{"title":"A priority-aware dynamic scheduling algorithm for ensuring data freshness in 5G networks","authors":"Beom-Su Kim","doi":"10.1016/j.future.2024.107542","DOIUrl":null,"url":null,"abstract":"<div><div>To ensure the freshness of information in wireless communication systems, a new performance metric named the age of information (AoI) is being adopted in the design of transmission schedulers. However, most AoI schedulers rely on iterative optimization methods, which struggle to adapt to real-time changes, particularly in real-world 5G deployment scenarios, where network conditions are highly dynamic. In addition, they neglect the impact of consecutive AoI deadline violations, which result in prolonged information deficits. To address these limitations, we present a 5G scheduler that can cope with dynamic network conditions, with the aim of minimizing the long-term average AoI under deadline constraints. Specifically, we consider a dense urban massive machine-type communication (mMTC) scenario in which numerous Internet of Things (IoT) devices frequently join or leave the network under time-varying channel conditions. To facilitate real-time adaptation, we develop a per-slot scheduling method that makes locally optimal decisions for each slot without requiring extensive iterations. In addition, we combine the per-slot scheduling method with a priority-rule scheduling algorithm to satisfy the stringent timing requirements of 5G. The simulation results show that the proposed scheduler reduces the average AoI by 10%, deadline violation rate by 40%, and consecutive violation rate by 20% approximately compared with other AoI schedulers.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107542"},"PeriodicalIF":6.2000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24005065","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
To ensure the freshness of information in wireless communication systems, a new performance metric named the age of information (AoI) is being adopted in the design of transmission schedulers. However, most AoI schedulers rely on iterative optimization methods, which struggle to adapt to real-time changes, particularly in real-world 5G deployment scenarios, where network conditions are highly dynamic. In addition, they neglect the impact of consecutive AoI deadline violations, which result in prolonged information deficits. To address these limitations, we present a 5G scheduler that can cope with dynamic network conditions, with the aim of minimizing the long-term average AoI under deadline constraints. Specifically, we consider a dense urban massive machine-type communication (mMTC) scenario in which numerous Internet of Things (IoT) devices frequently join or leave the network under time-varying channel conditions. To facilitate real-time adaptation, we develop a per-slot scheduling method that makes locally optimal decisions for each slot without requiring extensive iterations. In addition, we combine the per-slot scheduling method with a priority-rule scheduling algorithm to satisfy the stringent timing requirements of 5G. The simulation results show that the proposed scheduler reduces the average AoI by 10%, deadline violation rate by 40%, and consecutive violation rate by 20% approximately compared with other AoI schedulers.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.