• Journal of Platform Technology
• /
• v.11 no.5
• /
• pp.84-96
• /
• 2023

Network slicing refers to a technology that divides network infrastructure into multiple parts. Network slicing enables flexible network configuration while minimizing the physical resources required for network division. For this reason, network slicing technology has recently been developed and introduced in a form suitable for the 5G environment for efficient management of large-scale network environments. However, systematic analysis of network slicing research in the 5G environment has not been conducted, resulting in a lack of systematic analysis of the technology. Accordingly, in this paper, we provide insight into network slicing technology in the 5G network environment by conducting a comparative analysis of the technology. In this study's comparative analysis, 13 literatures on network slicing in the 5G environment was identified and compared and analyzed through a systematic procedure. As a result of the analysis, three network slicing technologies frequently used for 5G networks were identified: RAN (radio access network) slicing, CN (core network) slicing, and E2E (end-to-end) sliding. These technologies are mainly used for network services. It was confirmed that research is being conducted to achieve quality improvement and network isolation. It is believed that the results of this comparative analysis study can contribute to 6G technology research as a future direction and utilization plan for network slicing research.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2573-2589
• /
• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Information and Communications Magazine
• /
• v.33 no.6
• /
• pp.9-17
• /
• 2016

본고에서는 5G 망에서의 서비스 요구사항을 만족하기 위한 구조로서 Network Slicing 구조를 제안하고 세부 적용방안을 기술한다. 이를 위해 5G 서비스를 우선 정의하고 그에 따른 서비스 요구사항을 도출한 후, 이러한 요구사항과 관련해 현재 망의 문제점에 대해 기술하고 망 개선을 위한 기술 요구사항을 정립한다. 특히, 5G에서의 중요성이 높아질 것으로 전망되고 있는 'Network Slicing'의 필요성 및 개념에 대해 서술한다. Network Slicing에 대한 제조사들의 솔루션 동향, 3GPP 등 단체의 표준화 동향, APN 방식의 P-LTE/DECOR/RAN Slicing 등 관련 기술의 발전 동향을 포함한 5G Network Slicing 주요 기술 동향에 대해서 알아본다. 또한, Slice의 관리 및 BSS/OSS등과의 연계를 위한 통신사업자 입장에서의 플랫폼 요구사항을 정리한다. 5G Network Slicing을 충족하기 위한 주요 기술로 C/U plane 분리구조, 범용 서버를 활용한 NFV/SDN, Edge 기반의 분산된 수평적 네트워크, 데이터 오프로딩 및 지연시간 절감을 위한 Edge Computing 등을 들 수 있고 효율적인 자원 관리를 위한 Orchestration 등에 대해서도 알아본다. 이를 기반으로 하여 사업자 입장에서 5G Core Network 기술을 선도함은 물론이고 향후, 조기 상용화를 위한 진화 방향을 제시하고자 한다.

• Annual Conference of KIPS
• /
• 2019.10a
• /
• pp.211-212
• /
• 2019

Slicing Radio Access Network (RAN) can help in effectively utilizing the network bandwidth and to better manage the increasing traffic over interent. RAN slicing system discussed in this paper is based on an open-source slicing mechanism in which we write a JSON configuration file for slicing policy and send it to the FlexRAN controller. FlexRAN controlls the core networks (CNs) through OAI-RAN on the evolved packet core (EPC) component of this system. Each CN is responsible for handling a saperate RAN slice. The type of internet traffic is identified by the FlexRAN crontroller and is sent to the respective CN through OAI-RAN. CN handles the traffic according to the allocated bandwidth and in this way the internet traffic is sliced inside the EPC component.

• Journal of Internet Computing and Services
• /
• v.23 no.5
• /
• pp.17-23
• /
• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• Journal of information and communication convergence engineering
• /
• v.22 no.3
• /
• pp.189-198
• /
• 2024

This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks. This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types. Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies. This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands. The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation. Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios.

• Journal of Internet Computing and Services
• /
• v.22 no.5
• /
• pp.27-33
• /
• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.6
• /
• pp.9-14
• /
• 2009

In this paper, we proposed spreading code slicing technique for efficient transmitter detection in AT-DMB system with single frequency network. At the transmitter, the spreading code for transmitter identification inserted using slicing technique on forehead of null symbol and then transmitted. In this point, it requires high correlation characteristic spreading code. At the receiver, peak to peak value calculated by correlation process before signal demodulation. The transmitter information by proposed technique is employed to implement the single frequency network (SFN) which is proposed for solving a frequency inefficiency problem of the MFN. The results of the paper can be applied to wireless multimedia digital broadcasting system.

• Journal of information and communication convergence engineering
• /
• v.17 no.3
• /
• pp.185-190
• /
• 2019

The explosive growth of data traffic and services has created cost challenges for networks. Studies have attempted to effectively apply network slicing in fifth generation networks to provide high speed, low latency, and various compatible services. However, in network slicing using mixed-integer linear programming, the operation count increases exponentially with the number of physical servers and virtual network functions (VNFs) to be allocated. Therefore, we propose an efficient slice allocation method based on cluster technology, comprising the following three steps: i) clustering physical servers; ii) selecting an appropriate cluster to allocate a VNF; iii) selecting an appropriate physical server for VNF allocation. Solver runtimes of the existing and proposed methods are compared, under similar settings, with respect to intra-slice isolation. The results show that solver runtime decreases, by approximately 30% on average, with an increase in the number of physical servers within the cluster in the presence of intra-slice isolation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.794-815
• /
• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.