new technology, Internet of Vehicles (IoV) needs high bandwidth and low delay. However, the current on-board mobile terminal equipment cannot meet the needs of the IoV. Therefore, using mobile edge computing (MEC) can solve the problems of energy consumption and time delay in the IoV. In the MEC, task offloading can solve the problem of resource constraint on mobile devices effectively, but it is not optimal to offload all tasks to edge servers. In this paper, the vehicle computation task is regarded as a directed acyclic graph (DAG), and task nodes' execution location and scheduling order are optimized. Considering the energy consumption and delay of the system, the vehicle computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then a Non-dominated Sorting Genetic Strategy(NSGS) is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay and energy consumption. Finally, a large number of experiments are carried to prove the performance of the algorithm. The experimental results show that the algorithm can make the optimal decision in practical applications. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a recent communication paradigm that is considered one of the primary functions of the fifth-generation cellular system (5G). AR is one of the ultra-reliable low latency communications since it requires a total delay of about 5 ms for AR applications. Since mobile devices are battery-operated and have many limitations in computing resources, i.e., processing, storage, and energy, computing tasks associated with mobile AR applications have to be offloaded to nearby devices with sufficient computing resources. To this end, this paper provides a system structure for mobile AR and web-based AR applications; based on the modified multi-level mobile edge computing system (MM-MEC). Furthermore, the required offloading algorithms for managing data offloading from mobile devices to edge servers are introduced. The algorithms are latency and energy-aware, as the selection of the executing device for the AR computing tasks achieves the highest energy and latency efficiency. The proposed structure and offloading techniques have been validated through simulation processes. The developed framework and the proposed offloading schemes have been simulated over a reliable environment for various simulation The suggested system and its offloading methods were simulated in a dependable environment for a number of simulation situations. [ABSTRACT FROM AUTHOR]

The developments in smartphones, high data rates, and substantial video data traffic have increased the burden on cellular networks. Consequently, this burden significantly affects the Quality of Experience of the cellular users leading to an increased network delay for the diverse video content requests. To accommodate the requests from different users with varying requirements, one of the promising solutions is to cache videos in the near vicinity of users and transcode them online. The online transcoding is performed at the edge level of the cellular network to minimize the network delay and use the bandwidth efficiently. However, the feasibility of online transcoding is significantly affected by various factors, such as the codecs, configurations of virtual machines, the cost incurred, and estimated time to complete the transcoding task, among other parameters. Although online transcoding is discussed in the literature adequately, few studies discuss the feasibility of online transcoding while considering all the aforementioned critical parameters. This study examined the effects of a diverse range of critical parameters on the feasibility of online transcoding. We performed extensive simulations on the local machine environment to study various possible factors affecting online transcoding in detail. We then transcoded the same videos on Amazon Elastic Cloud Computing (EC2) Virtual Machines (VMs) to further study realistic cloud settings with fine-tuned configurations. Our experiments show the superior performance of some codecs and the effects of machine configurations on transcoding tasks duration. We aim to provide a benchmark for practitioners and researchers considering online transcoding in real-time multimedia applications. [ABSTRACT FROM AUTHOR]