With the rapid development of application containerization, Kubernetes has become the mainstream container orchestration system. However, the Kubernetes default scheduler uses static scheduling strategies. It did not take into account the possibility of inconsistency between the resource utilization of Pod during runtime and its own resource request claim, leading to a large amount of resource fragmentation on actual cluster nodes. In specific cases, unlike normal workload fluctuations, we have observed “surge” phenomena where an application usually only uses a small amount of resources for a long period of time but exhausts all resources in a short period of time. This could lead to service disruptions. For this application scenario, we have proposed a Kubernetes scheduling algorithm, KS-surge. It not only ensures the load balancing of system resources but also can effectively handle sudden situations such as a surge in task requests. The experimental results show that, compared to the default Kubernetes resource scheduler, our proposed algorithm improves the load balancing of the clusters while meeting the requirements of applications.

Aiming at the problem of task offloading in multiaccess edge computing (MEC) scenarios, this paper proposes a meta-reinforcement learning (Meta-RL)-based computational task offloading method. The algorithm adopts a two-layer architecture: the inner layer models the task offloading process as a Markov Decision Process (MDP), designs a reward function based on task latency and energy consumption, and designs a task offloading algorithm based on Proximal Policy Optimization (PPO) to make offloading decisions for each task in a single scenario and optimize the offloading performance within the scenario. The outer layer introduces the Meta-RL mechanism to optimize the initial parameters of the inner-layer neural network and learns multiple MDPs based on gradient descent to generate neural network parameters that can be applied to the intelligence of each scenario, so that the proposed algorithm can adapt to the offloading scenarios quickly. The proposed algorithm can quickly adapt to each offloading scenario. Simulation results show that the proposed algorithm improves the average cost by 14.7 % and 20.51 % compared with PPO and DDPG.

As part of airport behavior decisions, the accurate prediction of airport delay is highly significant in optimizing flight takeoff and landing sequences. However, the combination of various influencing factors affects airport delay prediction strongly, which would bring severe challenges in prediction. This paper introduces the sequence-to-sequence network and proposes a multi-step regression prediction method for the airport delay (DA-BILSTM) to accurately predict the airport delay. Rather than only considering a single kind of airport delay influencing factors, we design an attention fusion network for learning the sequence and condition correlation features adaptively. Moreover, the Bayesian optimization algorithm is introduced to optimize DA-BILSTM’s hyperparameters. The method is applied individually to two datasets for predicting the airport’s delays. The experiment results show that the prediction performance of DA-BILSTM is better than many state-of-the-art methods including the autoregressive integrated moving average model (ARIMA), long short-term memory (LSTM), gated recurrent unit(GRU), CNN-BILSTM, and TS-LSTM. When using DA-BILSTM in the two datasets, the average MAE of airport delay prediction in the next 5 hours is about 10 minutes, and the average RMSE is 20 minutes.

Mobile Edge Computing (MEC) can significantly save the energy consumption of small-cell base stations(SBSs) by using Dynamic Voltage Scaling (DVS) technology. In this paper, we study how to reduce energy consumption of SBSs by using DVS technology. We propose a scheme to divide base station groups in wireless MEC network, and SBSs in the same base station group collaboratively process tasks. For cooperating tasks in a base station group, we propose a task-offloading strategy, which can effectively reduce the energy consumption of the base station groups. We use Task-offloading Decision Algorithm (TDA) to decide the number of task fragments allocated to each SBSs in the base station group. For processing task fragments in the task queue of SBSs, we propose a computing resource allocation scheme, and we can get the processing time of every task fragments in the task queue by Task Fragments Processing Algorithm (TFPA). Experimental results show that the task-offloading strategy and computing source allocation scheme can reduce the energy consumption by 30%-40% in a cellular network composed of 100 base stations.

The life of many wireless sensor networks is limited by their battery-based energy source. Recharging batteries from a distance by the wireless energy transferring technique could lift this restriction. However, how to deploy the mobile charging device requires further research. In this paper, we take the energy constraint of mobile wireless charger (MWC) into consideration and aim at minimizing the total energy consumption by it in recharging cycles. After formulating the optimization problem, we present the MMES-LME method based on the modified MAXMIN Ant System and equalization strategy with the constraint of MWC limited energy. The equalization strategy is presented to equalize the lifespan of all sensor nodes to avoid the untimely death of WSN and balance the consumption of MWC travelling energy and recharging energy. Our experimental results demonstrate improved performance in comparison to the greedy method and MM-LME method, which is based on MAX-MIN Ant System.