Multi-access Edge Computing System Research Articles

Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems

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.

Towards end-to-end application slicing in Multi-access Edge Computing systems: Architecture discussion and proof-of-concept

Network slicing is one of the most critical 5G pillars. It allows for sharing a 5G infrastructure among different tenants leading to improved service customisation and increased operators’ revenues. Concurrently, introducing the Multi-access Edge Computing (MEC) into 5G to support time-critical applications raises the need to integrate this distributed computing infrastructure to the 5G network slicing framework. Indeed, end-to-end latency guarantees require the end-to-end management of slice resources. For this purpose, after discussing the main gaps in the state-of-the-art with regards to such an objective, we propose a novel slicing architecture that enables the management and orchestration of slice segments that span over all the domains of an end-to-end application service, including the MEC. We also show how this general management architecture can be instantiated into a multi-tenant MEC infrastructure. A preliminary implementation of the proposed architecture focusing on the MEC domain is also provided, together with performance tests to validate the feasibility and efficacy of our design approach.

Efficient Offloading for Minimizing Task Computation Delay of NOMA-Based Multiaccess Edge Computing

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task’s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

Energy-Efficient Resource Allocation and Subchannel Assignment for NOMA-Enabled Multiaccess Edge Computing

In this article, we study an energy-efficient nonorthogonal multiple access (NOMA) enabled multiaccess edge computing (MEC) system with strict latency requirements. We aim to minimize the energy consumption of all users by optimizing the resource allocation (including power and computation resources) and subchannel assignment, subject to the given latency constraint. The formulated problem, however, is a nonconvex combinatorial optimization problem. Nevertheless, we decompose the problem into a resource allocation subproblem and a subchannel assignment subproblem, and then solve the two subproblems iteratively. On one hand, we investigate the hidden convexity of the resource allocation subproblem under the optimal conditions, and propose an efficient algorithm to optimally allocate the resources by dual decomposition methods. On the other hand, we formulate the subchannel assignment subproblem into an integer linear programming problem and strictly prove that the problem is nondeterministic polynomial-time hard. We then solve it optimally by branch-and-bound methods, which is shown to be efficient in extensive simulations. Moreover, through considerable simulation results, we show that our proposed algorithm helps greatly reduce users’ energy consumption when communication resources (e.g., bandwidth) are limited. Additionally, it is verified that NOMA outperforms orthogonal multiple access in multiuser latency-sensitive MEC systems.

Combined Communication and Computing Resource Scheduling in Sliced 5G Multi-Access Edge Computing Systems

The fifth generation (5G) cellular networks aim to deliver data with low/ultra-low latency to users and support diverse services which require two main types of resources: communication and computing resources. These motivated the development of the Multi-access Edge Computing (MEC) paradigm because it can execute tasks’ computation closer to users to reduce latency. To satisfy quality of services (QoS) requirements of different applications, both communication and computing resources in 5G MEC systems can be hard partitioned or soft partitioned among multiple network slices where each slice supports one or more services. A typical soft partition approach is using scheduling. Furthermore, packets in the multi-resource system can either be scheduled individually for each resource using a discrete resource scheduling approach or scheduled collectively only once by combined resource scheduling. In this paper, we propose a combined resource scheduler, called Extended Weighted Fair Queueing with Latency Constraint (EWFQ/LC), to schedule packets among network slices subject to system fairness and their latency constraints. By exploiting the virtual finish time feature inherent in weight fair queueing, EWFQ/LC schedules packets according to the fairness when their latency constraints can be met while according to these latency constraints when they are possibly violated. Based on simulations of realistic heterogeneous traffic scenarios, we show that EWFQ/LC reduces latency by as much as 73% compared to scheduling each resource discretely and maintains latency satisfaction ratio above 90%. More importantly, EWFQ/LC is able to accomplish these improvements with greater fairness in resource consumption especially under heavy traffic conditions, i.e., traffic with stricter latency constraints do not excessively over-consume resources of other traffic classes.

Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing

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.

Energy-Efficient Beamforming and Resource Allocation for Multi-Antenna MEC Systems

This paper studies an energy-efficient beamforming and resource allocation for multi-access edge computing (MEC) systems consisting of multi-antenna access points (APs) and single-antenna users. We consider maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy efficiency</i> (EE) of a MEC system, defined as the total computed bits per total energy consumption of the MEC system. To enhance the EE performance, we employ multiple antennas at APs to exploit multiplexing- or (receive) beamforming-gain in the uplink and perform download beamforming for transmitting computation results in the downlink. We consider both spatial-division multiple access (SDMA) based MEC system and time-division multiple access (TDMA) based MEC system and compare their EE performance. We formulate EE maximization problems for the SDMA-based and TDMA-based MEC systems, which are nonconvex and thus cannot be solved by standard convex optimization techniques. We first transform the problems by applying semidefinite relaxation (SDR). Then, we solve the relaxed problems for the SDMA-based MEC system and TDMA-based MEC system by using Dinkelbach method and difference-of-concave programming algorithm. We observe that the SDMA-based MEC system outperforms the TDMA-based MEC system in EE performance most of the cases. However, for the special case when i) only users’ energy consumption is counted (i.e., energy consumption of AP and MEC server is not considered) for EE, and ii) there is no minimum throughput requirement, we show that the TDMA-based MEC system outperforms the SDMA-based MEC system. Simulation results demonstrate that the proposed schemes significantly enhance the EE of MEC systems.

Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey

With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.

5G-Enabled MEC: A Distributed Traffic Steering for Seamless Service Migration of Internet of Vehicles

Multiaccess edge computing (MEC) is considered as a backbone for the 5G network. The successive MEC network combines the networking and computation at the edge of the network to achieve the Quality of Services (QoS) with ultralow latency. The devices with mobility feature, whether hand-held devices or vehicles move from one edge server (ES) location to another ES, creates a nonoptimal environment in the long run. To maintain QoS and avoid service disruptions, existing network topologies do not fulfill the requirement. Hence, a unique traffic steering with dynamic path selection is required for live service migration of time-sensitive applications. In this article, we are the first to introduce a distributed traffic steering through the differentiation of two different types of network elements (i.e., ESs and routers), in a large MEC system. Using this concept, we, for the first time, resolve the scalability problem of a large MEC network into a partitioned MEC network. The proposed framework bounds the path-finding procedure with a filter strategy based on the network distance to eliminate the excess of nonrelated network elements. With a decentralized framework for MEC, we propose matrix-based dynamic shortest path selection and matrix-based dynamic multipath searching algorithms for dynamic path selection under the proposed autonomous network boundary discovery and must connect node block benchmarks. Our proposed dynamic traffic steering system works under two most important metric measurements (time delay and available bandwidth). Experimental results validate the effectiveness of dynamic and adaptive path searching in a partitioned controlled MEC network that significantly outperforms the centralized approaches with 35%–70% efficiency in QoS.

Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach

Recently, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) has been introduced as a promising edge paradigm for the future space-aerial-terrestrial integrated communications. Due to the high maneuverability of UAVs, such a flexible paradigm can improve the communication and computation performance for multiple user equipments (UEs). In this paper, we consider the sum power minimization problem by jointly optimizing resource allocation, user association, and power control in an MEC system with multiple UAVs. Since the problem is nonconvex, we propose a centralized multi-agent reinforcement learning (MARL) algorithm to solve it. However, the centralized method ignores essential issues like distributed framework and privacy concern. We then propose a multi-agent federated reinforcement learning (MAFRL) algorithm in a semi-distributed framework. Meanwhile, we introduce the Gaussian differentials to protect the privacy of all UEs. Simulation results show that the semi-distributed MAFRL algorithm achieves close performances to the centralized MARL algorithm and significantly outperform the benchmark schemes. Moreover, the semi-distributed MAFRL algorithm costs 23<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> lower opeartion time than the centralized algorithm.

UAV-Assisted Multi-Access Edge Computing: Technologies and Challenges

As the number of end users served by wireless networks continues to grow, modern wireless communication systems have put forward higher requirements for the diversity of wireless network forms. The unmanned aerial vehicle (UAV) wireless network communication system will occupy an important position in the future wireless communication system. With the explosive growth of 5G data traffic and Internet of Things (IoT) devices, computing-intensive, communication-intensive, and delay-sensitive applications have put tremendous pressure on UAV communication systems. Unlike terrestrial communication networks, the inherent characteristics of UAVs make it more troublesome to deal with the above-mentioned difficulties. Multi-access edge computing (MEC) can help mobile devices improve computing and communication capabilities, and its combination with UAVs is also an open subject. This article mainly summarizes the related knowledge and research of MEC and UAV wireless communication systems. We combine the UAV system with MEC technology and try to propose a UAV-assisted MEC wireless communication system. We analyze the key methods to realize this technology and point out a series of possible challenges. This article aims to provide new ideas for the proposed new architecture of aerial computing.

How to Leverage Mobile Vehicles to Balance the Workload in Multi-Access Edge Computing Systems

In this correspondence, we investigate the joint relay and server selection problem for a novel vehicle-assisted multi-access edge computing (MEC) system, where vehicles on the road relay data from an end device to appropriate MEC servers for computing in a store-carry-forward manner. To harness the stochastic nature of task arrivals, vehicle availability, and resource availability at MEC servers, we design a relaying scheme to minimize the average task latency based on a Markov decision process. Our study shows that our proposed scheme is highly effective in balancing the communications and computing while minimizing the end-to-end latency for task completion.

Maximizing the latency fairness in UAV‐assisted MEC system

Unmanned aerial vehicles (UAV) assisted edge computing has risen as an assuring technique to accommodate ubiquitous edge computation for resource-limited devices. Thus, this paper proposes an approach to maximize the latency fairness in a UAV-assisted multi-access edge computing (MEC) system. To maximize latency fairness, the authors focus on minimizing the maximum latency experienced among the users. In here, multiple ground users (GUs) offload their tasks to MEC UAV in the absence or unavailability of ground servers due to a disaster or heavy traffic where an iterative algorithm is proposed to minimize the maximum latency among the users subject to minimum control link rate and total power constraints. Sequentially, the UAVs' 3D location, offloading ratio, GUs' transmit power and GUs' computational capacity are optimized. The location of the UAV is optimized by using the novel approach, guided pattern search algorithm while the altitude of the UAV is optimized by analyzing the elevation angle dependant behaviour of the channel gain. A simple approach is utilized for optimizing the offloading ratio of the users by considering the problem as minimizing the point-wise maximum of two convex functions while the bisection method is used to optimize the power allocation. Numerical simulation results illustrate that the proposed approach outperforms other baseline approaches in convergence, minimizing the maximum latency and maximizing and maintaining the fairness among the GUs. Furthermore, it is proved that the guided pattern search algorithm converges at least 3.5 times better while the proposed combined optimization gives 400% fairness gain, in comparison with the baseline approach.

Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce the maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0–1 integer linear programming problem, which is NP-complete and very difficult to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is then converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the matching problem. Simulation results are presented to show the effectiveness of our proposed algorithm, and the normalization factor is of great significance to obtain the lower total cost.

Energy-Efficient Parallel Multi-Access Edge Computing in OFDMA Wireless Networks

We study a parallel multi-access edge computing (MEC) system in this work and investigate its energy-efficient resources management. Particularly, the MEC system consists of an edge node and a number of mobile users, where each user has an inelastic computing task with nonnegligible duration of task execution and nonnegligible size of task result. In addition, the MEC system operates in orthogonal frequency division multiple access (OFDMA) wireless networks and can support the transmission as well as the execution of different tasks in parallel. On this basis, we build a nonconvex mixed-integer nonlinear programming (MINLP) problem aiming to minimize the energy consumed by the system. By using the McCormick method and exploring the problem's structural properties, we first equivalently transform it to a manageable convex MINLP problem, and then devise two low-complexity suboptimal algorithms. Finally, numerical results are presented to show the effectiveness of our proposed algorithms.

UAV-Relaying-Assisted Multi-Access Edge Computing With Multi-Antenna Base Station: Offloading and Scheduling Optimization

Multi-access edge computing (MEC) systems where the base station (BS) is equipped with multiple antennas are basic application systems in the future. Moreover, unmanned aerial vehicles (UAVs) can be used as relays for responding to emergency service situations in the systems above. Thus, in this work, we investigate a UAV-relaying-assisted MEC system with multi-antenna BS. The optimization for offloading and scheduling strategies should be considered for the energy consumption minimization. First, a circular deployment scheme is adopted to overcome the difficulty of UAVs’ location optimizations. Then, the original problem is established, which aims to minimize the weighted sum of transmission and hovering energy consumptions by jointly optimizing offloading and scheduling strategies. Moreover, the non-convex original problem is transformed into four sub-problems and their optimal solutions are obtained by proposed algorithms. Finally, an iterative algorithm is proposed to obtain a sub-optimal solution of the original problem. Simulation results show that the proposed algorithm outperforms the three benchmark algorithms in terms of the weighted sum and approaches the performance of the relaxed lower bound algorithm. Furthermore, simulation results demonstrate that appropriate number of UAVs and BS's antennas can achieve better energy-saving performance.

Modeling and Verification of Service Allocation Policies for Multi-Access Edge Computing Using Probabilistic Model Checking

In recent times, Multi-Access Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative guarantees on performance metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. Further, the implications of MEC server failures on allocation policies have been relatively unexplored. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use the San Francisco taxi dataset, the LDNS availability dataset and allocation policies from recent literature to validate our approach. Experimental results demonstrate how our model can be utilized to quantitatively compare performance metrics of service allocation policies in MEC systems.

Balancing Loads among MEC Servers by Task Redirection to Enhance the Resource Efficiency of MEC Systems

To improve the resource efficiency of multi-access edge computing (MEC) systems, it is important to distribute the imposed workload evenly among MEC servers (MECSs). To address this issue, we propose a task redirection method to balance loads among MECSs in a distributed manner. In conventional methods, a congested MECS selects only one MECS to which it redirects tasks. By contrast, the proposed method enables a congested MECS to distribute its tasks to a set of MECSs, the loads of which are lower than that of the congested MECS by determining the number of tasks that it redirects to each selected MECS. We prove that our task redirection method drives a MEC system to a state where the resulting MECS load vector is lexicographically minimal. Through extensive simulation studies, we show that compared with the conventional methods, the proposed method can achieve the smallest load difference between the load of the MECS, the load of which is the highest, and that of the MECS, the load of which is the smallest. By lexicographically minimizing the MECS load vector, the proposed method decreases the average task blocking rate when the task offload rate is high. In addition, we show that the proposed method outperforms the conventional methods in terms of the number of tasks, the delay requirements of which are not satisfied.

Optimal Pricing for Job Offloading in the MEC System With Two Priority Classes

Multi-Access edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Nevertheless, different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose a partial-knowledge based pricing mechanism where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”