Privacy-Preserving Task Offloading Strategies in MEC

Mobile edge computing (MEC) is an emerging paradigm that provides radio access networks with augmented resources to meet the requirements of Internet of Things (IoT) services. MEC allows IoT devices to offload delay sensitive and computation intensive tasks to edge clouds deployed at base stations (BSs). Offloading tasks to edge clouds can alleviate the computing and battery limitations of IoT devices. However, task offloading in MEC for IoT may face serious transmission latency and computation latency problems with massive number of IoT devices. Moreover, some edge clouds can be overloaded due to the spatially inhomogeneous distributions of IoT tasks. To solve these problems, we investigate the workload balancing problems to minimize the transmission latency and computation latency in task offloading process while considering the limited bandwidth resources of BSs and computation resources in edge clouds. We formulate the workload balancing problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamics and show that the game always achieves a Nash equilibrium (NE). We further propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show that our proposed workload balancing algorithms can achieve better performance than existing solutions.

In the Internet of Things (IoT) environment, offloading computational tasks to edge servers is a key technology for alleviating the resource bottlenecks of mobile devices (MDs). However, in high-concurrency task offloading scenarios, edge servers face the issue of low resource allocation efficiency. Existing resource allocation schemes typically aim to improve resource utilization by indiscriminately redistributing idle resources, but these approaches fail to address the diversity of task demands. Due to the non-monotonic relationship between the computational load and latency constraints of different tasks, tasks exhibit heterogeneous resource demands under heterogeneous workloads. Therefore, fixed resource allocation strategies are difficult to adapt to the changes in task characteristics, leading to some tasks failing to complete on time due to insufficient resources, which in turn violates latency constraints and degrades overall system performance. To address this issue, this paper proposes a Task-Requirement-Based Resource Adaptive Allocation Algorithm (TRRAA). First, we construct a mathematical model to evaluate task requirements and quantify the computational resource demands of tasks at different time steps. Then, the algorithm dynamically allocates idle resources based on the real-time demands of tasks, prioritizing resources for tasks that are expected to miss their time constraints, ensuring timely completion. Finally, experimental results show that, compared to existing methods, the TRRAA algorithm improves task completion rates by 10 % when handling latency-constrained tasks.

Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

With the advance of novel applications, it is harder to deal with computation-intensive tasks due to the resource-constrained devices. Task offloading in Mobile Edge Computing (MEC) can effectively deal with this problem. Most of the existing studies view the task as a whole and do not consider the partition of task, which may result in the increasing delay of task processing. First, we construct a model of fine-grained task scheduling problem for heterogeneous MEC and consider the task as a Directed Acyclic Graph (DAG). Second, to optimize the average delay, we propose a priority-based heuristic task scheduling algorithm to handle dependencies of subtasks and achieve reasonable scheduling schemes between tasks and heterogeneous servers. Moreover, we design an idle time slot insertion strategy to realize the full utilization of MEC resources. Finally, the topology of DAGs is generated for simulation based on practical applications. Experimental result indicates that the proposed HFGO-CI could efficiently achieve the goal of average system delay reduction on the basis of reasonable decisions.

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

Task offloading in mobile edge computing (MEC) becomes particularly challenging when dealing with complex, interdependent subtasks, especially under dynamic network conditions and resource constraints. This challenge is central to enabling intelligent and adaptive behavior in holographic counterparts, real-time virtual replicas of physical consumer Internet of Things (IoT) devices such as smart wearables, home automation units, or augmented reality systems. In this paper, we propose HiDeR-GQ, a novel Hierarchical Dependency Reduction framework that integrates quantized graph-based deep reinforcement learning (DRL) which acts as the decision-making core of holographic counterparts deployed in heterogeneous edge networks. The core of HiDeR-GQ is a customized deep reinforcement learning agent that integrates a quantized Graph Attention Network (GATv2) for structure-aware state encoding of task dependency graphs, and a personalized federated Double Deep Q-Network (DDQN) to enable collaborative learning across distributed edge nodes while accounting for heterogeneity. To further optimize performance, we introduce a latency-aware dependency pruning mechanism that hierarchically simplifies task graphs by removing non-critical dependencies to reduce end-to-end latency without compromising task accuracy. We evaluate HiDeR-GQ in a simulated MEC environment involving multiple edge servers and users executing directed acyclic graph (DAG) structured application tasks. The results demonstrate that HiDeR-GQ achieves up to 25% lower average task completion delay, higher cumulative reward, and reduced communication cost compared to state-of-the-art offloading strategies. These benefits come with minimal trade-offs in decision optimality, highlighting HiDeR-GQ’s efficiency and scalability for next-generation IoT and edge intelligence applications.

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

Multi-objective Optimization Approach to High-Performance Cloudlet Deployment and Task Offloading in Mobile Edge Computing

Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

Mobile-edge computing (MEC) has great advantages in reducing latency and energy consumption, where mobile devices (MDs) can offload their computation-demanding and latency-critical tasks. However, privacy leakage may occur during the tasks offloading process, and most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and device-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC to achieve a delay and energy sub-optimal solution while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semi-parametric contextual Multi-armed Bandit (MAB) problem, which has a relaxed reward model. Then, we propose a Privacy-Aware Online Task Offloading (PAOTO) algorithm based on the transformed Thompson-Sampling (TS) architecture, through which we can: 1) receive the best possible delay and energy consumption performance; 2) achieve the goal of preserving privacy; and 3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

Joint task offloading and data caching in mobile edge computing networks

Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues