<![CDATA[Network slicing is one of the main mechanisms used in 5G systems to let several service types operate over the same physical infrastructure. In this work, the shared system includes eMBB, URLLC, mMTC, and Best Effort slices, all competing for limited bandwidth and CPU capacity. Because the resource-allocation rule can change both fairness and service quality, this paper studies three policies under the same traffic conditions: Jain-based egalitarian allocation (JF), Max-Min Fairness (MMF), and Proportional Fairness (PF). A Python simulator is used to apply each policy separately to identical demand samples, after which the resulting allocations are evaluated using Jain’s Fairness Index (JFI) and the Gini coefficient. The traffic model follows a Poisson process with low, medium, and high load levels, and each case is repeated for 100 independent Monte-Carlo runs. In addition to fairness, the evaluation reports throughput, packet loss ratio, jitter, delay, bandwidth and CPU utilization, SLA satisfaction, and one-way ANOVA tests. The results show that JF gives perfect equality at all loads (JFI = 1.0, Gini = 0), although this equality reduces performance when the offered load is high. MMF is the strongest demand-aware fairness policy and provides the most balanced behavior under medium and high load. PF gives the lowest high-load jitter, but it also records the weakest SLA satisfaction (0.12). The ANOVA results indicate statistically significant differences (p < 0.05) among the policies for most metrics in the medium- and high-load scenarios.]]>
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