Named Data Networking Forwarding Research Articles

Software-Defined Named Data Networking in Literature: A Review

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN.

A Contextual Multi-Armed Bandit approach for NDN forwarding

Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily growing, offering the potential to deliver intelligent, adaptable, and reliable NDN forwarding algorithms. In this context, this paper proposes efficient NDN forwarding strategies based on Contextual Multi-Armed Bandit (CMAB). Initially, we employ CMAB to address the challenge of forwarding Interest packets and introduce a new CMAB model tailored for NDN, dubbed CMAB4NDN. Subsequently, we construct the CMAB context using information derived from the content name and the network congestion state, which are then fed into the CMAB4NDN learning algorithm to decide on the best forwarding action. Further, we develop three CMAB strategies, namely Lin-ɛ-Greedy, Linear Upper Confidence Bound, and Contextual Thompson Sampling, and deploy them within our proposal. CMAB4NDN was implemented in ndnSIM, thoroughly evaluated, and compared with multiple state-of-the-art NDN forwarding algorithms across various scenarios. The obtained results confirm the relevance and superiority of our approach in terms of delay, throughput, and packet loss.

NDN Content Poisoning Mitigation Using Bird Swarm Optimization and Trust Value

Information-Centric Networking (ICN) is considered a viable strategy for regulating Internet consumption using the Internet’s underlying architecture. Although Named Data Networking (NDN) and its reference-based implementation, the NDN Forwarding Daemon (NFD), are the most established ICN solutions, their vulnerability to the Content Poisoning Attack (CPA) is regarded as a severe threat that might dramatically impact this architecture. Content Poisoning can significantly minimize the impact of NDN’s universal data caching. Using verification signatures to protect against content poisoning attacks may be impractical due to the associated costs and the volume of messages sent across the network, resulting in high computational costs. Therefore, in this research, we designed a method in NDN called Bird Swarm Optimization Algorithm-Based Content Poisoning Mitigation (BSO-Content Poisoning Mitigation Scheme). By aggregating the security information of entire routers along the full path, this system introduces the BSO to explore the secure transmission path and alter the content retrieval procedure. Meanwhile, based on the determined trustworthy value of each node, the BSO-Content Poisoning Mitigation Scheme can bypass malicious routers, preventing them from disseminating illicit content in the future. Additionally, the suggested technique can minimize content poisoning utilizing removing erroneous Data packets from the cache-store during the pathfinding process. The proposed method has been subjected to extensive analysis compared with the ROM scheme and improved performance justified in several metrics. BSO-Content Poisoning Mitigation Scheme is more efficient and faster than the ROM technique in obtaining valid Data packets and resulting in a higher good cache hit ratio in a comparatively less amount of time.

A Secure and Cached-Enabled NDN Forwarding Plane Based on Programmable Switches

Recently, the rapid development of software-defined networking (SDN) and programming protocol-independent packet processors (P4) provides a potential possibility for the deployment of Named Data Networking (NDN), which has aroused tremendous attention in academia. Existing P4-based NDN solutions mainly focus on how to describe the stateful forwarding characteristics of NDN in a programmable switch environment. However, the existing solutions still face many challenges such as cache availability and data confidentiality and do not support retransmission of interest packets and multicast forwarding of data packets. In this paper, we propose a new NDN forwarding plane based on programmable switches to address the above challenges. We design a decoupled cache module to avoid a large impact on the data plane forwarding performance when the cache function is enabled. Also, we enhance the design of the existing P4-based NDN forwarding plane to support interest retransmission and multicast forwarding of data packets. In addition, with the advantage of network programmability of P4 technology, we extend the content permutation algorithm and integrate it into the NDN forwarding plane, which makes our scheme support lightweight secure forwarding. Finally, we evaluate our scheme in the prototype system and conduct comparative experiments with representative schemes. Experiment results show that our scheme outperforms it in terms of content retrieval latency and received throughput and can support lightweight secure forwarding with low cost.

Smart Name Lookup for NDN Forwarding Plane via Neural Networks

Name lookup is a key technology for the forwarding plane of content router in Named Data Networking (NDN). To realize the efficient name lookup, what counts is deploying a highperformance index in content routers. So far, the proposed indexes have shown good performance, most of which are optimized for or evaluated with URLs collected from the current Internet, as the large-scale NDN names are not available yet. Unfortunately, the performance of these indexes is always impacted in terms of lookup speed, memory consumption and false positive probability, as the distributions of URLs retrieved in memory may differ from those of real NDN names independently generated by content-centric applications online. Focusing on this gap, a smart mapping model named Pyramid-NN via neural networks is proposed to build an index called LNI for NDN forwarding plane. Through learning the distributions of the names retrieved in the static memory, LNI can not only reduce the memory consumption and the probability of false positive, but also ensure the performance of real NDN name lookup. Experimental results show that LNI-based FIB can reduce the memory consumption to 58.258 MB for 2 million names. Moreover, as it can be deployed on SRAMs, the throughput is about 177 MSPS, which well meets the current network requirement for fast packet processing.

Cache management for large data transfers and multipath forwarding strategies in Named Data Networking

Named Data Networking (NDN) is a promising approach to provide fast in-network access to compact muon solenoid (CMS) datasets. It proposes a content-centric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are only dependent upon on-path caching. Such a design does not take advantage of the cached content available on the adjacent off-path routers in the network, thus reducing data transfer efficiency. In this work, we propose a software-defined, storage-aware routing mechanism that leverages NDN router cache-states, software defined networking (SDN) and multipath forwarding strategies to improve the efficiency of very large data transfers. First, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. In addition, we develop a centralized SDN-enabled control for the multipath forwarding strategy (S-MP), which leverages the global knowledge of NDN network states that distributes Interests efficiently. We perform extensive evaluations of our proposed methods on an at-scale wide area network (WAN) testbed spanning six geographically separated sites. Our proposed solutions easily outperform the existing NDN forwarding strategies. The D-MP strategy results in performance gains ranging between 10.4x to 12.5x over the default NDN implementation without in-network caching, and 12.2x to 18.4x with in-network caching enabled. For S-MP strategy, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, with in-network caching disabled and enabled, respectively. Further, we also present a comprehensive analysis of NDN cache management for large data transfers and propose a novel prefetching mechanism to improve data transfer performance. Due to the inherent capacity limitations of the NDN router caches, we use SDN to provide an intelligent and efficient solution for data distribution and routing across multiple NDN router caches. We demonstrate how software-defined control can be used to partition and distribute large CMS files based on NDN router cache-state knowledge. Further, SDN control will also configure the router forwarding strategy to retrieve CMS data from the network. Our proposed solution demonstrates that the CMS datasets can be retrieved 28%–38% faster from the NDN routers’ caches than existing NDN approaches. Lastly, we develop a prefetching mechanism to improve the transfer performance of files that are not available in the router’s cache.

LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks.

Named Data Networking (NDN) is a data-driven networking model that proposes to fetch data using names instead of source addresses. This new architecture is considered attractive for the Internet of Things (IoT) due to its salient features, such as naming, caching, and stateful forwarding, which allow it to support the major requirements of IoT environments natively. Nevertheless, some NDN mechanisms, such as forwarding, need to be optimized to accommodate the constraints of IoT devices and networks. This paper presents LAFS, a Learning-based Adaptive Forwarding Strategy for NDN-based IoT networks. LAFS enhances network performances while alleviating the use of its resources. The proposed strategy is based on a learning process that provides the necessary knowledge allowing network nodes to collaborate smartly and offer a lightweight and adaptive forwarding scheme, best suited for IoT environments. LAFS is implemented in ndnSIM and compared with state-of-the-art NDN forwarding schemes. As the obtained results demonstrate, LAFS outperforms the benchmarked solutions in terms of content retrieval time, request satisfactory rate, and energy consumption.

Modeling and improving named data networking over IEEE 802.15.4

Named Data Networking (NDN) is a promising architecture that aims to natively satisfy emerging applications such as the Internet of Things (IoT). Therefore, enabling NDN in real-world IoT deployments is becoming essential in order to benefit from Information Centric Networking (ICN) features. To design realistic NDN-based communication solutions for IoT, revisiting mainstream technologies such as low-power wireless standards may be the key. In this paper, we explore NDN forwarding over IEEE 802.15.4 following two steps. First, we mathematically model a broadcast-based forwarding strategy for NDN over constrained networks with the IEEE 802.15.4 technology in mind. The model estimates the number of frames transmitted and the mean round-trip time per request, under content popularity considerations. Second, based on mathematical and experimental observations of the broadcast-based forwarding at network level, we elaborate Named-Data Carrier-Sense Multiple Access (ND-CSMA), an adaptation of the Carrier-Sense Multiple Access (CSMA) algorithm used in IEEE 802.15.4. Results show that adaptations such as ND-CSMA may be reasonably envisioned to improve NDN efficiency with current IoT technologies.

An efficient and DoS-resilient name lookup for NDN interest forwarding

As a novel Internet architecture focused on data contents, Named Data Networking (NDN) has been proven to be of great value in supporting the Internet of Things, Edge computing, Blockchain, and other popular topics. They can benefit mainly from NDN's intrinsic properties, such as flexible multicasting, in-network caching, among several others. However, once NDN's forwarding plane suffers Denial of Service (DoS) attacks, the overall system performance would be affected significantly. In NDN data transmission, interest forwarding is the most time-consuming operation and thus opens a possible vector of DoS attacks. It is proposed in this paper a fast name lookup algorithm for NDN interest forwarding, which selects feature prefixes instead of lengths to filter out interest packets in NDN interest forwarding. Due to the excellent filtration with feature prefixes, the algorithm accelerates NDN forwarding processes. Compared with other existing solutions, the proposed algorithm shows more than 70% of time improvement in forwarding malicious interests, while remaining at the same performance level in standard cases.

Toward long-range adaptive communication via information centric networking

As Internet of Things (IoT) applications become more prevalent and grow in their use, a limited number ofwireless communication methods may be unable to enable dependable, robust delivery of information. It is necessary to enable adaptive communication and interoperability over a variety of wireless communication media to meet the requirements of large-scale IoT applications. This paper utilizes Named Data Networking (NDN), an up-and-coming Information-Centric Network architecture, to interconnect differing communication links via the network layer, and implements dynamic forwarding strategies and routing mechanisms which aid in the efficient dissemination of information. This work targets the creation of an interface technique to allow NDN to be transported via LoRa. This is acheived via the coupling of LoRa and WiFi using the NDN Forwarding Daemon (NFD) to create a universal ad hoc network. This network has the capacity for high range and multi-hop Device-to-Device (D2D) communication together with compatibility with other network communication media. Testing of the system in a real environment has shown that the newly created ad hoc network is capable of communicating over a several kilometer radius, while making use of the features provided by NDN to capitalize upon various links available to enable the efficient dissemination of data. Furthermore, the newly created network leverages NDN features to enable content-based routing within the LoRa network and utilize content-based routing techniques.

An Efficient Secured PIT Management and Attack Detection Strategy Enhanced by CSOA-DCNN Algorithm in a Named Data Networking (NDN)

Named Data Networking (NDN) is a developing Internet design that utilizes a new network communication model dependent on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves an important role of recording Interest packet information. In managing PIT, the issue of flow PIT measuring has been very challenging because of the huge use of long Interest lifetime especially when there is no adaptable replacement strategy, subsequently affecting PIT performance. Named Data Networking (NDN) might experience some emerging threats such as Interest Flooding Attacks (IFA). In this paper, we focus on the IFA that can seriously devour the memory resource for the Pending Interest Table (PIT) of each included NDN router by flooding a huge amount of malicious Interests with spoofed names. To extricate the pressure of PIT attacked by IFA, we propose a methodology of efficient Secured PIT management and attack detection strategy by using a cuckoo search optimization algorithmDeep convolutional neural network (CSOA-DCNN) algorithm in Named Data Network. The CSO algorithm initially utilizes a learning technique and afterward considers improved search operators and deep convolutional neural network architecture (DCNN) for classification. The network simulation tool is utilized to design and calculate PIT management. The results of the study on a 20 Gbps gateway trace shows that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s. The contribution of this study is significant for Interest packet management in NDN routing and forwarding systems.

Quality of Service provisioning in Named Data Networking via PIT entry reservation and PIT replacement policy

Named Data Networking (NDN) is a promising candidate of Future Internet Architecture (FIA) designed to solve many long-standing issues in current IP architecture such as security, mobility, and content distribution inefficiency. Pending Interest Table (PIT) is one of the essential building blocks of NDN, which is used in the successful delivery of data packets to the requesters. It helps to achieve intrinsic advantages such as anonymity, interest aggregation, multi-cast delivery, multi-path forwarding, and security. NFD (NDN Forwarding Daemon) currently does not constrain the size of PIT. Over-allocation of PIT does not contribute to network performance improvement rather helps in performance degradation. Therefore, there is a need to restrict PIT size. However, in the presence of bursty traffic, PIT may turn out to be full, and new incoming Interest packets may get dropped. In this case, NDN faces a new challenge to achieve Quality of Service (QoS) under bursty traffic. This paper presents two simple schemes to solve this problem via PIT entry reservation and PIT entry replacement policy. We present analytical models of both the schemes using Continuous Time Markov Chain (CTMC). We derive blocking and forced termination probability of Interest packets as performance metrics. The accuracy of the models is validated through simulation results.

SDN-Managed Provisioning of Named Computing Services in Edge Infrastructures

Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take “smart” decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.

Enhancing the SDTMA-NDN architecture for transferring the scientific data software using named data networking

Named Data Networking (NDN) is one of the promising future internet architectures, which focuses on the data rather than its location (IP/host-based system). NDN has several characteristics which facilitate addressing and routing the data: fail-over, in-network caching and load balancing. This makes it useful in areas such as managing scientific data across a distributed infrastructure. The Compact Muon Solenoid (CMS) experiment on the Large Hadron Collider (LHC) runs such an infrastructure and has a data access pattern amenable to Information-Centric Networking (ICN).In this paper, we propose SDTMA-NDN (Scientific Data Transfer Management Architecture using NDN) to distribute CMS’s software to worker-nodes and end-users. We provide several strategies to enhance the performance of NDN in transferring the scientific data. We build this on top of CERN Virtual Machine File System (CVMFS), a global, distributed filesystem designed to be used with cache-friendly workloads. CVMFS maintains its data by using content-addressable storage, which makes it suitable for NDN and a reasonable starting point for CMS. We examine the effect of in-network caching of NDN on the latency, how NDN provides transparent path selection to avoid high latency in the path, and how it balances several paths for retrieving the data. We compare SDTMA-NDN to the HTTP proxies used in the existing CVMFS-Content Distribution Network (CDN). Also, we propose two NDN forwarding strategies for retrieving the data; NDN Best-Path strategy (NDN-BP) and NDN Multi-path strategy (NDN-MP). SDTMA-NDN shows a better performance than the existing HTTP protocol in transferring the data. Further, it shows more resiliency and outperforms CVMFS-CDN under different network conditions.

Efficient Data Broadcast Mitigation in Multisource Named-Content Discovery for Vehicular CPS

Conventionally, a node or vehicle in the Named Data Networking (NDN) sends an interest packet to discover information and receives a single data packet in response. However, in the case of named data Vehicular Cyber-Physical System (VCPS), a vehicle may require content from multiple vehicles in the network. Therefore, NDN forwarding daemon must receive multiple data packets in response to a single interest. Moreover, the data broadcast storm worsens in the named data VCPS due to the broadcast nature of the wireless link. In this letter, we propose the data packet broadcast suppression method for any multisource content discovery in VCPS. The proposed scheme distributively selects the potential data forwarding vehicles based on the neighbor distance, node density, and the closeness to the data forwarding line of sight. Our simulation results show that the proposed scheme alleviates 55% data traffic, 17% average per-hop delay, and achieves 7% more data discovery ratio.

Forwarding Strategies in NDN-Based Wireless Networks: A Survey

Named Data Networking (NDN) is a promising Internet architecture paradigm of the future that adopts the Content Centric Networking (CCN) approach. Applications and users only care about the contents, while the Internet protocol (i.e., TCP/IP) focuses on communication by specifying and discovering the device location, which makes it more difficult and limiting. NDN supports the efficient distribution of the content, and secures the content rather than securing data channels. The forwarding mechanism in NDN-based wireless networks including MANET, WSN, VANET, and WMN are very important for efficient communication. NDN provides us a robust and simple communication mechanism. Two types of packets are exchanged over the networks in NDN forwarding: INTEREST and DATA. In this paper, a detailed survey is provided regarding forwarding strategies and forwarding issues in NDN/CCN based MANETs, VANETs, WSNs, and WMNs. Future challenges regarding forwarding in wireless environment are also highlighted.

Hierarchical Name-Based Mechanism for Push-Data Broadcast Control in Information-Centric Multihop Wireless Networks.

By design, Named Data Networking (NDN) supports pull-based traffic, where content is retrieved only upon consumer request. However, some of the use cases (i.e., emergency situations) in the Internet of Things (IoT) requires push-based traffic, where a producer broadcasts the data based on the emergency situation without any consumer request. Therefore, it is necessary to modify the existing NDN forwarding engine when designing for an IoT scenario. Although solutions are provided to enable push-based traffic in IoT, the main solutions in the current literature lack data broadcast control design. Moreover, the existing solutions use an additional interest messages exchange, which creates extra overheads in the network, thereby resulting in higher delay and lower throughput. In this paper, therefore, we propose a name-based push-data broadcast control scheme for IoT systems, and consider two scenarios, i.e., smart buildings and vehicular networks. The proposed scheme consists of a robust content namespace design, device namespace design, and minor amendments to the data packet format and unsolicited data policy of the forwarding engine as well. The evaluation is carried out for both scenarios. Simulation experiments show that the proposed scheme outperforms the recent proposed schemes in terms of total number of data packets processed in the network, total energy consumption, and average delay in the network by varying the number of data packets per 2 s and varying vehicle speed.

IoT Services Allocation at the Edge via Named Data Networking: From Optimal Bounds to Practical Design

Edge computing is a key paradigm to offload the core network and effectively process massive Internet of Things (IoT) raw data without sending them to the cloud. This paradigm normally relies on a set of purpose-built and pre-planned servers, which host storage and processing resources to provide IoT services close to the data sources, thus saving core network resources and offloading the remote cloud infrastructure. In this paper, we propose to turn the network edge into a dynamic, distributed computing environment that supports the provisioning of IoT services, by exploiting the recent evolution of named data networking (NDN), supporting both name-based data retrieval and computation. Specific name structure and novel NDN forwarding mechanisms are designed; a distributed strategy is also engineered to select the service executor among edge nodes, with the objectives to: 1) limit the raw IoT data traffic crossing the network and 2) allocate the service execution according to the nodes’ available processing resources. Numerical analysis shows that the performance of the proposed framework approaches the one of the optimal solution of a formulated integer linear programming problem. System-level ndnSIM simulations confirm that the proposal also outperforms the considered state-of-the-art benchmark solutions in terms of service provisioning time.

Reliable Detection of Interest Flooding Attack in Real Deployment of Named Data Networking

Named data networking (NDN) is a disruptive yet promising architecture for the future Internet, in which the content diffusion mechanisms are shifted from the conventional host-centric to content-centric ones so that the data delivery can be significantly improved. After a decade of research and development, NDN and the related NDN forwarding daemon implementations are now mature enough to enable stakeholders, such as telcos, to consider them for a real deployment. Consequently, NDN and IP will likely cohabit, and the future Internet may be formed of isolated administrative domains, each deploying one of these two network paradigms. The security question of the resulting architecture naturally arises. In this paper, we consider the case of denial of service. Even though the interest flooding attack (IFA) has been largely studied and mitigated through NACK packets in pure NDN networks, we demonstrate in this paper through experimental assessments that there are still some ways to mount such an attack, and especially in the context of coupling NDN with IP, which can hardly be addressed by current solutions. Subsequently, we leverage the hypothesis testing theory to develop a generalized likelihood ratio test adapted to evolve IFA attacks. Simulations show the relevance of the proposed model for guaranteeing the prescribed probability of false alarm and highlight the trade-off between detection power and delay. Finally, we consider a real deployment scenario where NDN is coupled with IP to carry HTTP traffic. We show that the model of IFA attacks is not very accurate in practice and further develops a sequential detector to keep a high detection accuracy. By considering data from the testbed, we show the efficiency of the overall detection method.

Packet Forwarding in Named Data Networking Requirements and Survey of Solutions

Named Data Networking (NDN) is the most promising paradigm recently conceived for future Internet architectures, where communications are driven by content instead of host addresses. To realize this novel paradigm, three novel tables, namely Content Store, Pending Interest Table, and Forwarding Information Base, are utilized in NDN forwarding plane. Designing and evaluating the quick enough forwarding plane with high capacity is a major challenge within the overall NDN research area. Since NDN was proposed in 2010, there have been many efforts focusing on this challenge and a rich literature has been developed. Unfortunately, there is a lack of the comprehensive sketch about the requirements of NDN forwarding plane and the study on various schemes proposed. Focusing on the above insufficiency, this survey gives the complete requirements and compares all the schemes proposed for NDN forwarding plane based on the data structure utilized. In addition, the survey also discusses some issues, challenges, and directions in future research. It is considered that designing a novel data structure to meet all requirements of the forwarding plane and studying on a better structure of content store play important roles, while discussing the necessity of a unified index, combining with other contents in NDN research and implementing a unified benchmark are also required in this domain.