Load balancing method and device based on SDK (Software Development Kit) ticket and computing equipment

Abstract

The embodiment of the invention relates to the technical field of communication, and discloses a load balancing method and device based on an SDK (Software Development Kit) ticket and computing equipment, and the method comprises the following steps: screening high and low load cells according to the load condition of an SDK ticket sampling correlation area coverage cell, and positioning a cell to be subjected to load balancing; according to the screened high-load cells and the screened low-load cells, determining the load conditions of the local station cells and the adjacent station cells, and further judging whether the cells to be subjected to load balancing have the possibility of balancing or not; and if yes, carrying out load balancing on the cell to be subjected to load balancing, and if not, carrying out capacity expansion and / or new station establishment on the cell to be subjected to load balancing. Through the mode, the problem area can be accurately positioned, the positioning time is shortened, and the load balancing efficiency is improved.

Description

technical field [0001] The embodiment of the present invention relates to the technical field of communication, and in particular to a load balancing method, device and computing device based on an SDK bill. Background technique [0002] With the rapid growth of mobile video services such as Migu Aikan, Youku, Sohu, Tencent, iQiyi, Douyin, and Kuaishou, the proportion of video services in data services continues to increase. According to the existing video service degradation base station (community) Analysis shows that the proportion of capacity problems is gradually increasing. The current load optimization method for video capacity problems is the traditional data service balancing method, which directly locates video capacity problems according to the wireless utilization rate of the cell or the physical resource block (physical resource block, PRB) utilization rate. The efficiency is low, and the positioning is not accurate. A faster and more effective capacity problem ...

AI Technical Summary

Problems solved by technology

[0002] With the rapid growth of mobile video services such as Migu Aikan, Youku, Sohu, Tencent, iQiyi, Douyin, and Kuaishou, the proportion of video services in data services continues to increase. According to the existing video service degradation base station (community) Analysis shows that the proportion of capacity problems is gradually increasing. The current load optimization method for video capacity problems is the traditional data service balancing method, which directly locates video capacity problems according to the wireless utilization rate of the cell or the physical resource block (physical resource block, PRB) utilization rate. The efficiency is low, and the location is inaccurate. A faster and more effective capacity problem location method is urgently needed to meet the rapid growth of video data services.

[0003] The current load balancing technical solution is relatively extensive, and has the following defects: the amount of data is too large, with more than 3.1 million pieces of data in the province’s video bills every day, and traditional methods cannot directly analyze massive data; the geographical dimension positioning is vague, and the balance is only based on the same station and the same direction The unbalanced difference of the cell is greater than 20%, and the wireless utilization rate load is less than 50%. Traffic balancing is performed, and only the sector balancing within 10° of the same station and the same direction is considered. The balanced geographical area is small, and the opposite side is not considered in the positioning process. Influenced by the base station and the left and right sectors in the same direction in the same station, the selection area is narrow, so there are cells with idle traffic around, and the excess traffic in this area cannot be absorbed, and the efficiency is slow and resources cannot be effectively allocated; it is not considered in the equalization process Traffic increases due to cross-area coverage, RF optimization dimension processing is not performed, and wireless environment optimization is not used; wireless utilization is only considered during equalization, and uplink and downlink problems are not distinguished accurately, and the uplink PRB utilization rate is high , the problem of low wireless utilization is ignored, which affects the perception of this part of the user; the processing process does not carry out the combination of big data simulation and sampling point geolocation, and the problem cannot be accurately determined; the problem location time is too long, and it takes 20 hours to locate a problem through the network management combined with the map minutes or so

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