Integration and management of Wi-Fi offloading in service provider infrastructures

A. Serdar Tan (MEF Author)##nofulltext##Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm

Intelligent service orchestration in edge cloud networks

The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform.This work has been partially funded by the EU H2020 5Growth Project (grant no. 856709), by MINECO grant TEC2017-88373-R (5G-REFINE), and Generalitat de Catalunya grant 2017 SGR, 1195

On Performance Analysis of Single Frequency Network with C-RAN

Centralized-RAN (C-RAN) is an architectural trend that uses resource sharing and a set of interference mitigation techniques to reduce capital and operational expenditures for mobile network operators (MNOs). One of the technical enablers of a C-RAN solution is single frequency network (SFN) that curbs the interference and allows MNOs to transmit over single frequency across coordinated cells. One of the main advantages of SFN is that it reduces the number of handovers between neighboring cells while improving the overall system performance. In contrast to previous approaches that demonstrate some of the most prominent C-RAN features, in this paper, we first investigate two different SFN deployment scenarios’ characteristics, benefits, and limitations. Second, we perform a simulation analysis of non-SFN and SFN without joint scheduling to observe signal to interference ratio heatmap distribution of the experimental test-site using similar system configurations. Finally, we perform an experimental analysis of joint scheduling in SFN based on coordinated inter baseband units scenario using C-RAN in a realistic environment. The experimental results are tested on a real operating site of a major MNO’s infrastructure in Turkey. Through experimental results, we show overall performance gains of SFN feature in terms of different key performance indicators that are obtained from coordinating remote radio units in an SFN cell. Finally, we discuss about the main takeaways, lessons learned, and challenges of the considered SFN implementation

Service-aware multi-resource allocation in software-defined next generation cellular networks

Şefik Şuayb Arslan (MEF Author)Network slicing is one of the major solutions needed to meet the requirements of next generation cellular networks, under one common network infrastructure, in supporting multiple vertical services provided by mobile network operators. Network slicing makes one shared physical network infrastructure appear as multiple logically isolated virtual networks dedicated to different service types where each Network Slice (NS) benefits from on-demand allocated resources. Typically, the available resources distributed among NSs are correlated and one needs to allocate them judiciously in order to guarantee the service, MNO, and overall system qualities. In this paper, we consider a joint resource allocation strategy that weights the significance of the resources per a given NS by leveraging the correlation structure of different quality-of-service (QoS) requirements of the services. After defining the joint resource allocation problem including the correlation structure, we propose three novel scheduling mechanisms that allocate available network resources to the generated NSs based on different type of services with different QoS requirements. Performance of the proposed schedulers are then investigated through Monte-Carlo simulations and compared with each other as well as against a traditional max-min fairness algorithm benchmark. The results reveal that our schedulers, which have different complexities, outperform the benchmark traditional method in terms of service-based and overall satisfaction ratios, while achieving different fairness index levels.WOS:000430793600019Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ1 - Q2ArticleUluslararası işbirliği ile yapılmayan - HAYIRMart2018YÖK - 2017-1

Joint iterative beamforming and power adaptation for MIMO ad hoc networks

In this paper, we present distributed cooperative and regret-matching-based learning schemes for joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment. Under the total network power minimization criterion, a joint iterative approach is proposed to reduce the mutual interference at each node while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching-based power minimization algorithms, transmit beamformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to converge to a pure strategy Nash equilibrium with high probability in the interference impaired network. The proposed cooperative and regret-matching-based distributed algorithms are also compared with centralized solutions through simulation results

Big Data Caching for Networking: Moving from Cloud to Edge

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Network

Big Data Meets Telcos: A Proactive Caching Perspective

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.Comment: 8 pages, 5 figure

Experimental validation of compute and network resource abstraction and allocation mechanisms within an NFV infrastructure

Proceedings of: IFIP/IEEE International Symposium on Integrated Network Management (IM), 17-21 May 2021, Bordeaux, France.5G supported capabilities (e.g., slicing) enable accommodating heterogeneous vertical services having their own requirements over a common cloud and transport infrastructure. In this context, the EU-H2020 5Growth project defines a service and infrastructure orchestration architecture to automatically deploy network services (NSes) fulfilling vertical demands. In this architecture, the Service Orchestrator (5Gr-SO), as a service provider, maps the vertical service needs into NS requirements (e.g., CPU, RAM, bandwidth, etc.). The 5Gr-SO interacts with an underlying infrastructure orchestrator referred to as 5Gr-RL. The 5Gr-RL, as an infrastructure provider, handles two main functions: i) abstraction of the resources exposed to the 5GrSO, and ii) fine-grained resource selection. Different interaction forms between both 5Gr-SO and 5Gr-RL arise differing in the exchanged abstracted information and resource allocation. We present two 5Gr-SO and 5Gr-RL interaction solutions stemming from two 5Gr-RL operational modes: Infrastructure Abstraction (InA) and Connectivity Service Abstraction (CSA). In the InA approach, the 5Gr-SO is granted with an aggregated view of the computing resources and a set of transport logical links between the cloud locations. In the CSA strategy, besides the aggregated view of the cloud resources, the logical links are associated to potential connectivity service types. Both InA and CSA strategies are presented describing their pros and cons. Moreover, the designed workflows (involving the devised abstraction and allocation algorithms) between the 5Gr-SO and 5Gr-RL entities are experimentally validated. Scalability studies are conducted upon two different cloud and transport infrastructure sizes in terms of the abstraction composition time, the expansion computation time, and total NS deployment time.Work supported in part by EU Commission H2020 5Growth project (Grant No. 856709), Spanish MICINN AURORAS (RTI2018-099178-B-I00) and Spanish MINECO 5G-REFINE (TEC2017-88373-R) projects and Generalitat de Catalunya grant 2017 SGR 1195

A multi-criteria decision making approach for scaling and placement of virtual network functions

This paper investigates the joint scaling and placement problem of network services made up of virtual network functions (VNFs) that can be provided inside a cluster managing multiple points of presence (PoPs). Aiming at increasing the VNF service satisfaction rates and minimizing the deployment cost, we use both transport and cloud-aware VNF scaling as well as multi-attribute decision making (MADM) algorithms for VNF placement inside the cluster. The original joint scaling and placement problem is known to be NP-hard and hence the problem is solved by separating scaling and placement problems and solving them individually. The experiments are done using a dataset containing the information of a deployed digital-twin network service. These experiments show that considering transport and cloud parameters during scaling and placement algorithms perform more efficiently than the only cloud based or transport based scaling followed by placement algorithms. One of the MADM algorithms, Total Order Preference by Similarity to the Ideal Solution (TOPSIS), has shown to yield the lowest deployment cost and highest VNF request satisfaction rates compared to only transport or cloud scaling and other investigated MADM algorithms. Our simulation results indicate that considering both transport and cloud parameters in various availability scenarios of cloud and transport resources has significant potential to provide increased request satisfaction rates when VNF scaling and placement using the TOPSIS scheme is performed.This work was partially funded by EC H2020 5GPPP 5Growth Project (Grant 856709), Spanish MINECO Grant TEC2017-88373-R (5G-REFINE), Generalitat de Catalunya Grant 2017 SGR 1195 and the National Program on Equipment and Scientifc and Technical Infrastructure, EQC2018-005257-P under the European Regional Development Fund (FEDER). We would also like to thank Milan Groshev, Carlos Guimarães for providing dataset for scaling of robot manipulator based digital twin service