Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.

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Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks

6G service-oriented space-air-ground integrated network: A survey

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.

Intent-based Control Loop for DASH Video Service Assurance using ML-based Edge QoE Estimation

The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2–5 times for the criterion of the number of customers who require high quality of experience of the service.

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Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking

Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.

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A Survey on Intent-Driven Networks

Software-Defined Networking (SDN) enhances the flexibility and programmability of networks by separating control plane and data plane. The logically centralized control mechanism makes the control plane vulnerable in both single and multiple controller scenarios. Malicious third parties can exploit vulnerabilities of reactive forwarding mode to launch distributed denial-of-service (DDoS) attacks against SDN controllers. Unfortunately, existing DoS/DDoS solutions under single controller can not afford effective performance under multiple controllers due to the absence of cooperative detection and mitigation. To solve the above problem, we propose a blockchain-based SDN-targeted DDoS defense framework (BSD-Guard) that can provide cooperative detection and mitigation mechanism to protect SDN controllers. BSD-Guard introduces a blockchain-based secure middle plane between control plane and data plane. The secure middle plane calculates the suspect rate of new flows based on the collected packets’ information and reports suspect lists to blockchain for immutably storing and sharing. Besides, the smart contract deployed on blockchain in advance constitutes collaborative defense strategies based on the suspect lists reported from multiple SDN domains. When receiving defense strategies, the secure middle plane converts them to specific flow table actions and installs actions into relevant switches. The experimental results indicate that BSD-Guard can efficiently detect DoS/DDoS attacks in multiple controllers scenario and issue precise defensive strategies near the source of attack by identifying the attack path.

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BSD-Guard: A Collaborative Blockchain-Based Approach for Detection and Mitigation of SDN-Targeted DDoS Attacks

Software Defined Network (SDN) is a new type of network architecture solution, and its innovation lies in decoupling traditional network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the development trend of the network in the future. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats.This paper focuses on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to changes of the network. Second, to mitigate this threat, we analyze the historical behavior of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source.The experimental results show that the scheme studied in this paper can effectively detect and mitigate DDoS attacks.

Distributed Denial of Service Defense in Software Defined Network Using OpenFlow

Next-generation communication networks can provide high capacity, low latency, and massive connections; however, they introduce novel challenges of management complexity, and traditional mathematical methods cannot well characterize the rational behavior of users. In this article, we pay attention to the methods of artificial intelligence (AI) and game theory. We first review the applications of machine learning (ML) and game theory models in wireless communications and summarize their advantages and disadvantages. After surveying the state of the art, in this article we propose a novel framework combining ML and game theory, which explores and exploits the benefits of the two disciplines. Finally, we apply our novel framework to solve the network selection problem in a 5G ultra-dense and heterogeneous network. Simulation results confirm the advantage of our presented framework on reducing the average delay of users.

Interactive Artificial Intelligence Meets Game Theory in Next-Generation Communication Networks

Users enjoy various kinds of services on the cost of huge bandwidth and energy consumption. The user quality of experience can be improved and transmission delay can be reduced by applying cache on the ultra-dense radio access network side. Most of the previous works aim at improving the throughput or the delay performance by using many caching strategies while ignoring energy consumption. In this paper, unlike conventional caching strategies, we focus on the joint optimization of delay and energy consumption by using the Nash bargaining game, which can ensure good fairness. Game-theoretic learning is introduced to determine the convergence of the algorithm. With a unique caching strategy, a new utility function is designed in consideration of the delay cost and energy consumption. Finally, simulation results verify a good convergence of the cache placement algorithm and the efficiency of the existing caching strategy.

Yanbo Song

Papers

A Survey on Intent-Driven Networks

BSD-Guard: A Collaborative Blockchain-Based Approach for Detection and Mitigation of SDN-Targeted DDoS Attacks

Distributed Denial of Service Defense in Software Defined Network Using OpenFlow

Interactive Artificial Intelligence Meets Game Theory in Next-Generation Communication Networks

Joint Delay and Energy Management for Cache-Enabled Ultra-Dense Cellular Networks: A Game-Theoretic Learning