Mobility management through access network discovery and selection function for load balancing and power saving in software-defined networking environment

Abstract

The mobile traffic has grown rapidly with the popularity of smart mobile devices. To accommodate increasing traffic, heterogeneous network integration is considered as a viable solution. By overlapping the coverage of heterogeneous networks (e.g., the long-term evolution (LTE) and Wi-Fi integrated network), the mobile operators can use the offloading service (e.g., Wi-Fi offloading) to reduce network congestion. In this approach, a proper network coordination mechanism is required for load balancing of the LTE and Wi-Fi integrated network. In this paper, we use access network discovery and selection function (ANDSF) to suggest selection of proper base stations (e.g., LTE evolved Node Bs or Wi-Fi access points) to user equipment (UE) for load balancing. We integrate the ANDSF with software-defined networking (SDN) to make the ANDSF-aided network more programmable, flexible, and dynamically manageable. Moreover, we propose a power-saving ANDSF (PSA) algorithm to appropriately assign network resource to UEs and reduce the power consumption of Wi-Fi access points (APs). We have implemented the SDN-based PSA and measured the delay times. We also conduct simulation experiments to show that the successful probability of UEs' resource requests to PSA is almost the same as the proposed schemes of the previous studies when the network traffic is unbalanced. Our study indicates that for unbalanced traffic, PSA can reduce 15.63 % more power consumption of Wi-Fi AP than the previous approaches.

A Systematic Review of Load Balancing Techniques in Software-Defined Networking

“…For the load balance of the LTE and Wi-Fi integrated network, an appropriate network coordination mechanism is required. The proposed technique in [85] provides a right base station selection (e.g., LTE evolved node BS or Wi-Fi access points) for user equipment (UE) for load balance using the access network discovery and selection function (ANDSF). The ANDSF is integrated with software-defined networking (SDN) to make the ANDSF network more programmable, flexible, and dynamically manageable.…”

SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

“…Even though ANDSF can communicate with non-3GPP access, it cannot cope up with dynamic nature of network. SDN is well capable of managing a dynamic environment, is used in proposing a power saving algorithm [32], in which the idea is to move the ANDSF bundle to SDN, thus, SDN becomes responsible to the accuracy of measurement reports. Other areas of focus like load balancing and queuing works are also investigated under SDN control in [33,34].…”

Extensive Literature Survey on Load Balancing in Software-Defined Networking

“…Figure 1 demonstrates the algorithmic classification of load balancing in SDN network. The classificationisdonebasedonrouting,filtering,optimization,probabilistic,frameworks,scheduling anddistanceparameters.Accordingly,inrouting,RBSR ,Taser , Bellman-Fordalgorithm(Lee&Sheu,2016),routingwavelengthalgorithms (Simeonidou,et al, 2013)wereadoptedthatcontributes6.66%oftheentirecontribution.Similarly,filteringoffersabout 8.33%oftheentirecontributionthatinvolvesschemeslike,NLMSalgorithm (Montazerolghaem, et al,2017),FlowMon (Xing,et al,2016), CHA(Trajano&Fernandez,2016),LM (Bradai,et al,2015),PTA (Liu,et al,2015).Optimizationalgorithmscompriseabout18.33%oftheentire contributionanditincludesschemessuchas,POS (Basta,et al,2017),MCOPSO (He,et al,2016), PressureStaticAlgorithm (Tuncer,et al,2015),SA (Lange,et al,2015) (Ahmadi&Khorramizadeh, 2018;Hu,et al,2015),CSO ,P-Pathalgorithm (Ru,et al,2013), DPS (Kanonakis,et al,2013)andHFA (Kang&Choo,2016).Also,probabilisticschemesincludes algorithmslike,SMDM (Wang,et al,2017c),Markovapproximation (Huang,et al,2016),Vgala (Han&Ansari,2016),BHT (Lin,et al,2016a),UCHO (Tartarini,et al,2018),TPR (Chen,et al,2018a),PFSCA ,PTA (Lin,et al,2016b),ANCE (Silva,et al,2016),Non-Markovianmodel (Longo,et al,2015),FLA (Guo,et al,2014),DMDA (Tao,2018),ANDSF (Yang, et al,2016),PDO (Duan,et al,2015),ATLB (Zhang,et al,2008)andEMA (Wang,et al,2017d) models,thatprovidesabout26.67%oftheentirecontribution.Inaddition,frameworkscomprises of10%ofthetotalcontributionwhichinvolvesschemeslike,RFHC (Wang,et al,2017b),crosslayerdesign (Niu,et al,2016),dataplanescheme…”

“…Citations Topologies Two-dimensionalHyperXtopology (Soni,et al,2017;Hu,et al,2015;Lin,et al,2016b;Koryachko,et al,2017) Internetnetworktopology (Ma,et al,2017) (Rückert,et al,2016) Treetopology (Wang,et al,2017a;Wang,et al,2017b;Huang,et al,2016;Lin,et al,2016a) Fattreetopology (Lange,et al,2015;Ahmadi&Khorramizadeh,2018;Tao,2018) TopologyZoo (Fang,et al,2013) Datacentrenetworktopology Molina,et al,2015;Guo,et al,2014) Ringtopology (Tuncer,et al,2015;Liu,et al,2015) Abilenenetworktopology ) Sparsetopology (Özçevik,et al,2017) Virtualizedtopology (Lee&Sheu,2016) Networktopology Testtopology (Silva,et al,2016) WiNeMOtopology (Trajano&Fernandez,2016) "4-post"networktopology (Longo,et al,2015) Startopology (Kanonakis,et al,2013) Point-to-multipointPONtopology, (Yang,et al,2016) Meshtopology Citations Configurations Staticnetwork-20-nodenetworkwith3controllers (Wang,et al,2017c) BTNorthAmerica(36nodesand76links) (Miao,et al,2015) OPSnode (Rangisetti,et al,2017) Evolved-NodeBs(eNBs) (Bradai,et al,2015) Rocketfuel(50nodes) (Ru,et al,2013) NSFnetmodel(14nodesand42links) (Montazerolghaem,et al,2017) 2SMDMSs7OpenFlowswitches (Nahida,et al,2017) FourstationsandtwoAPs (Basta,et al,2017) USA(18SGWs,4PGWs),Germany(15SGWs,3PGWs), (He,et al,2016) Foglayer (Han&Ansari,2016) RANcontroller, (Xing,et al,2016) 20sFlowenabledOpenvSwitches (Yonghong,et al,2014;Tartarini,et al,2018) bwm-ngtool (Shang,et al,2018) ThinkPadE530 (Chen,et al,2018a) APs(right)twoSDN-enabledAPs...…”