Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems

Abstract

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

“…Therefore, UAV deployment has become a key discussion issue in academia. In particular, a good UAV deployment can improve the quality and efficiency of task offloading for users and enhance the performance of the system [33,34]. Xu et al [35] proposed a sky-air-ground integrated mobile edge computing system to provide high-quality computing services in areas with missing or damaged communication infrastructures and designed an algorithm based on particle swarm optimization and greedy strategies to obtain a near-optimal solution for UAV deployment and mission offloading in the system.…”

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

“…Machine learning-based task allocation [28] DQN-D EC [54] IoT [38] MARL IoT [42] ACO IoT, 5G [51] FLOM-Opt [53] Q-Learning IoT, IoV [57] MA IoT [58] Heuristic, RL EC Quality of service task allocation [26] EC [32] MMAS EC [48] QT EC, IoT Resource-aware task allocation [76] MAPPO MEC [62] MDP EC [14] MAP VEC [75] JTORA VEC [72] JTORA MEC [74] JTORA MEC [15] DNF [16] MARL MEC [21] JTORA NOMA-MEC [37] EC [39] ELB [40] Knapsack MCS [70] EC, 5G…”

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

“…Chen et al [7] presented a UT-UDN system model that demonstrated a 20% reduction in time delay and a 30% decrease in energy costs, as indicated by simulation results. Elgendy et al [8] proposed a Mobile Edge Computing solution for Unmanned Aerial Vehicles (UAVs), which uses a multi-layer resource allocation scheme, a load balancing algorithm, and integer programming to achieve cost reduction. In the context of multi-cloud spatial crowdsourcing data placement, Wang et al [9] introduced a data placement strategy with a focus on cost-effectiveness and minimal latency.…”