<![CDATA[Cloud applications share execution capacity while imposing different deadlines and security screening requirements. A scheduler can therefore face congestion even when aggregate capacity appears sufficient, particularly when a large tenant reaches its concurrency limit. This study investigates interaction through a reproducible discrete event simulation of screened cloud jobs. Four nonpreemptive policies are compared: first in first out, shortest job first, earliest deadline first, and a deadline policy with a bounded penalty for tenants that have already consumed more virtual service. The primary experiment contains 240 runs covering three arrival rates, 20 random seeds, and 1,200 jobs per run. An additional 80 runs remove the restrictive tenant concurrency limit at the highest arrival rate. With eight workers and a four-worker tenant limit, shortest job first has a mean deadline miss rate of 5.26% at 1.5 jobs per simulated second, compared with 44.57% for first in first out and 23.97% for earliest deadline first. The service balance candidate reaches 28.91%, showing that the added fairness penalty does not improve deadline performance in this workload. Increasing the tenant limit from four to eight reduces the earliest deadline miss rate to 1.41%. These findings identify tenant capacity fragmentation and job length variation as stronger influences than the tested fairness adjustment. The results are generated from synthetic workloads and do not measure production cloud performance or resistance to cyberattacks. The contribution is an executable comparative study of scheduling under a mandatory modeled screening stage, with explicit workload assumptions and a tenant capacity sensitivity analysis.]]>
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