A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements in an almost-ubiquitous manner. Although 3GPP introduced the concept of self-organization networks (SONs) in 4G and 5G documents to automate network management, even this progressive concept will face several challenges as it may not be sufficiently agile in coping with the immense levels of complexity, heterogeneity, and mobility in the envisioned beyond-5G integrated networks. In the presented vision, we discuss how future integrated networks can be intelligently and autonomously managed to efficiently utilize resources, reduce operational costs, and achieve the targeted Quality of Experience (QoE). We introduce the novel concept of self-evolving networks (SENs) framework, which utilizes artificial intelligence, enabled by machine learning (ML) algorithms, to make future integrated networks fully intelligent and automated with respect to the provision, adaptation, optimization, and management aspects of networking, communications, and computation. To envisage the concept of SEN in future integrated networks, we use the Intelligent Vertical Heterogeneous Network (I-VHetNet) architecture as our reference. The paper discusses five prominent communications and computation scenarios where SEN plays the main role in providing automated network management. Numerical results provide an insight on how the SEN framework improves the performance of future integrated networks. The paper presents the leading enablers and examines the challenges associated with the application of SEN concept in future integrated networks

Integrating THz Wireless Communication Links in a Data Centre Network

Modern data centre networks are increasingly made up of optical fibre connections and controlled by software defined networking. Recent advances in THz wireless technologies have paved the way for ultra-high bandwidth wireless communication, reaching the point where these wireless links can begin to compete with optical fibre links. This work investigates the feasibility of integrating wireless THz links in a data centre network, reporting on the performance that can be achieved at the physical layer, proposing an architecture for the data link layer and illustrating, through network emulation, how these wireless links could be used to reduce congestion in a network

Software-Defined Networks for Resource Allocation in Cloud Computing: A Survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation

深層学習に基づくパケット伝送ストラテジーを用いた知的ネットワーク制御に関する研究

Tohoku University加藤寧課