Browsing by Subject "Binary freshness"

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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.
Open Access
Water-filling-based scheduling for weighted binary freshness in cache update systems
(Institute of Electrical and Electronics Engineers, 2023-10-09) Gamgam, Ege Orkun; Akar, Nail
We consider a cache update system with a remote server delivering time-varying contents of multiple Internet of Things (IoT) items with heterogeneous popularities and service times to a local cache so as to keep the items as fresh as possible at the cache. New content for an item arrives at the server according to a Poisson process and the server is equipped with multiple queues each of which holds the most up-to-date content for the corresponding item. In this setting, we study several scheduling policies employed at the server so as to maximize the popularity-weighted binary freshness across the items. The scheduling problem is first formulated as an infinite-horizon average-reward Markov decision process (MDP) which suffers from the curse of dimensionality when the number of items is large. We then propose a water-filling-based scheduling (WFS) policy and its extension, namely, extended WFS (E-WFS) policy, with worst case complexities being quadratic and cubic in the number of items, respectively, based on convex optimization applied to a relaxation of the original system. Simulation results are provided to validate the effectiveness of the proposed policies.