Underlying Network Resources Research Articles

Utilizing User Bandwidth Resources in Information-Centric Networking through Blockchain-Based Incentive Mechanism

Idle bandwidth resources are inefficiently distributed among different users. Currently, the utilization of user bandwidth resources mostly relies on traditional IP networks, implementing relevant techniques at the application layer, which creates scalability issues and brings additional system overheads. Information-Centric Networking (ICN), based on the idea of separating identifiers and locators, offers the potential to aggregate idle bandwidth resources from a network layer perspective. This paper proposes a method for utilizing user bandwidth resources in ICN; specifically, we treat the use of user bandwidth resources as a service and assign service IDs (identifiers), and when network congestion (the network nodes are overloaded) occurs, the traffic can be routed to the user side for forwarding through the ID/NA (Network Address) cooperative routing mechanism of ICN, thereby improving the scalability of ICN transmission and the utilization of underlying network resources. To enhance the willingness of users to contribute idle bandwidth resources, we establish a secure and trustworthy bandwidth trading market using blockchain technology. We also design an incentive mechanism based on the Proof-of-Network-Contribution (PoNC) consensus algorithm; users can “mine” by forwarding packets. The experimental results show that utilizing idle bandwidth can significantly improve the scalability of ICN transmission under experimental conditions, bringing a maximum throughput improvement of 19.4% and reducing the packet loss rate. Compared with existing methods, using ICN technology to aggregate idle bandwidth for network transmission will have a more stable and lower latency, and it brings a maximum utilization improvement of 13.7%.

Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN

Software-Defined Networking (SDN) revolutionizes the rigid operation and management mode of traditional vertical architecture. A centralized controller enables more granular control of the network based on a global view. However, the traffic forwarding mode in the traditional networks is still inherited, which cannot make full use of the underlying network resources to meet higher quality of service. In addition, the demand for massive data processing has also forced operators to seek the help of intelligent management solutions. In this paper, we propose a flowlet-level multipath routing scheme based on graph neural network to leverage the performance optimization benefits of parallel transport. The flowlet-based solution strikes a balance between flow transmission granularity and reordering, which can effectively improve the end-to-end transmission efficiency. Specifically, first, we design an adaptive flow splitting scheme to achieve multipath transmission when the network state changes dynamically by using the rule timeout mechanism that comes with the OpenFlow protocol. Secondly, we propose to use graph neural network to predict link delay to assist in adaptive flow splitting and forwarding path selection intelligently. Simulation results show that the graph neural network has good convergence and generalization in delay prediction, and the flow splitting mechanism based on rule timeout realizes adaptive flow splitting according to network state changes. The overall scheme outperforms existing typical solutions in terms of time overhead, end-to-end delay, flow completion time, and throughput. Meantime, experiments on different topologies verify the scalability of our scheme.

Evaluating an Application Aware Distributed Dijkstra Shortest Path Algorithm in Hybrid Cloud/Edge Environments

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra's routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

A MECS redistribution algorithm for SDN-enable MEC using response time and transmission overhead

The software-defined networks-enable mobile edge computing (SDN-enable MEC) architecture, which integrates SDN and MEC technologies, realizes the flexibility and dynamic management of the underlying network resources by the MEC, reduces the distance between the access terminal and computing resources and network resources, and increases the terminal's access to resources. However, the static distribution relationship between MEC servers (MECSs) and controllers in the multi-controller architecture may result in unbalanced load distribution among the controllers, which would degrade network performance. In this paper, a multi-objective optimization MECS redistribution algorithm (MOSRA) is proposed to decrease the response time and overhead. A controller response time model and link transmit overhead model are introduced as basis of an evolutionary algorithm which is proposed to optimize MECS redistribution. The proposed algorithm aims to select an available sub-optimizes individual by using a strategy based coordination transformation from Pareto Front. That is, when the master controller of the MECS is redistributed, both of the network overhead of the MECS to the controller and the response time of the controller to the MECS processing request are optimized. Finally, the simulation results demonstrate that the MOSRA can solve the redistribution problem in different network load levels and different network sizes within the effective time, and has a lower control plane response time, while making the edge network plane transmission overhead lower, compared with other algorithms.

Reliable Virtual Network Mapping Algorithm With Network Characteristics and Associations

In the existing research on reliable virtual network mapping, the relationship between network parameters is insufficiently analyzed, the network topology and historical mapping data are not fully utilized, resulting in a low virtual network mapping success rate and low utilization of the underlying network resources. To resolve this problem, this article proposes a reliable virtual network mapping algorithm based on network features and associations. Firstly, the network features related to reliable virtual network mapping are sorted out, and the underlying node reliability matrix is established based on historical data. Secondly, a Bayesian network-based inference model is constructed. Finally, based on the underlying node reliability matrix and virtual network features, two algorithms named NFA-TS and NFA-LR about reliable virtual network mapping were proposed. Compared with two algorithms named SVNE and VNE-SSM, the proposed algorithm shows the competitive performance on virtual network mapping success rate and the underlying network resource utilization.

Machine learning-assisted cross-slice radio resource optimization: Implementation framework and algorithmic solution

Network slicing is a central feature in 5G and beyond systems to allow operators to customize their networks for different applications and customers. With network slicing, different logical networks, i.e. network slices, with specific functional and performance requirements can be created over the same physical network. A key challenge associated with the exploitation of the network slicing feature is how to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-temporal traffic variability while ensuring that network slices can be provisioned and assured within the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a promising approach to cater for the complexity of resource allocation optimization among network slices. This paper tackles the description of a feasible implementation framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits within the presented implementation framework.

Guest Editorial: Seventh Edition of the IEEE JSAC Series on Network Softwarization and Enablers

5G is raising high expectations, offering new avenues for research, and paving the way to a safer and highperforming set of innovative use-cases. For use-cases related for instance to Internet of Things (IoT), self-driving vehicles, or unmanned aerial vehicles (UAVs), the programming and management of the underlying network resources will be carried out in a dynamic and scalable manner. In this vein, many enabling technologies are adopted by service providers, cloud providers and enterprises, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV).

EnFlow: An Energy-Efficient Fast Flow Forwarding Scheme for Software-Defined Networks

In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely- priority scheduling, routing, and re-routing. The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.

Towards Yo-Yo attack mitigation in cloud auto-scaling mechanism

Cloud platforms could automatically scale underlying network resources up and down in response to changes in the traffic load. Such an auto-scaling mechanism can largely enhance the elasticity and scalability of cloud platforms. However, it may introduce new security threats. For example, the Yo-Yo attack is a newly disclosed attack against the cloud auto-scaling mechanism. Attackers periodically send bursts of traffic to cause the auto-scaling mechanism to oscillate between the scale-up process and the scale-down process, which may result in significant performance degradation and economic loss. None of the prior work addressed the problem of mitigating such an attack. In this paper, we propose a Trust-based Adversarial Scanner Delaying (TASD) approach to effectively and proactively mitigate the Yo-Yo attack on the cloud auto-scaling mechanism. In TASD, we first propose to use the trust-based scheme to establish trust values for users, which is leveraged to identify adversarial requests. Trust values are updated by jointly considering the request mode and the auto-scaling status. Then, we aim to disable the condition under which the Yo-Yo attack takes effect by injecting certain delay, under the QoS constraints, to manipulate the response time of suspicious requests and deceive the attackers. Our extensive evaluation demonstrates that our approach achieves promising results, e.g., it can detect at least 80% Yo-Yo adversarial users and reduce more than 41% malicious scale-ups.

An Efficient Survivable Design With Bandwidth Guarantees for Multi-Tenant Cloud Networks

In cloud data centers (DCs), where hosted applications share the underlying network resources, network bandwidth guarantees have shown to improve predictability of application performance and cost. However, recent empirical studies have also shown that often DC devices and links are not all that reliable and that failures may cause service outages, rendering significant revenue loss for the affected tenants, as well as the cloud operator. Accordingly, cloud operators are pressed to offer both reliable and predictable performance for the hosted applications. While much work has been done on solving both problems separately, this paper seeks to develop a joint framework by which cloud operators can offer both performance and availability guarantees for the hosted tenants. In particular, this paper considers a simple model to abstract the bandwidth guarantees requirement for the tenant and presents a protection plan design which consists of backup virtual machines (VMs) placement and bandwidth provisioning to optimize the internal DC traffic. We show through solid motivational examples that finding the optimal protection plan design is highly perplexing, and encompasses several constituent challenges. Owing to its complexity, we decompose it into two subproblems, and solve them separately. First, we invoke a placement subproblem of the minimum number of backup VMs and then we explore the most efficient correspondence between backup and primary VMs (i.e., protection plan) which minimizes the bandwidth redundancy. Further, we study the design of various facets of such a plan by exploiting bandwidth sharing opportunities in multi-tenant cloud networks.

Self‐modeling based diagnosis of network services over programmable networks

SummaryIn this paper, we propose a multilayer self‐diagnosis framework for network services within the software‐defined networking and network functions virtualization environments. The framework encompasses 3 main contributions: (1) the definition of multilayered templates to identify the components to supervise across the physical, logical, virtual, and service layers. These templates are also finer‐granular, extendable, and machine‐readable; (2) a topology‐aware and a service‐aware self‐modeling module that takes as input the templates, instantiates them, and generates an on‐the‐fly diagnosis model, which includes the physical, logical, and the virtual dependencies of network services; (3) a topology‐aware and a service‐aware root cause analysis approach that takes into account the network services views and their underlying network resources observations within the aforementioned layers to automate the diagnosis of programmable networks. We also present extensive simulations to prove and evaluate the following aspects: a fully automated diagnosis model generation and a fine‐grained and reduced uncertainty diagnosis of the root cause for network services failures including those of their underlying resources. We include in this extended paper relevant state‐of‐the‐art on topology and service aware diagnosis approaches for different types of network technologies, a deeper insight of our approach and problem formalization, and additional results.

Expanded Android Application for Openflow-based Visual Interface in Software-Defined Network

In the past year, the northbound interface application has become a hot topic in the Software-Defined Network technology. It offers an opening method and makes people able to develop own business application upper the SDN. In this paper, we propose and evaluate a visual plane based on android phone application to present an explicit watch of the state and working performance of the Openflow-based underlying network topology in Software-Defined Network. The impacts of the designing architecture are evaluated in the form of software simulation and actual operating effects of the android application that we have designed. Through the interface opened by the floodlight controller to the upper business application, we are aimed at making it able to call the underlying network resources and inquiry its status information conveniently. So this paper wants to achieve the unified dispatching by elaborated methods and transplants the idea to the android application. By the way of the software programming, we could make the android phone to probe the southbound network resources through the northbound interface whose connotation we will expand. Between the northbound interface and the android application, we introduce a data center and select the mysql database to implement its function. Through the technology framework we built, we are able to use an android phone to access the status of the underlying devices and monitor the software defined network better aiming at providing better service. And we judge its function from the perspective of overall feasibility and stability not only through the system performance but also sound experiment effects. Finally, we can use any android phone to view about the topology of the network effectively and simply.

Entrepreneurial performance of early-stage ventures: dynamic resource management for development and growth

Effective allocation of resources for the development and growth of early-stage ventures (ESVs) is a continuous challenge for their equity stakeholders. This paper explores the relevance of intellectual capital (IC) as a critical resource for entrepreneurial performance in ESVs driven by new knowledge development and technological innovation. An interdisciplinary literature review is conducted to examine prior studies on entrepreneurial performance and resource management for ESVs. A conceptual framework is developed using a taxonomy of IC that explains how the underlying network resources enhance the development and growth of ESVs. Utilizing a multiple case study approach, this paper suggests a dynamic process of resource allocation into heterogeneous IC that are optimally coordinated by the founders during an ESV’s development and growth through interaction with external networks. This approach in resource allocation complements the staged entrepreneurial finance and investment strategy for ESVs and their subsequent developments.

M2M Communication in Virtual Sensor Network for SHAAL

Machine-to-Machine (M2M) communication has led to a new paradigm of Internet of Things (IoT). The future of M2M communication in smart home lies in the aggregation and allocation of resources and service provisioning of diverse applications using different radio technologies. M2M communication may operate on the virtual sensor network to provide independent applications running on heterogeneous network simultaneously. M2M is going to play a major role in the area of Smart Home and Ambient Assisted Living (SHAAL) providing assistance to the elderly people with smart sensors that monitor the home environment and provides aid health monitoring to human requiring medical assistance. The current state of the art frameworks are dedicated to specific applications with the support of single radio network with limited service provisioning options. However, in order to fully exploit the resources in this paper we present a service provisioning framework realization of M2M in virtual sensor network for SHAAL, which allows independent parties to work together in a secure and reliable manner. In addition to this, the framework is designed to include benefits of Service Oriented Architecture (SOA) and Resource Oriented Architecture (ROA) along with the use of different low power, low data rate protocols. A middleware is used as a platform to link the underlying virtual networks with various applications. The network virtualization approach is adopted to design an efficient middleware framework that can effectively discover and manage the underlying network resources and provide services at home gateway. The framework will be used as the basis for the development of the SHAAL networked system.

An Adaptive Channel Reconfiguration Algorithm for Multi-Channel Multi-Radio Wireless Mesh Networks

The maximum utilization of Multi Channel - Multi Radio Wireless Mesh Networks (WMNs) can be achieved only by intelligent Channel Assignment (CA) and Link Scheduling (LS). A common CA and LS may not be optimal, in terms of utilization of underlying network resources, for every traffic demand in the network. Using the best CA and LS for every traffic demand results in channel reassignments which in turn lead to traffic disruption in the network. This makes WMNs very unreliable. In this paper, we present a simple, general, and efficient framework to quantitatively evaluate a reconfiguration policy, based on the two conflicting objectives, namely maximizing network utilization and minimizing traffic disruption. Then we propose a reconfiguration algorithm called Clustered Channel Assignment Scheme (CCAS), based on clustering of similar traffic matrices. We demonstrate the effectiveness of CCAS which mainly depends on the correlation between successive traffic matrices through extensive simulation studies.

Overlay multicast routing algorithm with minimum overlay cost

Compared with IP multicast, overlay multicast usually consumes more underlying network resources. Therefore, utilizing the underlying network resources effectively has some practical significance when constructing the multicast delivery tree based on overlay network. The concept of overlay cost was proposed, and the overlay multicast routing problem was defined to find a degree and delay constrained spanning tree with the minimum overlay cost for an undirected complete graph. The goal of the solution was to satisfy the applications' requirements and the end hosts' performance needs, and to make the consumption of underlying network resources minimum at the same time. A heuristic genetic algorithm for this problem was given, and the simulation results verify the effectiveness of the algorithm.

Using policy-based MPLS management architecture to improve QoS on IP network

Multi-Protocol Label Switching (MPLS) is in the process of standardization by the Internet Engineering Task Force (IETF). It is regarded as a technology for traffic engineering and QoS in IP-networks. We proposed an IETF Policy-based Network Management Framework and policies with MPLS specific classes. It uses a three-level policy architecture, which includes managing on device, network, and service level using policies for supporting Inter-serve and Diff-serve based end-to-end QoS in the Internet. A prototyping of policy-based management system for MPLS Traffic Engineering is operating on MPLS network elements. Several experiments illustrate the efficiency and feasibility in this architecture. The results show it can reduce the time of the setup of MPLS traffic engineering tunnel over hops and MPLS traffic engineering tunnel deletion. The proposed integrated policy based management architecture will allow network service providers to offer both quantitative and qualitative services while optimizing the use of underlying network resources.

A generic architecture for autonomic service and network management

As the Internet evolves into an all-IP communication infrastructure, a key issue to consider is that of creating and managing IP-based services with efficient resource utilization in a scalable, flexible, and automatic way. In this paper, we present the Autonomic Service Architecture (ASA), a uniform framework for automated management of both Internet services and their underlying network resources. ASA ensures the delivery of services according to specific service level agreements (SLAs) between customers and service providers. As an illustrative example, ASA is applied to the management of DiffServ/MPLS networks, where we propose an autonomic bandwidth sharing scheme. With the proposed scheme, the bandwidth allocated for each SLA can be automatically adjusted according to the measured traffic load and under policy control for efficient resource utilization, while SLA compliance over the network is always guaranteed.

Inference and labeling of metric-induced network topologies

The development and deployment of distributed network-aware applications and services require the ability to compile and maintain a model of the underlying network resources with respect to one or more characteristic properties of interest. To be manageable, such models must be compact; and to be general-purpose, should enable a representation of properties along temporal, spatial, and measurement resolution dimensions. In this paper, we propose MINT - a general framework for the construction of such metric-induced models using end-to-end measurements. We present the basic theoretical underpinnings of MINT for a broad class of performance metrics, and describe PERISCOPE, a Linux embodiment of MINT constructions. We instantiate MINT and PERISCOPE for a specific metric of interest - namely, packet loss rates - and present results of simulations and Internet measurements that confirm the effectiveness and robustness of our constructions over a wide range of network conditions.

A New Access Control Solution for a Multi-Provider Wireless Environment

The fundamental goal in future 4G mobile multi-service networks is to provide an efficient mobile computing environment. We presume that future terminals will be equipped with multiple heterogeneous wireless interfaces. We want to enable each user to seamlessly move between different providers' networks. Besides seamless roaming, a key consideration is also devoted to quality-of-service provision. In this article, we propose a model and an architectural framework for supporting quality of service in the context of interconnected multi-provider wireless systems. The proposed integrated COPS-based management and RADIUS-based access control architecture will allow providers to offer multimedia services while optimizing the use of the underlying network resources. We suggest new concepts and protocols to provide solutions to these challenges and describe the French national research project named MMQoS aiming on building such future networks.