Browsing by Subject "Absorbing Markov chains"

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Open Access
Distribution of age of information in status update systems with heterogeneous information sources: an absorbing markov chain-based approach
(Institute of Electrical and Electronics Engineers, 2023-05-31) Akar, Nail; Gamgam, Ege Orkun
In this letter, we obtain the exact distributions of the Age of Information (AoI) and Peak AoI (PAoI) in a non-preemptive multi-source status update system for (i) Generate-At-Will (GAW) servers with probabilistic transmissions (ii) Random Arrival with Single Buffer (RA-SB) servers employing probabilistic buffer management, using absorbing Continuous-Time Markov Chains (CTMC). For both servers, the information sources are allowed to have different relative urgencies, phase-type service time distributions, and transmission error probabilities, for the sake of generality. Numerical examples are presented to validate the proposed analytical model.
Open Access
Scheduling and queue management for information freshness in multi-source status update systems
(2023-09) Gamgam, Ege Orkun
Timely delivery of information to its intended destination is essential in many ex-isting and emerging time-sensitive applications. While conventional performance metrics like delay, throughput, or loss have been extensively studied in the literature, research concerning the management of age-sensitive traffic is relatively immature. Recently, a number of information freshness metrics have been intro-duced for quantifying the timeliness of information in networked systems carrying age-sensitive traffic, primarily the Age of Information (AoI) and peak AoI (PAoI) metrics as well as their alternatives including Age of Synchronization (AoS), ver-sion age, binary freshness, etc. The focus of this thesis is the development and performance modeling of age-agnostic scheduling and queue management policies in various multi-source status update systems carrying age-sensitive traffic, using the recently introduced information freshness metrics. In this thesis, first, the exact distributions of the AoI and PAoI for the probabilistic Generate-At-Will (GAW) and Random Arrival with Single Buffer (RA-SB) servers are studied with general number of heterogeneous information sources with phase-type (PH-type) service time distributions for which an absorbing Continuous-Time Markov Chains (CTMC) based analytical modeling method, namely AMC (Absorbing Markov Chains) method, is proposed. Secondly, a homogeneous multi-source status update system with Poisson information packet arrivals and exponentially distributed service times is studied for which the server is equipped with a queue holding the freshest packet from each source referred to as Single Buffer Per-Source Queueing (SBPSQ). For this case, two SBPSQ-based scheduling policies are studied, namely First Source First Serve (FSFS) and the Earliest Served First Serve (ESFS) policies, using the AMC method, and it is shown that ESFS presents a promising scheduler for this special setting. Third, a general status update system with two heterogeneous information sources is studied, i.e., sources have different priorities and generally distributed service times, for Deterministic GAW (D-GAW) and Deterministic RA-SB (D-RA-SB) servers. The aim in both servers is to minimize the system AoI/AoS that is time-averaged and weighted across the two sources. For the D-GAW server, the optimal update policy is obtained in closed form. A packet replacement policy, referred to as Pattern-based Replacement (PR) policy, is then proposed for the D-RA-SB server based on the optimal policy structure of the D-GAW server. Finally, scheduling in a cache update system is investigated where a remote server delivers time-varying contents of multiple items with heterogeneous popularities and service times to a local cache so as to maximize the weighted sum binary freshness of the system, and the server is equipped with a queue that holds the most up-to-date content for each item. A Water-filling based Scheduling (WFS) policy and its extension, namely Extended WFS (E-WFS) policy, are proposed based on convex optimization applied to a relaxation of the original system, with low computational complexity and near optimal weighted sum binary freshness performance.