Heterogeneous Mobile Edge Computing Research Articles

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Dynamic offloading technique for real-time edge-to-cloud computing in heterogeneous MEC–MCC and IoT devices

In the realm of Mobile Cloud Computing (MCC), where mobile environments converge with cloud capabilities to address challenges related to performance, environmental considerations, and security, the complexity of task offloading intensifies with the growing number of cloud providers and application units. While Mobile Edge Computing (MEC) shows promise in enhancing MCC performance, the central research challenge revolves around efficiently determining when to offload computational tasks to the cloud or handle them locally at the edge. It involves finding the optimal balance between utilizing cloud resources and enhancing edge computing performance. This study aims to streamline the decision-making process, seeking a harmonious balance between optimal cloud resource utilization and improved edge computing performance. To this end, we introduce HECOM (Hybrid Edge-to-Cloud Offloading for Heterogeneous Computing), a strategic model that combines three decision-making algorithms and an efficient hybrid computation offloading method. HECOM swiftly identifies optimal offloading servers for individual application units, addressing the challenge of efficient offloading decision-making. Importantly, HECOM provides optimal solutions without waiting for all training processes to conclude. Evaluation results from experiments and simulations affirm that the proposed model exhibits impressive energy efficiency, reducing consumption by 25%, increasing offloading ratio by 30%, and minimizing latency and time delay by 15%, making it ideal for real-time applications. It significantly enhances network resource allocation and achieves a more balanced overall performance profile.

Cost-Effective Scheduling for Dependent Tasks With Tight Deadline Constraints in Mobile Edge Computing

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task’s future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.

Layer Dependency-Aware Learning Scheduling Algorithms for Containers in Mobile Edge Computing

Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

Energy-Efficient Task Scheduling Based on Traffic Mapping in Heterogeneous Mobile-Edge Computing: A Green IoT Perspective

As a key enabler of emerging 6G applications, mobile edge computing (MEC) improves the system performance by coordinating the computation resources between the mobile devices and edge. However, the energy consumption and carbon emission are boosted as the device number increases. This paper constructs a hierarchical network-assisted task offloading architecture for Green Internet of Things (IoT). Then the authors propose an energy-efficient scheduling based on traffic mapping with energy slack-space proportional pre-allocation algorithm (ESTMP) in the heterogeneous edge computing system to minimize the schedule length under certain energy constraints. The proposed algorithm is suitable for delay-sensitive mobile applications such as Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR), which process massive multi-modal data. Simulation results demonstrate the significant advantages of the algorithm in terms of the schedule length of arriving parallel tasks, resource utilization and energy consumption.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

Resource Allocation and Computation Offloading for Wireless Powered Mobile Edge Computing.

In this paper, we investigate a resource allocation and computation offloading problem in a heterogeneous mobile edge computing (MEC) system. In the considered system, a wireless power transfer (WPT) base station (BS) with an MEC sever is able to deliver wireless energy to the mobile devices (MDs), and the MDs can utilize the harvested energy for local computing or task offloading to the WPT BS or a Macro BS (MBS) with a stronger computing server. In particular, we consider that the WPT BS can utilize full- or half-duplex wireless energy transmission mode to empower the MDs. The aim of this work focuses on optimizing the offloading decision, full/half-duplex energy harvesting mode and energy harvesting (EH) time allocation with the objective of minimizing the energy consumption of the MDs. As the formulate problem has a non-convex mixed integer programming structure, we use the quadratically constrained quadratic program (QCQP) and semi-definite relaxation (SDR) methods to solve it. The simulation results demonstrate the effectiveness of the proposed scheme.

Joint computing, communication and cost-aware task offloading in D2D-enabled Het-MEC

Due to the exploding traffic demands and the diversity of novel applications requiring extensive computation and radio resources, research has been active to devise mechanisms for responding to these challenges. Mobile edge computing (MEC) and device-to-device (D2D) computation task offloading are expected to play a major role in serving devices with limited capabilities, and thus enhance system performance. In this work, we propose a joint computing, communication and cost-aware task offloading optimization problem aiming at maximizing the number of completed tasks, while minimizing energy consumption and monetary cost in D2D-enabled heterogeneous MEC networks. Our proposed scheme allows partial offloading where a requester mobile terminal offloads different parts of its data task simultaneously to multiple peer mobile terminals (MTs), edge servers and cloud. We formulate and solve the optimal allocation strategy then decompose the problem into two sub-problems in an attempt to reduce its complexity. Furthermore, we propose a low-complexity algorithm that generates high performance results and can be applied for large-scale networks. Compared to conventional and state-of-the-art system models, results show the effectiveness of the proposed schemes and provide useful insights into the tradeoffs between the number of completed tasks, energy consumption and monetary cost.

Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks

This paper explores the problem of joint computational offloading and content caching (OCP) in the wireless heterogeneous mobile edge computing (MEC) system, where each small-cell base station (BS) is equipped with the MEC server having the content caching/processing capabilities. The communication and computing resources of the system are allocated to users requesting or offloading their contents via the BSs to minimize the system-wide computational overhead. Due to the non-deterministic polynomial time hardness of the OCP, it is difficult to solve it with an exact integer-programming (IP) method. Instead, the problem is solved by adopting the Hopfield neural network (HNN) based approach. In particular, the HNN model representing the OCP is constructed. The global energy minimum of the model corresponds to a solution of the OCP. However, because of the negativity of diagonal weights, the convergence of this model to a stable state cannot be guaranteed. Subsequently, a range of “synthetic” HNN models with the global convergence property is developed to replace the original HNN. Based on these models, three different search algorithms are formulated and implemented in a long-term evolution advanced network. The algorithms demonstrate an improved performance when compared to other relevant IP methods in simulations.

Latency minimization for the MEC-aided vehicular network under Doppler Effect

In this paper, we consider a heterogeneous Mobile Edge Computing (MEC)-aided multi-layer vehicular network consisting of multiple vehicles as Edge Device (EDs), a road side unit (RSU), and a cloud center (CC). The raw data produced by the vehicles can be processed at the three nodes separately. EDs transmit the data to the RSU via wireless communication link, and a wired link is considered between the RSU and the CC. Taking the mobility of vehicles and the limited amount of resources into account, we jointly discuss the task offloading and resource allocation in this three-layer MEC-aided vehicular network. Specifically, the interactions of Doppler Effect and the bandwidth provided by RSU to different vehicles are investigated. Aiming at minimizing the latency while increasing the utilization of resource, we design an iterative task assignment and resource allocation (ITR) algorithm. The simulation results indicate that: 1) The average latency of all tasks is significantly decreased, and the optimal bandwidth allocation scheme to the EDs is achieved; 2) Due to the Doppler Effect of vehicles, it is important to consider both the mobility of vehicles and the length of communication links when allocating resource.

Computation Offloading in Heterogeneous Mobile Edge Computing With Energy Harvesting

Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. Computation offloading, which leverages powerful MEC servers (MEC-ss) to augment the computing capability of less powerful mobile devices (MDs), is intrinsically a distributed computing over heterogeneous MEC networks. In this article, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs' computation generation and MEC-ss' computation task receiving. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of an MD are taken into consideration. A noncooperative computation offloading game is formulated. The action is a vector that denotes the amount of computation tasks offloaded to all MEC-ss (the element value can be zero) and local process. We give the definition and existence analysis of the Nash equilibrium (NE). Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. We can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive NE-orienting iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and effectiveness of the proposed algorithm.

Data-driven Trust Prediction in Mobile Edge Computing-based IoT Systems

We propose a data-driven distributed machine learning approach to scalably predict the trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing (MEC)-based IoT systems. The proposed approach formulates training distributed trust prediction models within an MEC-based IoT system as a Network Lasso problem. We then introduce a variant of Stochastic Alternating Method of Multipliers framework (S-ADMM) enriched with the ability for feature selection at each MEC layer. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on three real-world datasets adjusted to exhibit the context-dependent trust information accumulated in MEC environments within a given MEC topology. The experimental results affirmed the effectiveness of our approach and its suitability to predict trustworthiness of IoT services in MEC-based IoT systems.

AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions

By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for next generation networks. However, current fixed-lo-cation MEC architecture may not be able to make real-time decision in dynamic environments, especially in large-scale scenarios. To address this issue, in this article, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, i.e., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions, are discussed. In addition, the AI-based joint Resource schEduling (ARE) framework with two different AI-based mechanisms, i.e., Deep neural network (DNN)-based and deep reinforcement learning (DRL)-based architectures, are proposed. DNN-based solution with online incremental learning applies the global optimizer and therefore has better performance than the DRL-based architecture with online policy updating, but requires longer training time. The simulation results are given to verify the efficiency of our proposed ARE framework.

Joint Task Scheduling and Energy Management for Heterogeneous Mobile Edge Computing With Hybrid Energy Supply

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

Distributed Blockchain-Based Trusted Multidomain Collaboration for Mobile Edge Computing in 5G and Beyond

Mobile edge computing (MEC) sinks computing power to the edge of networks and integrates mobile access networks and Internet services in 5G and beyond. With the continuous development of services, privacy protection is extremely important in a heterogeneous MEC system for multiserver collaboration. However, most of the existing schemes only consider the privacy of users or services other than the privacy of network topology. For the purpose of topology privacy protection, this article employs blockchain to construct heterogeneous MEC systems and adopts accommodative bloom filter as a carrier for multidomain collaborative routing consensus without exposing topology privacy. Blockchain is used to implement multiplex mutual trust networking and collaborative routing verification through the membership service and consensus mechanism. Experiments are conducted to evaluate the feasibility and performances of our scheme. The results indicate that the proposed scheme can highly improve the credibility and efficiency of MEC collaboration.

Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning

With the maturity of 5G technology and the popularity of intelligent terminal devices, the traditional cloud computing service model cannot deal with the explosive growth of business data quickly. Therefore, the purpose of mobile edge computing (MEC) is to effectively solve problems such as latency and network load. In this paper, deep reinforcement learning (DRL) is first proposed to solve the offloading problem of multiple service nodes for the cluster and multiple dependencies for mobile tasks in large-scale heterogeneous MEC. Then the paper uses the LSTM network layer and the candidate network set to improve the DQN algorithm in combination with the actual environment of the MEC. Finally, the task offloading problem is simulated by using iFogSim and Google Cluster Trace. The simulation results show that the offloading strategy based on the improved IDRQN algorithm has better performance in energy consumption, load balancing, latency and average execution time than other algorithms.

Task Scheduling in Deadline-Aware Mobile Edge Computing Systems

Mobile edge computing (MEC) is a new computing approach in which computation tasks carried by mobile devices (MDs) can be offloaded to MEC servers or computed locally. Since the MDs are always battery limited and computation tasks have strict deadlines, how to schedule the execution of each task energy effectively is important. Comparing with existing works, we consider a much more complexed scenario, in which multiple moving MDs sharing multiple heterogeneous MEC servers, and a problem named as minimum energy consumption problem in deadline-aware MEC system is formulated. Such problem is proved to be NP-hard, and two approximation algorithms are proposed focusing on single and multiple MD scenarios, respectively. The performances of these algorithms are varied by theoretical analysis and simulations.