Joint Service Caching, Task Offloading, and Service Delivery in Mobile Edge Computing for Latency Critical UAV Networks

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

In the future mobile edge networks, the Internet of things (IoT) applications will be latency-sensitive and computationally intensive. Given the resource limitation of IoT devices, mobile edge computing (MEC) servers are critical to support the efficient processing of IoT tasks. Since MEC servers attached to the ground base stations are generally deployed in fixed locations and vulnerable to physical damage, the unmanned aerial vehicle (UAV) and satellite-assisted MEC framework has been proposed to leverage the flexibility of UAVs and the broad coverage of satellites. However, efficient utilization of the UAV/satellite resources is challenging for the static ground IoT devices because of the dynamic in terms of aerial and space network topology and IoT task arrival rates. To adapt to the changing environment and utilize the interaction among multiple UAVs, we propose a multi-agent deep deterministic policy gradient (MADDPG) framework to jointly optimize the traveling routes of multi-UAVs and the offloading decision of IoT devices. To minimize the processing cost in terms of task processing latency and energy consumption of IoT devices, cooperative UAVs can help find the optimal task offloading location for each IoT device. Simulation results show the proposed algorithm based on MADDPG can averagely decrease 20% of the above processing cost compared with the benchmark approach.

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) 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.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Due to the exponential expansion of the Internet of Things (IoT) technology, the increasing amount of data generated by numerous IoT devices has become a significant challenge for communication and computing networks. On the other hand, the concept of Mobile Edge Computing (MEC) presents a viable solution as it brings computing, storage, and networking capabilities in close proximity to the user, enabling the hosting of applications that require significant computation power and low latency right at the network’s edge. In addition, the maneuverability, cost-effectiveness, and ease of deployment make Unmanned Aerial Vehicles (UAVs) highly versatile wireless platforms. Capitalizing on the benefits of MEC and UAVs, this paper proposes a UAV-assisted MEC system that offloads services to reduce the computation burden in IoT. The objective is to maximize the task completion rate, considering factors such as user transmit power, task offloading rate, and UAV trajectory variables. To tackle this optimization problem, this paper devises a method based on the Deep Deterministic Policy Gradient (DDPG), an algorithm for continuous action spaces in deep reinforcement learning. The numerical simulations show the effectiveness of the proposed approach in comparison to other benchmarks, i.e., deep Q-network (DQN) and full offloading methods. In a simulation, the proposed DDPG method can achieve a fully optimized system with a 100% task success rate, while the DQN-based method achieves a lower task success rate of 95.93% and the full offloading method only obtains a task success rate of 82.73%.

Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users' quality-of-experience guaranteed.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Mobile Edge Computing (MEC) migrates the computing center to the network edge to provide computing services for Mobile Users (MUs). However, due to the limited capacity of MUs and insufficient types of services stored by edge servers, it brings great challenges to computation offloading and service caching in Internet of Things (IoT) network. In this paper, we investigate the joint optimization problem of computation offloading, resource allocation and service caching replacement in a collaborative MEC system to minimize the total cost of all MUs under the latency and resource constraints. Accordingly, we formulate the problem as a Mixed Integer Non-linear Programming (MINLP) problem. In order to solve this problem, the Deep Deterministic Policy Gradient (DDPG)-based algorithm is proposed. The simulation results show that the proposed algorithm is better than other benchmark schemes in different scenarios.

With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

With the diversification and personalization of mobile user demands, as well as the rapid development of distributed computing technology, leveraging mobile edge computing task offloading to provide users with convenient services has become a research focus. However, existing methods still face issues such as time consumption, high resource consumption, and data silos. Based on this perspective, we propose the federated Knowledge distillation based mobile Edge Computing task Offloading (KECO) method to achieve accurate task offloading and system energy saving. Through federated distillation learning, a teacher-student model is established, and complex and parameter-rich models are used as teachers to assist in the training of the student model. The teacher model transfers the knowledge and information it had learned to the student to enhance the generalization ability of the student. Finally, using the lightweight and flexible nature of the student model, it is deployed in a distributed system to implement the task offloading strategy in mobile edge computing. Experiments show that KECO performs well in large-scale task classification and allocation, and has efficient, reliable, and wide application potential.