Empirical Experience and Experimental Evaluation of Open5GCore over Hypervisor and Container

Chang, Hung-Cheng; Qiu, Bo-Jun; Chen, Jyh-Cheng; Tan, Tze-Jie; Ho, Ping-Fan; Chiu, Chen-Hao; Lin, Bor‐Shyh

doi:10.1155/2018/6263153

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…[152], [153] Hypervisor Perf. [154], [155] Multi-Core Platform [156] Security [157]- [159] sHype [160] HyperSafe [161] HyperWall [158] SecVisor [162] ?…”

Section: B: Omnixmentioning

confidence: 99%

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

et al. 2022

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Scalable and flexible communication networks increasingly conduct the packet processing for Network Functions (NFs) in General Purpose Computing (GPC) platforms. The input/output (I/O)-intensive and latency-sensitive packet processing is challenging for the operating systems and hypervisors running on GPC platforms. This article surveys the existing enabling technologies and research studies on operating system and hypervisor aspects that directly influence the packet processing for NFs on GPC platforms. We organize this survey according to the main categories abstraction approach, memory access, and I/O strategy. We further categorize abstraction approach technologies and research studies into the categories operation systems, hypervisors, and containers. We partition the memory access category into the two subcategories of memory allocation and memory access, while we partition the I/O strategy category into the sub-categories I/O device virtualization and I/O device access. Our survey gives a comprehensive summary of the capabilities and limitations of the existing enabling technologies and researched approaches for abstraction, memory access, and I/O for NF packet processing. We outline critical future research directions for advancing NF packet processing on GPC platforms.

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“…[152], [153] Hypervisor Perf. [154], [155] Multi-Core Platform [156] Security [157]- [159] sHype [160] HyperSafe [161] HyperWall [158] SecVisor [162] ?…”

Section: B: Omnixmentioning

confidence: 99%

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

et al. 2022

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“…Recently, we have applied those technologies to establish a service-oriented architecture for the 4G/LTE&5G testbed located at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU) [ [23]. There are essential revolutions in the 5G's main components as described in Fig.1: The C-RANs (Centralized/Cloud-Radio Access Network), Open5GSON, Open5GCore, and applications.…”

Section: Applying Big Data ML and Sdn/nfv To 5g Platform At Nctumentioning

confidence: 99%

“…For example, in this architecture, a BBU (Base-Band Units) can connect to multiple RRUs (Remote Radio Heads) through optical fibers. The second revolution is in the Open5GCore, which is specifically illustrated as in research [23], in which Evolved Packet Core (EPC) is virtualized using hypervisor and container (Docker container) and its components such as P-GW, S-GW, Authorization and Accounting (AAA), home subscriber server (HSS), and mobility management entity (MME) are run on commodity hardware under the management of SDN/NFV orchestrations. As a result, in each component, the control plane is decoupled from the data plane, for example, the P-GW will contain the P-GW-C and P-GW-U.…”

Section: Applying Big Data ML and Sdn/nfv To 5g Platform At Nctumentioning

confidence: 99%

Enhanced Handover Clustering and Forecasting Models Based on Machine Learning and Big Data

Lin

Tung

et al. 2018

TMLAI

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In mobile networks, handover (HO) is one of the most important and complex KPIs (Key Performance Indicators), which directly affect to Quality of Service (QoS), Quality of Experience (QoE), and mobility performance. Moreover, its configuration parameters such as handover thresholds and handover neighbor lists are the key factors for implementing network optimization such as load balancing and energy saving. In a study before, the authors proposed clustering and forecasting models using ML algorithms and Time Series models to cluster, forecast, and manage the HO behavior of a huge number of cells. In this study, on the other hand, the authors firstly investigated more network KPIs to analyze their relationship with HO KPIs, and then, proposed new clustering, forecasting, and abnormal detection models that are expected to make them much more comprehensive. Finally, the performances of the proposed models were evaluated by applying them to a real dataset collected from the HO KPIs and other KPIs of more than 6000 cells of a real network during the years, 2016 and 2017. The results showed that the study was successful in identifying the relationship among network KPIs and significantly improving the performance of the HO clustering, forecasting, abnormal detection models. Moreover, the study also introduced the integration of emerging technologies such as machine learning (ML), big data, softwaredefined network (SDN), and network functions virtualization (NFV) to establish a practical and powerful computing platform for future self-organizing networks (SON).

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“…The RAN of 5G integrates new technologies such as massive MIMO and optical fiber to support high-speed connections for wide-area wireless connectivity to various types access devices, such as 3GPP (e.g., LTE-E-UTRAN), non-3GPP (e.g., WiMAX), Wireless Sensor Network (WSN). The Open5GCore component was described in the study [7]. Its elements such as S-GW, P-GW, home subscriber server (HSS), and mobility management entity (MME) are virtualized and run on commodity data center under the control of SDN/NFV orchestrations.…”

Section: Experimental Platformmentioning

confidence: 99%

“…This study, firstly, explores various emerging technologies, such as big data, ML, SDN/NFV, and cloud computing, and then integrates them to propose a comprehensive platform called Open5GMEC for 5G and vehicular networks. SDN/NFV are considered as the most critical technologies for 5G networks to provide the full power of programmability, interoperability, agility, short time to market, and low-cost solution by virtualizing network components and creating multiple logical end-to-end networks [7]. Moreover, recently, ML and big data have been exploited as the key technologies to empower computing components in 5G networks.…”

Section: Introductionmentioning

confidence: 99%

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

Lin

et al. 2018

TNC

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Mobile Edge Computing (MEC) is an emerging technology and an essential component of 5G networks to bring cloud services closer to users. That means data collection, storage, processing, computing, communication, and network control are implemented at network edges. MEC is expected to be able to satisfy a variety of delay-sensitive services and applications. On the other hand, the development of vehicles to everything (V2X) communication brings many requirements to future networks to guarantee full intelligence, automatic, and faster computation, management, and optimization to fulfill network QoS (quality of service) and QoE (quality of experience). To deal with those requirements, recently, softwaredefined networking (SDN), network functions virtualization (NFV), big data, and machine learning (ML) have been proposed as emerging technologies and the necessary tools for MEC and vehicular networks. This study aims to integrate those technologies to build a comprehensive architecture and an experimental framework for future 5G MEC called Open5GMEC. Moreover, the authors analyzed challenges and proposed relevant solutions for future vehicular communications in 5G networks. Finally, based on this framework, we successfully implemented several powerful ML-based applications for V2X such as object detection, network slicing, and migration services, which are executed at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).

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Empirical Experience and Experimental Evaluation of Open5GCore over Hypervisor and Container

Cited by 5 publications

References 19 publications

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

Enhanced Handover Clustering and Forecasting Models Based on Machine Learning and Big Data

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

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