We are developing an advertising system on SaaS platforms for the entertainment industry, with a focus on solving the problem of multi tenant resource scheduling. From 2017 to 2019, the global entertainment industry grew rapidly, and SaaS became a key support for digital transformation due to its low cost, high security, and flexible customization. But the old system has many flaws - single functionality, insufficient reputation evaluation models, and inability to keep up with resource scheduling. We used the fuzzy analytic hierarchy process to establish a tenant reputation evaluation system, scoring each tenant. Then, by combining Min2 dynamic adjustment and cyclic queue strategies, resource allocation can be optimized. The system integrates modules such as finance, data statistics, and tenant information management. The third mode of data isolation is MySQL read-write separation combined with Redis caching, which ensures security and improves access speed. The test results are okay. All functional modules can run smoothly, and performance tests show an average response time between 158 and 258 milliseconds. The throughput is 329 to 471 times per second, and the error rate does not exceed 0.52% for 1000 to 3000 concurrent threads. The resource scheduling strategy has increased the overall hit rate, reduced operating costs, and basically met SLA constraints.This mechanism can adapt to the dynamic changes of tenants and provide some practical reference for advertising resource scheduling on SaaS platforms in the entertainment industry.

Linlin Sun, Optimization of Resource Scheduling for Multi Tenant Advertising Systems. International Journal of Neural Network (2026), Vol. 5, Issue 1: 90-97. https://doi.org/10.38007/NN.2026.050110.

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