Distribution of Age of Information in the Erlang Loss System

2026-09-30

AI Summary

This study addresses the computational intractability of deriving exact distributions for the Age of Information (AoI) and its peak in bufferless Erlang loss systems (M/M/c/c). To overcome this challenge, we propose an absorbing Markov chain-based modeling framework. By reformulating a single AoI cycle as an absorption problem and integrating Poisson process theory with birth-death processes, we derive closed-form matrix-exponential solutions for the AoI distribution under three update policies: non-preemptive, random preemptive, and preemptive oldest-update. The analysis reveals how distinct update strategies differentially shape AoI distributional characteristics. These findings establish a rigorous theoretical foundation for evaluating system performance under age constraints and for optimizing server resource allocation.

Abstract

In this paper, we study the exact distributions of the age of information (AoI) and peak AoI (PAoI) in a bufferless setting in which time-stamped updates, or processed tasks, generated by one or several information sources according to a Poisson process for each source, are submitted to a shared pool of $c$ homogeneous servers with exponentially distributed service times, i.e., the so-called M/M/c/c or the Erlang loss system. We consider three update management policies which come into play when a new update arrives to find all the servers busy: the arriving update is blocked (non-preemptive, NP) as in the Erlang loss system, or it preempts a randomly chosen update of its own source in service (preempt at random, PR), or the stalest such update (preempt the stalest, PS). All three policies preserve the same birth-death structure of the server occupancy process associated with the underlying Erlang loss system. However, their AoI and PAoI distributions can be very different. The approach we take is the absorbing Markov chain (AMC) method, in which a single AoI cycle, rather than the entire sample path of the system, is modeled by an absorbing Markov chain. Via the AMC method, we derive the exact distributions of AoI and PAoI in matrix-exponential form. The method extends to multiple sources sharing the same pool of servers, with the AoI of a given source being affected by the remaining sources only through their aggregate update rate. Numerical examples illustrate the implications of our findings, including distribution-based server provisioning under age violation constraints.

Problem

• Age of Information
• Erlang loss system
• Peak AoI
• Bufferless system
• Update management policies

Innovation

• Age of Information
• Erlang loss system
• Absorbing Markov chain
• Matrix-exponential distribution
• Update management policies