Data Mining Practical Machine Learning Tools and Techniques

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

A Survey on the Edge Computing for the Internet of Things

Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers, and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations toward potential solutions.

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Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions

To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges

The research and development (R&D) and the standardization of the 5th Generation (5G) mobile networking technologies are proceeding at a rapid pace all around the world. In this paper, we introduce the emerging concept of network slicing that is considered one of the most significant technology challenges for 5G mobile networking infrastructure, summarize our preliminary research efforts to enable end-to-end network slicing for 5G mobile networking, and finally discuss application use cases that should drive the designs of the infrastructure of network slicing.

End-to-end Network Slicing for 5G Mobile Networks

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices. key words: software-defined networking (SDN), network functions virtualisation (NFV), network virtualization, 5G, network slicing

Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Enabling early detection of Denial of service (DoS) attacks in network traffic is an important and challenging task because DoS attacks have become one of the most serious threats to the Internet. In this paper, we develop an IP packet size entropy (IPSE)-based DoS detection scheme in which the entropy is markedly changed when traffic is affected by an attack. Through our analysis, we find that the IPSE-based scheme is capable of detecting not only long-term attacks but also short-term attacks that are beyond the volume-based schemespsila ability to detect.

Detecting DoS attacks using packet size distribution

While distributed denial-of-service (DDoS) threats have been raising concerns for many years, a widely-acceptable solution is still absent. In this paper, we advocate a novel and promising solution for DDoS defense by using powerful cloud infrastructures as new battlefields. To explore this idea, we design and implement a cloud-based attack defense system called CLAD, which is running on cloud infrastructures as a network service to protect Web servers.

DDoS defense as a network service

A wireless communication apparatus includes a wireless communication device for multiplexing communication channels according to a predetermined method for carrying out wireless communication with a terminal, and carrying out communication with the terminal; and a control device for providing a plurality of program executing environments corresponding to the respective communication channels to operate in parallel or a in multitask manner, wherein, in each of the program executing environment, a process for providing a virtual wireless communication device interface corresponding to a corresponding communication channel and for carrying out Media Access control for communication via the virtual wireless communication device interface is executed, and the control device causes only the program being executed in the program executing environment to carry out communication via a virtual wireless communication device interface provided by the program being executing in the program executing environment.

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Papers

End-to-end Network Slicing for 5G Mobile Networks

Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Detecting DoS attacks using packet size distribution

DDoS defense as a network service

Wireless communication apparatus