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The fifth-generation (5G) cellular network includes a cloud-based architecture called Cloud Radio Access Network (C-RAN). This architecture centralizes BaseBand Units (BBUs) of all Base Stations (BSs) into a virtualized BBU pool in the cloud. BBUs generate Virtual Machines (VMs) to manage User Equipment (UE) requests, and adjusting the number of VMs can enhance both network capacity and energy efficiency. This paper explores the impact of VM management on energy efficiency in 5G C-RAN through a detailed analysis of two strategies: the Virtual Machine Hysteresis Allocation Strategy (VMHAS) and the Virtual Machine Allocation Strategy (VMAS). VMHAS utilizes the hysteresis mechanism to reduce energy consumption by adjusting the number of VMs in BBUs based on traffic load. It consists of switching the idle VMs to sleep mode to save energy. This strategy is evaluated under two setups: one with two hysteresis levels of VMs and one with three hysteresis levels. In contrast, VMAS allocates VMs without considering sleep mode. We develop performance Markov Reward Models (MRMs) for the investigated strategies. We use Probabilistic Model Checking (PMC) to check the performance requirements written with Continuous Stochastic Reward Logic (CSRL). The investigated strategies are implemented and checked using the PRISM model checker. The results show that the VMHAS configurations, especially those with three hysteresis levels, significantly reduce energy consumption compared to VMAS.

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Efficiency of Varying Hysteresis Levels on Energy Consumption in Cloud Radio Access Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Maroua Idi,
  • Sana Younes

摘要

The fifth-generation (5G) cellular network includes a cloud-based architecture called Cloud Radio Access Network (C-RAN). This architecture centralizes BaseBand Units (BBUs) of all Base Stations (BSs) into a virtualized BBU pool in the cloud. BBUs generate Virtual Machines (VMs) to manage User Equipment (UE) requests, and adjusting the number of VMs can enhance both network capacity and energy efficiency. This paper explores the impact of VM management on energy efficiency in 5G C-RAN through a detailed analysis of two strategies: the Virtual Machine Hysteresis Allocation Strategy (VMHAS) and the Virtual Machine Allocation Strategy (VMAS). VMHAS utilizes the hysteresis mechanism to reduce energy consumption by adjusting the number of VMs in BBUs based on traffic load. It consists of switching the idle VMs to sleep mode to save energy. This strategy is evaluated under two setups: one with two hysteresis levels of VMs and one with three hysteresis levels. In contrast, VMAS allocates VMs without considering sleep mode. We develop performance Markov Reward Models (MRMs) for the investigated strategies. We use Probabilistic Model Checking (PMC) to check the performance requirements written with Continuous Stochastic Reward Logic (CSRL). The investigated strategies are implemented and checked using the PRISM model checker. The results show that the VMHAS configurations, especially those with three hysteresis levels, significantly reduce energy consumption compared to VMAS.