ZQTRTT: A Multipath Scheduler for Heterogeneous Traffic in ICNs Based on Zero Queueing Time Ratio
2022 (English)In: ICN 2022 - Proceedings of the 2022 9th ACM Conference on Information-Centric Networking, Association for Computing Machinery, Inc , 2022Conference paper, Published paper (Refereed)
Abstract [en]
Information-centric networks (ICNs) intrinsically support multipath transfer and thus have been seen as an exciting paradigm for IoT and edge computing, not least in the context of 5G mobile networks. One key to ICN's success in these and other networks that have to support a diverse set of services over a heterogeneous network infrastructure is to schedule traffic over the available network paths efficiently. This paper presents and evaluates ZQTRTT, a multipath scheduling scheme for ICN that load balances bulk traffic over available network paths and schedules latency-sensitive, non-bulk traffic to reduce its transfer delay. A new metric called zero queueing time (ZQT) ratio estimates path load and is used to compute forwarding fractions for load balancing. In particular, the paper shows through a simulation campaign that ZQTRTT can accommodate the demands of both latency-sensitive and-insensitive traffic as well as evenly distribute traffic over available network paths.
Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2022.
Keywords [en]
ICN, low latency, multipath scheduling, zero queue time, 5G mobile communication systems, Queueing networks, Queueing theory, Scheduling, Heterogeneous traffic, Information Centric Networks, Multipath, Network paths, Network-based, Queue time, Time ratio, Heterogeneous networks
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:ri:diva-60260DOI: 10.1145/3517212.3558080Scopus ID: 2-s2.0-85138683785ISBN: 9781450392570 (print)OAI: oai:DiVA.org:ri-60260DiVA, id: diva2:1702346
Conference
9th ACM Conference on Information-Centric Networking, ICN 2022, 19 September 2022 through 21 September 2022
Note
Funding details: 101015922; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS; Funding text 1: This work was partly funded by The Knowledge Foundation (KKS) through the SIDUS READY project. B. Ahlgren was additionally supported by the Democritus project within the Digital Futures centre, and the AI@EDGE EU Horizon 2020 project under grant agreement No 101015922.
2022-10-102022-10-102023-05-10Bibliographically approved