Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing

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.

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

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.

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

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.

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.

The extensive growth in Industrial Internet of Things (IIoT) applications has tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms such as multiaccess edge computing (MEC), which facilitates a low latency and scalable solution for the execution of industrial workloads. However, continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a consolidated stochastic computation offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to the finite number of busy MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Furthermore, we provide the expression for conditional waiting time (CWT) and unconditional waiting time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations, it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems, thereby providing a low latency, and resource sensitive solution for the considered system.

In multi-access edge computing (MEC) systems, there are several local cache servers caching contents to satisfy the users’ requests, instead of letting the users download via the remote cloud server. In this paper, a content scheduling problem (CSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate cache servers, and thus all cells and their caching decisions must be jointly taken. We first prove that CSP is $\mathcal{N}\mathcal{P}$-hard, then we formulate CSP using integer linear programming. For problem solving, via a mathematical reformulation, we derive a column generation algorithm embedded into a rounding scheme. Our performance evaluation demonstrates that the solutions obtained are within 0.8% from global optimality.

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.

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.

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.

Mobility-aware task offloading in MEC with task migration and result caching

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

A compelling need for Real Time decision making with detection, diagnosis & resolution of key industry related parameters from Industrial assets enabled with Digital Twins in hyper connectivity with IoT & low latency requirements as a primary driver for digitization of private industry. Focus on Industrial Revolution (IR) 4.0 with industry Digital Transformation solution is architected on Connectivity, Command, Control, Compute and Cyber Secure enabled industrial process control operations (5C Solution). 5G technology is chosen for its inherent high bandwidth capability and low latency features enabled by Multi-access Edge Computing (MEC) and cloudification architecture with virtualization-based solution. Quality of Service (QoS) and Quality of Experience (QoE) metrics with high reliability and high availability architectures are chosen as critical features. 5G New Radio - Unlicensed (NR-U) spectrum considered for realizing dense Radio Access Network (RAN) solution with Inter-site Distances (ISD) varying from 500mtrs to 1km with Rx sensitivity of upto −95dBm to enable the connectivity to distributed oil & gas field assets. 5G coverage design of the network is done with focus on interference minimization, neighbor relation management, load balancing and parameter optimization. Converged Connectivity Network with Industrial Automation Bus (IAB) based architecture is proposed to be realized with Digital Communication network Gateway (DCG) covering the functions of Operational and Information Technology (OT/IT). Oil & Gas industry's 3Rs (Rigs, Reservoirs & Refineries) with varying bandwidths - 10kbps to 1Gbps is supported by 5G NR-U RAN with Adaptive Antenna System (AAS) and beamforming technology using 4×4 Multiple Input Multiple Output (MIMO), 40/80MHz channel Bandwidth, 256 DL-QAM (Quadrature Amplitude Modulation) and latency requirements from - 100msec to 1msec enabled by MEC are met with use cases of process sensors, drones, Automated Guided Vehicle (AGV), Industrial robot (IR), Augment Reality/Virtual Reality (AR/VR) and Mission Critical Services (MCS) with highest degree of built-in cyber security.

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.