## 1 Introduction  
With the support of the internet of things (IoT) and cloud computing, new-generation information systems have gained increasing prominence in recent decades. Among them, intelligent video surveillance applications require massive computing and storage resources [1; 2; 3; 4; 5; 6; 7] to offer a high quality of service. However, the complexity of video surveillance task and the diversity of data in different dimensions, such as volume, variable, and velocity, inevitably cause a heavy burden on the network. Therefore, with a large amount of video surveillance data flooding to the cloud center, cloud data center networks are facing increasing data transmission problems [8]. At this time, the network throughput has become the bottleneck of video surveillance transmitting massive data. What needs to be solved urgently is the issue of traditional hardware devices and static scheduling algorithms finding it difficult to handle the transmission task for such massive amounts of information. The emergence of software-defined networking (SDN) [9] solves the problems of traditional hardware, which is non-programable. SDN uses methods including centralization of network control and separation of control logic and underlying switches, allowing researchers to use related technologies (OpenFlow [10]) to focus on the network flow scheduling algorithm to meet different needs. One of the highly recognized schemes is load-balanced scheduling [11]. Load-balanced scheduling increases the network throughput and reduces the end-to-end delay of the flow by balancing the traffic transmitted on the network link. Dynamic load-balanced scheduling algorithms can better adapt to the real-time changing state of the network when compared with the inextensibility of traditional static scheduling algorithms [12]. Nowadays, some load-balanced scheduling algorithms have been proposed for data center networks [13; 14]. Nevertheless, some of these algorithms only care about the overall throughput of the network and perform the same processing for all flows in the network [15]. Not specifically considering the characteristics of different flows results in high latency for small flows in some conditions. Furthermore, some of the dynamic adjustment algorithms rely on the corresponding feedback mechanism after the network is blocked. Therefore, the performance of the network is closely related to the selected corresponding threshold, which is a critical value defined by different articles to measure network performance [12]. A relatively low threshold will add extra overhead to the network, whereas a relatively high threshold will not significantly alleviate the network congestion. The algorithm is passive if it only adjusts when it meets the congestion threshold. In a word, the diversity and uncertainty of network tasks lead to more complex scientific problems. A single strategy cannot achieve the goal well, so a composite adaptive method is needed. Inspired by the above situation, the MFLBS algorithm is designed to prevent network congestion through a proactive scheduling mechanism and dynamically adjusted on routing and bandwidth allocation in real time to maximize network throughput and reduce delay, according to the network situation. We split the entire network into two sub-nets: first, tune into the dynamic routing network of the flow to balance the network load and take into account the delay of the small flow, and then, enter the static routing network to actively prevent network congestion, according to the network status. Meanwhile, the MFLBS algorithm is tested in the widely used tree and mesh network topologies [16] to observe its universality. The specific contributions of this article are as follows:  
* A new cloud data center scheduling algorithm (MFLBS) is proposed and formulated to achieve the goal of maximizing throughput.