A game incentive mechanism for energy efficient federated learning in computing power networks

Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay.

This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple network data analytics functions (NWDAFs) are embedded in the unmanned aerial vehicle (UAV)-aided MEC servers according to the framework standardized by the 3rd Generation Partnership Project (3GPP) specification. Here, UAVs can use federated learning (FL) with their part of local datasets, while the remaining local datasets can be offloaded to the centralized server for centralized learning (CL). To achieve such HFCL energy-efficiently for battery-constrained UAVs, we propose an energy-efficient computation offloading (ECO) scheme for the HFCL. We rigorously formulate analytical models for the overall energy consumption at MEC servers on UAVs and total latency for HFCL process based on the amount of offloaded datasets at each UAV. Here the energy consumption includes i) the transmission energy consumption for offloading the local dataset and ii) the energy consumption for FL processing, which includes local training and local model uploading. To balance between the overall energy consumption and total latency during HFCL process while preventing overload at UAVs, we propose a theoretical framework for the ECO problem that optimizes the amount of offloaded local datasets over multiple UAVs on aerial networks. Here, the ECO problem is formulated as the convex optimization problem under the continuous domain of the amount of offloaded datasets. Our numerical and simulation results show that our proposed framework can construct wireless traffic prediction models with an acceptable training accuracy in an energy-efficient manner over various benchmarks by striking a balance between energy consumption and overall training latency.

With modern Electric Connected Vehicles (ECVs) becoming more intelligent and entertaining, the Multi-access Edge Computing (MEC) servers deployed near the Road Side Units (RSUs) have been expected to not only improve the computing performance, but also alleviate the ECVs' battery burden. However, the uneven spatial and temporal distribution of vehicle arrivals cause that the MEC servers in busy areas are overloaded, which results in degraded computation performance and limited energy alleviation for ECVs. In this paper, we consider the task distribution among nearby MEC servers and propose a novel partial offloading strategy where multiple blocks of the tasks are cooperatively processed by MEC servers in the vehicles' moving directions. After formulating the mathematical model to jointly optimize the computing latency and energy consumption of ECVs, a Deep Reinforcement Learning (DRL) based task partial offloading strategy is proposed and a Deep Q-Network (DQN) is adopted to make the offloading decision. Simulation results illustrate the significant alleviation of energy consumption and improvement of computation performance compared with conventional methods.

Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.

The offloading policy has attracted much attention in the study of Device-to-Device (D2D) Communication underlaying cellular network integrated with Mobile Edge Computing (MEC) service. D2D communication can improve energy efficiency and prolong the battery life of user equipment (UEs) by offloading computation tasks from a UE to a nearby UE via a D2D link. How to coordinate UEs to offload tasks in D2D underlay communication integrated with MEC Service for reducing interference and improving network energy efficiency is a very important subject. In this paper, we study a D2D underlay communication integrated with MEC service system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in local region, (iii) offloading tasks to a nearby UE. Furthermore, we formulate the computation offloading problem as a potential game and propose a D2D communication integrated with MEC service offloading computation algorithm for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system energy consumption under time constraints.

The fifth generation (5G) and beyond wireless networks are envisioned to provide an integrated communication and computing platform that will enable multipurpose and intelligent networks driven by a growing demand for both traditional end users and industry verticals. This evolution will be realized by innovations in both core and access capabilities, mainly from virtualization technologies and ultra-dense networks, e.g., software-defined networking (SDN), network slicing, network function virtualization (NFV), multi-access edge computing (MEC), terahertz (THz) communications, etc. However, those technologies require increased complexity of resource management and large configurations of network slices. In this new milieu, with the help of artificial intelligence (AI), network operators will strive to enable AI-empowered network management by automating radio and computing resource management and orchestration processes in a data-driven manner. In this regard, most of the previous AI-empowered network management approaches adopt a traditional centralized training paradigm where diverse training data generated at network functions over distributed base stations associated with MEC servers are transferred to a central training server. On the other hand, to exploit distributed and parallel processing capabilities of distributed network entities in a fast and secure manner, federated learning (FL) has emerged as a distributed AI approach that can enable many AI-empowered network management approaches by allowing for AI training at distributed network entities without the need for data transmission to a centralized server. This article comprehensively surveys the field of FL-empowered mobile network management for 5G and beyond networks from access to the core. Specifically, we begin with an introduction to the state-of-the-art of FL by exploring and analyzing recent advances in FL in general. Then, we provide an extensive survey of AI-empowered network management, including background on 5G network functions, mobile traffic prediction, and core/access network management regarding standardization and research activities. We then present an extensive survey of FL-empowered network management by highlighting how FL is adopted in AI-empowered network management. Important lessons learned from this review of AI and FL-empowered network management are also provided. Finally, we complement this survey by discussing open issues and possible directions for future research in this important emerging area.

Various real-time, latency-sensitive, and high-speed mobile applications are evolving as 5G applications. These applications are realized using Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technologies in the 5G system. MEC platform, MEC service, and MEC application are the main components of an MEC Framework. NFV Orchestrator, Virtualized Infrastructure Manager (VIM), and virtualization technologies such as Virtual Machines (VM) and Containers are the main pillars of the NFV technology. In this paper, we study the impact of the virtualization technologies in the deployment of the MEC framework and its components while. We also study the impact of virtualization technologies on NFV and MEC KPIs such as onboarding time, instantiation time, MEC service and application response times. The experiments and its analysis show that containers perform better than VM to instantiate/terminate MEC components in the NFV framework. The observed MEC service KPIs show that the edge application’s performance will be improved to meet the QoE of the applications irrespective of the virtualization technology used. These results can be used as a reference while deploying the MEC components based on their granular functionalities.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

The deployment of fifth-generation (5G) and beyond 5G (B5G) networks is the ambitious objective of modern research on future mobile networks that are evolving to support computation-intensive and communication-sensitive applications. Such applications (e.g., autonomous vehicles, industrial automation, and remote surgery) impose diverse quality-of-service (QoS) requirements on the network in terms of processing, latency, reliability, and bandwidth, and will require ultra-reliable low-latency communication (URLLC), paving the way for multi-access edge computing (MEC). Our work considers a dynamic indoor B5G network in a robotic scenario where agents continuously need MEC services and migrate from one cell to another to perform their tasks in an ultra-dense cell environment. Assuming that every MEC service is a virtual machine (VM) to execute in one of the cells with the possibility of migrating the VM to another cell by paying some cost, we formalize the joint problem of (1) placing/migrating the VMs to respect their end-to-end communication latency requirements and (2) allocating their computation and communication bandwidth as a mixed-integer linear program (MILP). An MILP solver is then used to find the optimal VM placements/migrations and bandwidth allocations over a time horizon.Keywords5GMECMILPURLLCReal-time

Optimal resource management for multi-access edge computing without using cross-layer communication

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

Cross-silo federated learning (FL) is a privacypreserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data. Recently, the cross-silo FL in multi-access edge computing (MEC) is used in increasing industrial applications. Most existing research on cross-silo FL pays attention to the performance aspect, ignoring the incentive mechanism for high-quality client selection and long participation in model training for efficient and stable FL, which has prevented the widespread adoption of cross-silo FL in MEC. In this paper, we propose an incentiVe mechanism with QuAlity-Aware and Reputation-Aware based on the infinitely repeated game for cross-silo FL named VARF. VARF selects high-quality and highreputation edge nodes (ENs) as candidates for model training in the cross-silo FL by a heuristic algorithm and then motivates the selected ENs to actively contribute their resources. VARF also models the long-term behavior of ENs in cross-silo FL as an infinitely repeated game and derives a stable and long-term cooperative strategy for clients while maximizing the amount of local data for model learning in cross-silo FL. Extensive simulations with real-world datasets demonstrate that the performance of VARF is more beneficial than other benchmarks. Meanwhile, experimental results show that cloud platforms (CPs) and ENs eventually form a long and stable cooperative relationship under the trigger strategy.

Multi-access edge computing (MEC) promises to deliver localized computing power and storage. Coupled with low-latency 5G radio access, this enables the creation of high added-value services for mobile users, such as in-vehicle infotainment or remote driving. The performance of these services as well as their scalability will however depend on how MEC will be deployed in 5G systems. This paper evaluates different MEC deployment options, coherent with the respective 5G migration phases, using an accurate and comprehensive end-to-end (E2E) system simulation model (exploiting Simu5G for radio access and Intel CoFluent for core network and MEC), taking into account user-related metrics, such as response time or MEC latency. Our results show that 4G radio access is going to be a bottleneck, preventing MEC services from scaling up. On the other hand, the introduction of 5G will allow a considerable higher penetration of MEC services.

Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS’s energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.

In industrial production, mass data transmission and processing are required with an increase in production data, which causes large-scale and complicated wiring in the manufacturing industry. However, it is difficult to achieve improved performance through traditional wireless communication technologies. 5G, a leading communication technology, with its Ultra-Reliable Low Latency Communication (uRLLC) combined with Multi-access Edge Computing (MEC), provides huge potential for the service with requirements for higher bandwidth, lower latency, and more computation. The Local Area Network type (LAN-type) Service supported by the 5G system reduces the net distance, which further decreases latency and provides data security. Based on this, in this paper, a 5G MEC gateway system is proposed to realize local wireless communication in a factory. The gateway system includes an intranet Address Resolution Mechanism, which is designed to resolve User Equipment IP addresses, a MEC server, which acts as a switch for packets broadcasting, and network tunnels, which are established between gateways to transmit data in the 5G LAN. In addition, the implemented testbed includes Raspberry Pi, which acts as the gateway, and the OpenVPN server, which runs in the MEC. Finally, two cases are shown to transfer Profinet and OPC UA data with this system.