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Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719, Tung, Chih-Wei, Chen, Jyh-Cheng and Li, Frank Y.
(2018)
Proportional and preemption-enabled traffic offloading for IP flow mobility: Algorithms and performance evaluation.
IEEE Transactions on Vehicular Technology, 67 (12).
pp. 12095-12108.
ISSN 0018-9545
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Abstract
IP Flow Mobility (IFOM) enables a user equipment to offload data traffic at the IP flow level. Although the procedure of IFOM-based flow offloading has been specified by 3GPP, how many IP flows should be offloaded and when offloading should be performed are not defined. Consequently, IP flows may be routed to a target access network which has a strong signal strength but with backhaul congestion or insufficient access capability. In this paper, we propose two algorithms, referred to as proportional offloading (PO), and proportional and preemption-enabled offloading (PPO), respectively, for IP flow offloading in hybrid cellular and wireless local area networks. The PO algorithm decides an optimal proportion of IP flows which could be offloaded by considering available resources at the target access network. In the PPO algorithm, both service continuity and network utilization are taken into consideration. Furthermore, a detailed analytical model is developed in order to evaluate the behavior of the proposed algorithms. The analytical model is validated through extensive simulations. The results show that by dynamically adjusting the percentage of traffic flows to be offloaded, PO can reduce blocking probability and increase resource utilization. PPO further improves the performance at the cost of slightly higher offloading overhead.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3gpp,analytical models,delays,heuristic algorithms,ip networks,wireless fidelity,automotive engineering,aerospace engineering,applied mathematics,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2203 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 18 Oct 2018 09:31 |
| Last Modified: | 22 Dec 2024 01:18 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/68594 |
| DOI: | 10.1109/TVT.2018.2872829 |
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