Mobile Edge Computing (MEC) is a technology that shows great promise in enhancing the computational power of smart devices (SDs) in the Internet of Things (IoT). However, the fixed location and limited coverage of MEC servers constrain their performance. To overcome this issue, this paper explores a multiple unmanned aerial vehicle (UAV) assisted MEC system. The proposed system considers a scenario where multiple UAVs work together to provide computing services while dynamically adjusting their frequency based on the task size, under the constraint of limited energy. This paper aims to maximize computation bits, SDs’ fairness, and UAVs’ load balancing in multi-UAV assisted MEC system by jointly optimizing the trajectory and frequency. To address this challenge, we model it as a Partially Observable Markov Decision Process and propose a joint optimization strategy based on multi-agent deep reinforcement learning. The effectiveness of the proposed strategy is evaluated on both synthetic and realistic datasets. The results demonstrate that our strategy outperforms other benchmark strategies.

Mobile edge computing has been widely used in the Internet of Things (IoT) field. In mobile edge computing, users’ tasks can be executed on local devices or offloaded to edge servers for processing. However, in the multi-edge server collaboration scenario, service coverage of multiple edge servers may overlap with each other. Therefore, users in the overlapped service coverage areas need to determine which server to be offloaded. Inefficient task offloading strategies may result in unbalanced workloads of the edge servers, which causes negative impacts on task completion latency and the total number of served users. To address this problem, we propose a task offloading and resource allocation strategy in a multi-edge server collaboration scenario in this paper. In more detail, we consider that task demands arrive dynamically and tasks can be processed locally or offloaded to edge servers. We then model the task offloading and resource allocation problem as a partially observable Markov game (POMG) and propose a task offloading and resource allocation strategy based on a reinforcement learning algorithm I-PDQN to ensure that the task latency requirements are met while maximizing the number of served users and minimizing the average task energy consumption. Finally, we evaluate the performance of the strategy proposed in this paper against some typical benchmark strategies under different system parameters through experiments. The experimental results show that the I-PDQN-based task offloading and resource allocation strategy can outperform benchmark approaches.

The rapid development of mobile device applications put tremendous pressure on edge nodes with limited computing capabilities, which may cause poor user experience. To solve this problem, collaborative cloud-edge computing is proposed. In the cloud-edge computing, an edge node with limited local resources can rent more resources from a cloud node. According to the nature of cloud service, cloud service can be divided into private cloud and public cloud. In a private cloud environment, the edge node must allocate resources between the cloud node and the edge node. In a public cloud environment, since public cloud service providers offer various pricing modes for users’ different computing demands, the edge node also must select the appropriate pricing mode of cloud service; which is a sequential decision problem. In this stydy, we model it as a Markov decision process and parameterized action Markov decision process, and we propose a resource allocation algorithm cost efficient resource allocation with private cloud (CERAI) and cost efficient resource allocation with public cloud (CERAU) in the collaborative cloud-edge environment based on the deep reinforcement learning algorithm deep deterministic policy gradient and P-DQN. Next, we evaluated CERAI and CERAU against three typical resource allocation algorithms based on synthetic and real data of Google datasets. The experimental results demonstrate that CERAI and CERAU can effectively reduce the long-term operating cost of collaborative cloud-side computing in various demanding settings. Our analysis can provide some useful insights for enterprises to design the resource allocation strategy in the collaborative cloud-side computing system.

Nowadays, Mobile Edge Computing (MEC) has been widely deployed to enhance the computational capabilities of mobile devices. However, the geographic location of MEC servers is usually fixed. In order to provide flexible edge computing services, some works have considered integrating Unmanned Aerial Vehicles (UAVs) into MEC networks. In the context of UAV-assisted edge computing, there usually exist multiple UAVs and users, and each UAV may aim to maximize its profit by providing computing services, while users will decide which UAVs to utilize based on their preferences. In this context, how UAVs and users effectively plan their trajectories becomes particularly important as it will affect the profitability of UAVs and the user experience. Since the trajectories of UAVs and users are affected by each other, we model the trajectories of UAVs and users as a Stackelberg game, and then design trajectory planning strategies for users and UAVs based on Independent Proximal Policy Optimization (IPPO) and Proximal Policy Optimization (PPO) respectively, aiming to maximize UAVs’ profits while ensuring user acceptance of UAV services. Finally, we evaluate the proposed trajectory planning strategy against three typical benchmark strategies using synthetic and realistic datasets. The experimental results demonstrate that our strategy can outperform benchmark strategies in terms of UAV profit while guaranteeing users’ service experience.