A Cloud Native Solution for Dynamic Auto Scaling of MME in LTE

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%

Architectural Challenges and Solutions for Collocated LWIP - A Network Layer Perspective

Achieving a tighter level of aggregation between LTE and Wi-Fi networks at the radio access network (a.k.a. LTE-Wi-Fi Aggregation or LWA) has become one of the most prominent solutions in the era of 5G to boost network capacit y and improve end user's quality of experience. LWA offers flexible resource scheduling decisions for steering user tr affic via LTE and Wi-Fi links. In this work, we propose a Collocated LTE/WLAN Radio Level Integration architecture at IP layer (C-LWIP), an enhancement over 3GPP non-collocated LWIP architecture. We have evaluated C-LWIP performance in vari ous link aggregation strategies (LASs). A C-LWIP node ( i.e. , the node having collocated, aggregated LTE eNodeB and Wi-Fi access point functionalities) is implemented in NS-3 which introd uces a traffic steering layer ( i.e. , Link Aggregation Layer) for efficient integration of LTE and Wi-Fi. Using extensive simulations, we verified the correctness of C-LWIP module in NS-3 and evaluat ed the aggregation benefits over standalone LTE and Wi-Fi netwo rks with respect to varying number of users and traffic types. We found that split bearer performs equivalently to switched b earer for UDP flows and switched bearer outperforms split bearer in the case of TCP flows. Also, we have enumerated the potential challenges to be addressed for unleashing C-LWIP capabilit ies. Our findings also include WoD-Link Aggregation Strategy whi ch is shown to improve system throughput by 50% as compared to Naive-LAS in a densely populated indoor stadium environmen t

Software Defined Approaches for Management of Mobility, Load and Energy in Wireless Networks

Proliferation of smart mobile devices and their applications demand Mobile Network Operators (MNOs) to expand their infrastructure to address coverage and capacity issues. Cellular networks, with their current in fl exible and expensive network infrastructure, are facing various challenges in ef fi ciently handling the exponentially growing traf fi c demands of users. MNOs have started dense deployment of Long Term Evolution (LTE) Heteroge- neous Networks (HetNets) with various small cells (Femto Base Stations (FBSs), pico, mi- cro, etc. ) under overlaying Macro cells for expanding network coverage and offering higher data rates. To further augment capacity of cellular networks, Wi-Fi Access Points (APs) are also being deployed by MNOs. However, dense deployment of small cells increases con- trol plane complexity in handling Radio Access Network (RAN) tasks like load balancing, interference management, mobility management and energy savings. In distributed LTE- RANs, in order to solve RAN control plane tasks ef fi ciently, various cells need to exchange a lot of messages over X2 interface. Moreover, deployment of a lot of small cells can lead to higher energy consumption. Besides, changing existing solutions or incorporating new solutions can lead to increase in capital expenditure (CAPEX) and operational expenditure (OPEX) of operators. Aforementioned issues raised need of simpli fi cation of control and management tasks and ef fi cient usage of radio resources in wireless networks such as LTE HetNets and IEEE 802.11 based Wireless Local Area Networks (WLANs). This could be achieved by adopting novel networking paradigms which could simplify the task of net- work management and control tasks, and allow faster deployment of newer solutions on top of existing network hardware equipment by software updates/upgrades. Software De- fi ned Networking (SDN) is one such revolutionary paradigm which makes networks more agile and fl exible by separation of data plane and control plane tasks. In this thesis work, in order to offer seamless mobility in multi-channel enterprise WLANs environment, a programmable WLAN architecture is used. Speci fi cally, a seam- less load-aware hand-off algorithm is proposed and its performance is evaluated on a Soft- ware De fi ned WLAN (SD-WLAN) testbed. Proposed load-aware hand-off algorithm not only offers seamless mobility in enterprise WLAN, it also effectively utilizes neighbor APs for offering load balance in the network. Besides, in order to provide programmable, fl exi- ble and scalable solutions for LTE-RAN, a Software De fi ned-LTE-Radio Access Network (SD-LTE-RAN) framework is proposed using OpenFlow enabled eNodeBs (OFeNBs). Proposed SD-LTE-RAN framework is implemented in NS-3 simulator with OpenFlow module and then used for evaluating the performance of various load balance algorithms and cell switch-off mechanisms. Exponential growth of mobile traf fi c can cause exorbitant load even in LTE networks. vii As User Equipments (UEs) are typically associated with a near-by cell (eNB), spatio- temporal variation in traf fi c demands makes the LTE networks suffer from load imbalance problem. Due to the distributed nature of eNB operation in LTE-RAN, traditional solu- tions to tackle load imbalance problem could lead to excessive overhead over X2 interface. Hence, managing densely deployed cells is very challenging in the existing distributed LTE RAN. In this thesis work, load imbalance issue is addressed by proposing a novel Quality of Service (QoS) Aware Load Balance (QALB) algorithm on the SD-LTE-RAN framework. For taking load balance decisions, the QALB algorithm considers loads of neighbor cells, QoS pro fi les of UEs and their expected throughputs w.r.t. neighbor cells. Unlike existing load balance algorithms, it does not change handover-offset parameters of cells to avoid ping pong handovers. The QALB runs in linear time in terms of number of cells and UEs, and hence it is suitable for real-time deployment of LTE networks. In various load bal- ance experiments conducted in NS-3, proposed QALB algorithm is able to maintain better QoS data rates ( > 80% of their con fi gured Guaranteed Bit Rates (GBRs)) for more than 70% of the cells in the network, while existing load balance solutions are able to do the same for only 50% of the cells in the network. In overall, the QALB algorithm is able to decrease the total network overload by 15% compared to existing solutions. We also eval- uated the QALB algorithm in mobility scenarios and identi fi ed that it is able to decrease average network overload by 10% compared to existing solutions. To evaluate the network wide fair load distribution, we de fi ned Load Balance Index (LBI) using Jain’s Fairness In- dex and found that QALB is also able maintain better LBI compared to existing solutions. Moreover, control ovehead of QALB and existing load balance algorithms is same on the proposed framework. In this thesis work, in order to reduce energy costs of the HetNets, we propose an in- terference and QoS aware cell switch-off strategy (IQ-CSOS) on the SD-LTE-RAN frame- work. The IQ-CSOS runs in polynomial time in terms of number of Macros, small cells and UEs. Unlike existing CSOSs, in selection of cells for switch-off and subsequent han- dover of their associated UEs to overlaying Macros, IQ-CSOS considers both traf fi c load of small cells and their cross-tier interference effect on the Macros. Hence, IQ-CSOS is able to provide higher energy savings. In evaluation, unlike existing works, we investi- gated both network energy costs and QoS satisfaction of sessions during CSOS decisions. In performance evaluation carried out in NS-3 under various test scenarios, it is observed that IQ-CSOS is able to provide 50-80% of network energy savings in terms of small cells energy consumption. Besides, it is able to provide 30% more energy savings compared to existing CSOSs with marginal affect on QoS in the network. Moreover, control ovehead of IQ-CSOS and existing CSOSs is same on the proposed framewor

Software Defined Wireless Networks: A Survey of Issues and Solutions

Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks

Maximizing Dual Cell Connectivity Opportunities in LTE Small Cells Deployment

Most of the LTE (Long Term Evolution) network operators are deploying low power small cells in hotspots like airports, shopping malls and corporate offices to meet increasing data demands. Since users are not deemed to fixed locations in such places, the network experiences uneven distribution of traffic load across the cells which degrades the average user throughput. This problem is even more severe if the deployment of small cells is unplanned. In order to address this, in this work, we propose two variant of small cell placement models: an optimal Femto placement with full power (OPT-FP) model and an opportunistic Femto placement with power control (OPPR-PC) model. These models incorporate a constraint which helps small cells providing dual cell connectivity (DCC) for as many number of users as possible and then schedule them jointly for improving their throughputs

Auto scaling of data plane VNFs in 5G networks

In order to meet the traffic demand from diverse next generation wireless network applications and exponentially increasing mobile subscriptions, various 5G network architectures are proposed by leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. Network slicing will be one of the 5G technologies that would support next-generation wireless applications over a shared network infrastructure. However, improper network slicing may lead to either over-provisioning or under-utilization of the underlying network infrastructure resources, especially the 5G core network. Over-provisioning of data plane components such as Serving Gateway (SGW) and Packet Data Network Gateway (PGW) can lead to higher CAPEX and OPEX to mobile operators. In this paper, we propose a novel auto-scaling approach called Bit rate Aware Auto Scaling (BAAS) that maintains a precise UE bit rate requirement in the network slices without over-provisioning of data plane resources

QoS Aware load balance in software defined LTE networks

Exponential growth of mobile subscribers and various application data traffic requirements cause exorbitant load in Long Term Evolution (LTE) networks. Telecom operators are considering small cells and Wi-Fi for handling this huge traffic demand. As User Equipments (UEs) are typically associated with a near-by cell (eNB), spatio-temporal variation in traffic demands makes the LTE networks suffer from load imbalance problem. Due to the distributed nature of eNB operation in LTE Radio Access Network (RAN), traditional solutions to tackle load imbalance problem could lead to excessive overhead over X2 interface. Hence, managing densely deployed cells is very challenging in the existing distributed LTE RAN. In this work, load imbalance issue is addressed by proposing a centralized Software Defined LTE RAN (SD-LTE-RAN) framework and a novel QoS Aware Load Balance (QALB) algorithm. For taking load balance decisions, the QALB algorithm considers loads of neighbor cells, QoS profiles of UEs and their expected throughputs w.r.t. neighbor cells. Unlike existing load balance algorithms, it does not change handover-offset parameters of cells to avoid ping pong handovers. The proposed framework and QALB algorithm are implemented in NS-3 simulator. In various load balance scenarios, proposed QALB algorithm is able to maintain better QoS data rates (>80% of their configured Guaranteed Bit Rates (GBRs)) for more than 70% of the cells in the network. While existing load balance algorithms are able to do the same for only 50% of the cells in the network. In overall, the QALB algorithm is able to decrease the total network overload by 15% compared to existing load balance algorithms. We also evaluated the QALB algorithm in mobility scenarios and identified that it is able to decrease average network overload by 10% compared to existing load balance algorithms. To evaluate the network wide fair load distribution, we defined load balance index (LBI) using Jain's Fairness Index. (C) 2016 Elsevier B.V. All rights reserved

Enhancing performance of victim macro users via joint ABSF and dynamic power control in LTE HetNets

The rise in mobile data traffic demands from indoor users (UEs) coupled with poor cellular coverage in indoor environments, triggering the growth of Femto cell deployments. However, due to densification of Femtos, frequency reuse one, and lack of coordination among Femtos in LTE HetNets, cell-edge users of Macro cells, who are in the vicinity of Femtos, get affected by cross-tier interference of the Femtos. In this work, we investigate how to improve performance of Victim Macro UEs (VMUEs). In order to provide better coordination between interfering Macro and Femtos, a centralized algorithm is proposed which performs Femto muting via joint Almost Blank Subframe (ABSF) and power control. During ABSF, Femto automatically adjusts its transmission power depending on the level of interference suffered by VMUEs. Our proposed scheme, called as RrMute, is compared with baseline schemes and the simulation results show that RrMute enhances the performance of VMUEs without jeopardizing the performance of Femto UEs in LTE HetNets

Load-aware hand-offs in software defined wireless LANs

In Wireless Local Area Networks (WLANs), providing seamless mobility and balancing load among Access Points (APs) are challenging issues due to simple signal strength based association and hand-off mechanisms employed at wireless clients. Extensions to Software Defined Networking (SDN) framework for wireless networks could help to address theses issues in an efficient and cost-effective manner with a central view of WLAN at the SDN controller. In this work, we propose a novel load-aware hand-off algorithm for SDN based WLAN systems which considers traffic load of APs in addition to received signal strength at wireless clients to solve load imbalance among APs and offer seamless mobility. We implemented the proposed algorithm on a small-scale prototype testbed and obtained improved network throughput for mobile clients as well as static clients compared to legacy hand-off algorithms used in WLANs