## 1 Introduction
[PERSON] and [PERSON] [31] proposed a hybrid GA-PSO algorithm to reduce the makespan and balance the load of dependent tasks over heterogeneous resources. [PERSON] et al. [32] proposed an efficient hybrid algorithm combining the gravitational search algorithm and heterogeneous earliest finish time algorithm. With the significantly increasingly large amounts of data produced by satellites, more studies have been conducted to address the challenges in remote sensing big data storage [33; 34] and the efficient and scalable processing of remotely sensed data [1; 35; 36; 37]. PSO is widely used for different remote task scheduling research. [PERSON] et al. [38] used PSO in task scheduling for unmanned aerial vehicle (UAV) swarm remote sensing in distributed photovoltaic array maintenance. [PERSON] et al. [39] proposed a hybrid algorithm of PSO and a genetic algorithm to improve the dynamic regional splitting planning of a remote sensing satellite swarm. There is a growing need to develop an effective multi-objective scheduling framework that jointly minimizes the task cost, task energy consumption, and execution time. [PERSON] et al. [40] developed a multi-objective PSO algorithm to minimize the waiting time and maximize the system throughput. [PERSON] et al. [41] proposed a multi-objective Quality-of-Service model to address customers' expectations based on execution time and execution cost criteria. [PERSON] et al. [42] proposed a comprehensive multi-objective model to solve a task scheduling sub-problem by using the Bayes belief model and learnable ant colony optimization. [PERSON] et al. [43] used an evolutionary computation scheduling method for satellite periodic continuous observation task scheduling, and several constraints were considered for the satellite working pattern. In a recent study by [PERSON] et al. [44], an energy-efficient solution for multi-objective task scheduling for the cloud implementation of hyperspectral image classification was proposed. However, only execution time and energy consumption were taken into consideration. With the emergence of new types of Internet of Things (IoT) devices, the performance service requirements for the network have increased. In addition to the traditional way of using upgraded network bandwidth to avoid network congestion, it can also predict the available performance level of the network by analyzing historical service data so as to select network resources and make adjustments according to actual conditions. Based on the historical data of the network transmission of nodes, it is found that there are relatively few methods to study the law of the network transmission of nodes. Especially in the field of remote sensing data collection, there is little relevant research on predicting the collection speed of each node, guiding dynamic task scheduling, and improving the resource utilization of nodes. This paper mainly studies network-intensive task scheduling. The research on network data transmission problems is mostly aimed at the transmission of a large amount of task data or the higher requirements for network transmission data. In order to reduce the impact of Coflow Completion Time (CCT) on applications in the data parallel framework, in 2018, [PERSON] [45] changed the practice of pre-setting coflow composing flow endpoints in traditional research work and proposed a joint online Reducer Placement and Coflow bandwidth scheduling framework to minimize the average CCT in the cloud cluster. In 2019, [PERSON] et al. [46] used the recurrent neural network sequence prediction algorithm to realize the prediction of the central processing unit (CPU) utilization and network bandwidth utilization of real-time migration in the cloud environment. In order to monitor the network traffic of the network data center, network utilization must be improved. The conventional approach is to modify the hardware or terminal host to increase monitoring resources, but this will increase monitoring overhead and is not conducive to long-term development. [PERSON] et al.