A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Mobile edge computing in wireless communication networks: design and optimization

This dissertation studies the design and optimization of applying mobile edge computing (MEC) in three kinds of advanced wireless networks, which is motivated by three non-trivial but not thoroughly studied topics in the existing MEC-related literature. First, we study the application of MEC in wireless powered cooperation-assisted systems. The technology of wireless power transfer (WPT) used at the access point (AP) is capable of providing sustainable energy supply for resource-limited user equipment (UEs) to support computation offloading, but also introduces the double-near-far effect into wireless powered communication networks (WPCNs). By leveraging cooperation among near-far users, the system performance can be highly improved through effectively suppressing the double-near-far effect in WPCNs. Then, we consider the application of MEC in the unmanned aerial vehicle (UAV)-assisted relaying systems to make better use of the flexible features of UAV as well as its computing resources. The adopted UAV not only acts as an MEC server to help compute UEs' offloaded tasks but also a relay to forward UEs' offloaded tasks to the AP, thus such kind of cooperation between the UAV and the AP can take the advantages of both sides so as to improve the system performance. Last, heterogeneous cellular networks (HetNets) with the coexistence of MEC and central cloud computing (CCC) are studied to show the complementary and promotional effects between MEC and CCC. The small base stations (SBSs) empowered by edge clouds offer limited edge computing services for UEs, whereas the macro base station (MBS) provides high-performance CCC services for UEs via restricted multiple-input multiple-output (MIMO) backhauls to their associated SBSs. With further considering the case with massive MIMO backhauls, the system performance can be further improved while significantly reducing the computational complexity. In the aforementioned three advanced MEC systems, we mainly focus on minimizing the energy consumption of the systems subject to proper latency constraints, due to the fact that energy consumption and latency are regarded as two important metrics for measuring the performance of MEC-related works. Effective optimization algorithms are proposed to solve the corresponding energy minimization problems, which are further validated by numerical results

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

Data-Driven Network Management for Next-Generation Wireless Networks

With the commercialization and maturity of the fifth-generation (5G) wireless networks, the next-generation wireless network (NGWN) is envisioned to provide seamless connectivity for mobile user terminals (MUTs) and to support a wide range of new applications with stringent quality of service (QoS) requirements. In the NGWN, the network architecture will be highly heterogeneous due to the integration of terrestrial networks, satellite networks, and aerial networks formed by unmanned aerial vehicles (UAVs), and the network environment becomes highly dynamic because of the mobility of MUTs and the spatiotemporal variation of service demands. In order to provide high-quality services in such dynamic and heterogeneous networks, flexible, fine-grained, and adaptive network management will be essential. Recent advancements in deep learning (DL) and digital twins (DTs) have made it possible to enable data-driven solutions to support network management in the NGWN. DL methods can solve network management problems by leveraging data instead of explicit mathematical models, and DTs can facilitate DL methods by providing extensive data based on the full digital representations created for individual MUTs. Data-driven solutions that integrates DL and DT can address complicated network management problems and explore implicit network characteristics to adapt to dynamic network environments in the NGWN. However, the design of data-driven network management solutions in the NGWN meets several technical challenges: 1) how the NGWN can be configured to support multiple services with different spatiotemporal service demands while simultaneously satisfying their different QoS requirements; 2) how the multi-dimensional network resources are proactively reserved to support MUTs with different mobility patterns in a resource-efficient manner; and 3) how the heterogeneous NGWN components, including base stations (BSs), satellites, and UAVs, jointly coordinate their network resources to support dynamic service demands, etc. In this thesis, we develop efficient data-driven network management strategies in two stages, i.e., long-term network planning and real-time network operation, to address the above challenges in the NGWN. Firstly, we investigate planning-stage network configuration to satisfy different service requirements for communication services. We consider a two-tier network with one macro BS and multiple small BSs, which supports communication services with different spatiotemporal data traffic distributions. The objective is to maximize the energy efficiency of BSs by jointly configuring downlink transmission power and communication coverage for each BS. To achieve this objective, we first design a network planning scheme with flexible binary slice zooming, dual time-scale planning, and grid-based network planning. The scheme allows flexibility to differentiate the communication coverage and downlink transmission power of the same BS for different services while improving the temporal and spatial granularity of network planning. We formulate a combinatorial optimization problem in which communication coverage management and power control are mutually dependent. To solve the problem, we propose a data-driven method with two steps: 1) we propose an unsupervised-learning-assisted approach to determine the communication coverage of BSs; and 2) we derive a closed-form solution for power control. Secondly, we investigate planning-stage resource reservation for a compute-intensive service to support MUTs with different mobility patterns. The MUTs can offload their computing tasks to the computing servers deployed at the core networks, gateways, and BSs. Each computing server requires both computing and storage resources to execute computing tasks. The objective is to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from re-configuring resource reservation. To this end, we develop a data-driven network planning scheme with two elements, i.e., multi-resource reservation and resource reservation re-configuration. First, DTs are designed for collecting MUT status data, based on which MUTs are grouped according to their mobility patterns. Then, an optimization algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a meta-learning-based approach is proposed to re-configure resource reservation for balancing the network resource usage and the re-configuration cost. Thirdly, we investigate operation-stage computing resource allocation in a space-air-ground integrated network (SAGIN). A UAV is deployed to fly around MUTs and collect their computing tasks, while scheduling the collected computing tasks to be processed at the UAV locally or offloaded to the nearby BSs or the remote satellite. The energy budget of the UAV, intermittent connectivity between the UAV and BSs, and dynamic computing task arrival pose challenges in computing task scheduling. The objective is to design a real-time computing task scheduling policy for minimizing the delay of computing task offloading and processing in the SAGIN. To achieve the objective, we first formulate the on-line computing scheduling in the dynamic network environment as a constrained Markov decision process. Then, we develop a risk-sensitive reinforcement learning approach in which a risk value is used to represent energy consumption that exceeds the budget. By balancing the risk value and the reward from delay minimization, the UAV can explore the task scheduling policy to minimize task offloading and processing delay while satisfying the UAV energy constraint. Extensive simulation have been conducted to demonstrate that the proposed data-driven network management approach for the NGWN can achieve flexible BS configuration for multiple communication services, fine-grained multi-dimensional resource reservation for a compute-intensive service, and adaptive computing resource allocation in the dynamic SAGIN. The schemes developed in the thesis are valuable to the data-driven network planning and operation in the NGWN

Resource management for cost-effective cloud and edge systems

With the booming of Internet-based and cloud/edge computing applications and services,datacenters hosting these services have become ubiquitous in every sector of our economy which leads to tremendous research opportunities. Specifically, in cloud computing, all data are gathered and processed in centralized cloud datacenters whereas in edge computing, the frontier of data and services is pushed away from the centralized cloud to the edge of the network. By fusing edge computing with cloud computing, the Internet companies and end users can benefit from their respective merits, abundant computation and storage resources from cloud computing, and the data-gathering potential of edge computing. However, resource management in cloud and edge systems is complicated and challenging due to the large scale of cloud datacenters, diverse interconnected resource types, unpredictable generated workloads, and a range of performance objectives. It necessitates the systematic modeling of cloud and edge systems to achieve desired performance objectives.This dissertation presents a holistic system modeling and novel solution methodology to effectivelysolve the optimization problems formulated in three cloud and edge architectures: 1) cloud computing in colocation datacenters; 2) cloud computing in geographically distributed datacenters; 3) UAV-enabled mobile edge computing. First, we study resource management with the goal of overall cost minimization in the context of cloud computing systems. A cooperative game is formulated to model the scenario where a multi-tenant colocation datacenter collectively procures electricity in the wholesale electricity market. Then, a two-stage stochastic programming is formulated to model the scenario where geographically distributed datacenters dispatch workload and procure electricity in the multi-timescale electricity markets. Last, we extend our focus on joint task offloading and resource management with the goal of overall cost minimization in the context of edge computing systems, where edge nodes with computing capabilities are deployed in proximity to end users. A nonconvex optimization problem is formulated in the UAV-enabled mobile edge computing system with the goal of minimizing both energy consumption for computation and task offloading and system response delay. Furthermore, a novel hybrid algorithm that unifies differential evolution and successive convex approximation is proposed to efficiently solve the problem with improved performance.This dissertation addresses several fundamental issues related to resource management incloud and edge computing systems that will further in-depth investigations to improve costeffective performance. The advanced modeling and efficient algorithms developed in this research enable the system operator to make optimal and strategic decisions in resource allocation and task offloading for cost savings

Satellite-MEC Integration for 6G Internet of Things: Minimal Structures, Advances, and Prospects

The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings' to those of Internet-of-Things (IoT) devices'. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disastrous areas. However, satellite networks face the inherent challenges of low data rate and large latency, which may not support computation-intensive and delay-sensitive IoT applications. Mobile Edge Computing (MEC) is a burgeoning paradigm by extending cloud computing capabilities to the network edge. By utilizing MEC technologies, the resource-limited IoT devices can access abundant computation resources with low latency, which enables the highly demanding applications while meeting strict delay requirements. Therefore, an integration of satellite communications and MEC technologies is necessary to better enable 6G IoT. In this survey, we provide a holistic overview of satellite-MEC integration. We first discuss the main challenges of the integrated satellite-MEC network and propose three minimal integrating structures. For each minimal structure, we summarize the current advances in terms of their research topics, after which we discuss the lessons learned and future directions of the minimal structure. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated satellite-MEC network

Task number maximization offloading strategy seamlessly adapted to UAV scenario

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists