Vasilakos, Xenofon, Al-Khalidi, Mohammed ORCID: https://orcid.org/0000-0002-1655-8514, Siris, Vasilios A, Reed, Martin J ORCID: https://orcid.org/0000-0002-6708-4478, Thomos, Nikolaos ORCID: https://orcid.org/0000-0001-7266-2642 and Polyzos, George C (2017) Mobility-based proactive multicast for seamless mobility support in cellular network environments. In: 1st Workshop on Mobile Edge Communications (MECOMM), 21 August 2017, Los Angeles, CA.
|
Accepted Version
Available under License In Copyright. Download (339kB) | Preview |
Abstract
Information-Centric Networking (ICN) is receiver driven, asynchronous and location-independent, hence it natively supports client-mobility. However, post-handover delay is a problem for delay-sensitive mobile applications, as they need to (re-)submit their subscriptions and wait for them to get resolved and (probably re-) transmitted before receiving the demanded data. To avoid this problem and optimize performance, this paper proposes a Mobilitybased Proactive Multicast (MPM) scheme. Unlike reactive or blind multicast solutions proposed in the past, MPM takes autonomous decisions locally at various network access points (cells) prior to the movement of mobile clients, using a semi-Markov mobility prediction model that predicts next-cell transitions, along with anticipating the duration between the transitions for an arbitrary user in a cellular network. Since cellular backhaul links are typically a bottleneck, MPM trades-off effectively part of the capacity of the (congested) backhaul link for a decreased delay experienced by users after handovers thanks to a congestion pricing scheme used for backhaul capacity allocation. Our preliminary performance evaluation results show that MPM captures well the temporal locality of mobile requests due to the semi-Markov mobility prediction model, hence it achieves a better performance compared to both a (i) blind/naïve multicast and a (ii) content popularity-based proactive multicast.
Impact and Reach
Statistics
Additional statistics for this dataset are available via IRStats2.