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

Inferring persistent interdomain congestion

There is significant interest in the technical and policy communities regarding the extent, scope, and consumer harm of persistent interdomain congestion. We provide empirical grounding for discussions of interdomain congestion by developing a system and method to measure congestion on thousands of interdomain links without direct access to them. We implement a system based on the Time Series Latency Probes (TSLP) technique that identifies links with evidence of recurring congestion suggestive of an under-provisioned link. We deploy our system at 86 vantage points worldwide and show that congestion inferred using our lightweight TSLP method correlates with other metrics of interconnection performance impairment. We use our method to study interdomain links of eight large U.S. broadband access providers from March 2016 to December 2017, and validate our inferences against ground-truth traffic statistics from two of the providers. For the period of time over which we gathered measurements, we did not find evidence of widespread endemic congestion on interdomain links between access ISPs and directly connected transit and content providers, although some such links exhibited recurring congestion patterns. We describe limitations, open challenges, and a path toward the use of this method for large-scale third-party monitoring of the Internet interconnection ecosystem

Cloud Native Solutions for Orchestration in LTE Networks

Due to rapid growth in the use of handheld mobile devices and as a vital carrier of IoT traffic, mobile networks must undergo necessary infrastructure-wide revisions to alleviate the traffic explosion. In addition to data traffic, there has been a significant rise in the control signalling overhead due to more handover under small cell scenario and IoT. 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. Taking advantage of the advances in network virtualization, using NFV, SDN and the Cloud computing, the 5G system is making network slicing a reality. 3GPP as well as 5G Infrastructure Partnership by European Commission has come up with several 5G architectures that support network slicing. They classify slices into three categories (i) massive Machine-Type Communication (mMTC) for IoT services, (ii) Ultra-Reliable Low-Latency Communication (URLLC) for low latency communication, and (iii) extreme Mobile Broadband (xMBB), which requires high data rates. We propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of 5G mobile core in a production data center based on microservice architecture. The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required microservices for load balancing. We discuss the implementation of a Network Slicing Engine for autoscaling of data planes in LTE core network. We also propose a RAN Slicing framework which can schedule downlink and uplink transmissions of network slices. We focused on reducing latency of URLLC slice by applying scheduling policies. Our scheme ofiers strict requirements, especially in terms of latency and reliability as shown in our simulation results

Providing Low Latency Guarantees for Slicing-Ready 5G Systems via Two-Level MAC Scheduling

5G comes with the promise of sub-millisecond latency, which is critical for realizing an array of emerging URLLC services, including industrial, entertainment, telemedicine, automotive, and tactile Internet applications. At the same time, slicing-ready 5G networks face the challenge of accommodating other heterogeneous coexisting services with different and potentially conflicting requirements. Providing latency and reliability guarantees to URLLC service slices is thus not trivial. We identify transmission scheduling at the RAN level as a significant contributor to end-to-end latency when considering network slicing. In this direction, we propose a two-level MAC scheduling framework that can effectively handle uplink and downlink transmissions of network slices of different characteristics over a shared RAN, applying different per-slice scheduling policies, and focusing on reducing latency for URLLC services. Our scheme offers the necessary flexibility to dynamically manage radio resources to meet the stringent latency and reliability requirements of URLLC, as demonstrated by our simulation results

Load-aware dynamic RRH assignment in Cloud Radio Access Networks

Due to spatio-temporal variation of mobile subscriber's data traffic requirements, traffic load experienced by base stations present at different cell sites exhibit highly dynamic behavior in traditional cellular systems. This non-uniform and dynamic traffic load leads to under utilization of the base station computing resources at cell sites. Cloud Radio Access Network (C-RAN) is an innovative architecture which addresses this issue and keeps the Total Cost of Ownership (TCO) under safe limit for cellular operators. In C-RAN, the baseband processing units (BBUs) are segregated from cell sites and are pooled in a central cloud data center thereby facilitating shared access for a set of Remote Radio Heads (RRHs) present at cell sites. In order to truly exploit the benefits of C-RAN, the BBU pool deployed in the cloud has to efficiently serve clusters of RRHs (i.e., many-to-one mapping between RRHs and BBUs in the BBU pool) and thereby minimizing the required number of active BBUs. In this work, potential benefits of C-RAN are studied by considering realistic traffic loads of base stations deployed in urban areas by using statistical models. We propose a lightweight and load-aware algorithm, Dynamic RRH Assignment (DRA), which achieves BBU pooling gain close to that of a well known First-Fit Decreasing (FFD) bin packing algorithm. Using extensive simulations, we show that DRA consumes only 25% of time on average compared to FFD for the case of urban cellular deployment of 1000 RRHs. DRA slightly overestimates the required number of active BBUs as compared to FFD by 1.7% and 1.4% for weekdays and weekends, respectively

Poster: Scalable network slicing architecture for 5G

The diversified use cases of next-generationmobile networks can be realized by the key concept of Network Slicing. Scaling of network slices is required to cope with the resources needed for peak traffic demand. In this paper, we demonstrate scaling of network slices based on the type of network slice such as enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC) in order to ensure Service Level Agreement (SLA) guarantees of the network slices with the help of our proposed Network Slicing Profiler (NSP) and Network Slice Scaling Function (NSSF) in an ETSI MANO based network slicing framework

Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

Abstract Background Automated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans. Methods In this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman’s correlation was run to examine if the algorithm’s probability score was associated with the severity stages of IFTMH. Results Six hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman’s correlation coefficient of 0.15 was achieved between the algorithm’s probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied. Conclusions The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm’s probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs