Software-defined networking (SDN) has gained a tremendous attention in the recent years, both in academia and industry. This revolutionary networking paradigm is an attempt to bring the advances in computer science and software engineering into the information and communications technology (ICT) domain. The aim of these efforts is to pave the way for completely programmable networks and control-data plane separation. Recent studies on feasibility and applicability of SDN concepts in cellular networks show very promising results and this trend will most likely continue in near future. In this work, we study the benefits of SDN on the radio resource management (RRM) of future-generation cellular networks. Our considered cellular network architecture is in line with the recently proposed Long-Term Evolution (LTE) Release 12 concepts, such as controldata plane split, heterogeneous networks (HetNets) environment, and network densification through deployment of small cells. In particular, the aim of our RRM scheme is to enable the macro base station (BS) to efficiently allocate radio resources for small cell BSs in order to assure quality-of-service (QoS) of moving users/vehicles during handoffs. We develop an approximate, but very time-and space-efficient algorithm for radio resource allocation within a HetNet. Experiments on commodity hardware show algorithm running times in the order of a few seconds, thus making it suitable even in cases of fast moving users/vehicles. We also confirm a good accuracy of our proposed algorithm by means of computer simulations.
The current status of a campus research testbed that is being constructed to allow for the exploration of digital service delivery and smart networked environments using different networking technologies is presented.
QoS provisioning is one of the key challenges facing current as well as future Internet architectures. Its dependency on content recognition does not allow a straightforward support of QoS in the IP, host-centric, model. In contrast, Information-Centric Networking (ICN) offers native content identification in the network, which can be exploited to develop a common, elegant, framework for supporting QoS-based delivery. Therefore, ICN may naturally overcome many of the cumbersome fixes and limitations of today's solutions. In this work, we exploit the flexibility in semantic representation offered by ICN to present a flexible and scalable ICN-based QoS model. Our model defines QoS requirements as information items that can be linked to the content at various aggregation levels, independent of the communication approach. Therefore, it can be applied uniformly to various network types and hierarchies. Furthermore, our model offers enhanced traffic treatment as well as resource utilization while significantly reducing the overhead on the network.
With the growth of mobile handsets and services provided over wireless networks, the need of dynamically managed environments is obvious. Network providers have already moved from E1/T1 lines to more scalable technologies, including EPONs (Ethernet Passive Optical Networks) and WiMax. These new technologies support service differentiation and Quality of Service (QoS). This work presents an overlay, cross-technology, signaling protocol which allows information exchange, enabling convergence in terms of common resource management, between different types of networks. This behavior is desirable mainly in the fixed-mobile convergence (FMC) area. The benefits of such a common, distributed, resource management scheme are presented in this work. A set of simulations performed using OMNet++, show the advantages of its use in converged EPONWiMax networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.