Analyzing the age of information in prioritized status update systems under an interruption-based hybrid discipline

Performance Evaluation(2024)

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摘要
Motivated by real-life applications, a special research-work interest has been recently directed towards the prioritized status update systems, which prioritize the update streams according to their timeliness constraints. The preferential service treatment between priority classes is commonly based on classical disciplines, preemption and non-preemption. However, both disciplines fail to give an even satisfaction between all classes. In our work, an interruption-based hybrid preemptive/non-preemptive discipline is proposed under a single-buffer system modelled as an M/M/1/2 priority queueing system. Each class being served (resp. buffered) can be preempted unless its recorded number of service preemptions reaches the predetermined in-service (resp. in-waiting) threshold. All thresholds between classes are the controlling parameters of the whole system’s performance. Using the stochastic hybrid system approach, the age of information (AoI) performance metric is analyzed in terms of its statistical average along with the higher-order moments, considering a general number of priority classes. Closed-form results are also obtained for some special cases, giving analytical insights about the AoI stability in heavy loading conditions. The average AoI and its dispersion are numerically investigated for the case of a three-class network. The significance of the proposed model is manifested in achieving a compromise satisfaction between all priority classes by a thorough adjustment of its threshold parameters. Two approaches are proposed to clarify the adjustment of these parameters. It turned out that the proposed hybrid discipline compensates for the limited buffer resource, achieving more promising performance with low design complexity and low cost. Moreover, the proposed scheme can operate under a wider span of the total offered load, through which the whole network satisfaction can be optimized under some legitimate constraints on the age-sensitive classes.
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