Content Routing Research Articles

2TierEdge-Defense: a cascaded defense framework with rule-based LSTM for NCIFA in NDN

ABSTRACT Non-Collusive Interest Flooding Attacks (NCIFA) disrupt Named Data Networks' (NDNs) seamless communication and content distribution through QoS degradation. This work proposes 2TierEdge-Defense: a framework based on Long Short Term Memory (LSTM) to detect NCIFA in NDN at the edge content routers. The framework has been evaluated on large-scale Rocketfuel topologies for AT&T Internet Service Provider. The 2TierEdge-Defense framework consists of attack detection and mitigation modules with detection at edge router and their interfaces. During offline training and evaluation, the 2TierEdge-Defense framework can detect NCIFA with 0.92 (F1-score) and 0.84 (F1-score) at edge routers and their interfaces, respectively, with cascaded F1-score of 0.9872. Upon detection, the mitigation strategy in the 2TierEdge-Defense framework can improve the QoS metrics as quickly as in 0.45 seconds with an overall F1 score as high as 0.9872. The 2TierEdge-Defense is evaluated for scalability topology scenarios to ensure better performance when deployed on NDN content routers with a cascaded miss rate of 0.1 and a false alarm rate of 0.07.

Hierarchical, Attribute and Hash-Based Naming and Forwarding Aided Smart Campus of Things

In order to provide universal ability to access information and communication among Internet-connected devices, the Sustainable Internet of Things (IoT) is on a mission to bring all objects or devices under one roof. Future Internet architecture, especially Information-Centric Networking (ICN), can easily handle the connectivity offered and information created by the massive amount of devices to make it as sustainable IoT applications. Named Data Networking (NDN), one of the several future Internet designs that employ ICN as its foundation, shows promise. NDN integration with IoT-based applications gives solutions to numerous problems. However, this fusion makes accessing the IoT content easier, provided that an effective naming scheme is created to execute this operation. In this work, we build an innovative NDN-based naming scheme (NDN–NS) and put it into practise for consumer, producer, and content routers using our own secure forwarding schemes (NDN–NFS). Due to its scalability, heterogeneity, and security needs, IoT-based Smart Campus (IoT-SC) scenarios are taken into consideration for design and evaluation. We give a complete activity list based on NDN–NS that is split into two communication models (PusH Type Communication (PHTC) and PulL Type Communication (PLTC)) that can be applied to any IoT application. In terms of interest satisfaction rate (ISR), delay, and number of transmissions, we compare the NDN–NFS to legacy NDN. The outcomes demonstrate that NDN–NFS outperforms classic NDN in terms of performance and efficiency.

A bargain game theory assisted interest packet forwarding strategy for information centric network

In Information Centric Network (ICN), when implemented using NDN (Named Data Network) architecture, a client generates an ‘interest’ packet to retrieve a content. The interest packet carries the name of the searched content and may pull the data from any intermediate router that holds the requested content. An efficient interest packet forwarding mechanism is highly desirable in ICN to make the content retrieval faster. This paper describes an interest forwarding technique aiming at minimizing content retrieval time and network overhead. The process of interest forwarding is modelled as bargain game with data seeker as seller and content provider as buyer. Intermediate Content Routers (CRs) between the consumer and the actual producer is classified as either capable or incapable CRs represented as cCR and iCR respectively. Initially all CRs holds certain currency value based on their attributes. The cCRs are capable of serving interests and they are rewarded with a pre agreed trading price. The iCRs on the other hand are not capable of serving any content and hence they are rewarded by a fraction of the trading price based on the assistance provided in forming the data transmission path. To evaluate the strength of the new scheme the proposed bargain game theory (BGT) based technique is integrated with three existing protocols, namely, the Cache Less for More (CL4M), Characteristic Time Routing (CTR), and Probability based caching (pCache). The performance of new variations of these protocols are compared with their base versions. The simulation is performed in ns-3 based ndnSim-2.0. Performance parameters observed in the simulation are, content search latency, server hit ratio, network load, overhead and throughput. Simulation observation reveals that the integration of the proposed scheme significantly improves the performance in terms of aforementioned parameters.

A Cross-Layer Green Information-Centric Networking Design Toward the Energy Internet

To address the energy-efficiency issue in Information-Centric Networking (ICN), this article proposes a novel Green ICN design, which adapts the power consumption of network nodes to the optimized utilization level proportionally. By learning over the consumers’ interactive data traffic pattern/behavior, we introduce a new concept of cross-layer power adaption conducted through dynamically adjusting link rate corresponding to content popularity to reduce the wasteful power consumption of Content Routers (CRs). We also develop a controlling policy for each content provider to map its status to the most suitable operating mode to diminish power consumption. Moreover, we propose a smart Selective Caching Scheme (SCS) so that the caching portion in a CR's cache memory is adjusted according to content popularity and available spaces of two customized content cache spaces, namely hot and cold caching queues, for storing popular and unpopular content objects. This scheme can further decrease the power from caching since it is diminished when the traffic load is reduced via the proposed CRs' adaptive mechanism. The evaluation results with practical insights in several distinct scenarios show that the proposal can provide considerably higher energy efficiency and network performance at the same time, typically achieving at least 20% power-saving with a higher hop reduction ratio, compared to existing Internet designs with relevant state-of-the-art caching strategies.

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.

SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks

ABSTRACT A consumer in Information Centric Network (ICN) generates an Interest packet by specifying the name of the required content. As the network emphasizes on content retrieval without much bothering about who serves it (a cache location or actual producer), every Content Router (CR) either provides the requested content back to the requester (if exists in its cache) or forwards the Interest packet to the nearest CR. While forwarding an Interest packet, the ICN routing by default does not provide any mechanism to predict the probable location of the content searched. However, having a predictive model before forwarding may significantly improve content retrieval performance. In this paper, a machine learning (ML) algorithm, particularly a Support Vector Machine (SVM) is used to forecast the success of the Interest packet. A CR can then send an Interest packet in the outgoing interface which is forecasted successful. The objective is to maximize the success rate which in turn minimizes content search time and maximizes throughput. The dataset used in is generated from a simulation topology designed in ndnSim comprising 10 K data points having 10 features. The linear, RBF and the polynomial kernel (with degree 3) are used to analyze the dataset. The polynomial kernel shows the best behavior with 98% accuracy. A comparative retrieval time with and without ML demonstrates around 10% improvement with SVM enable forwarding compared to normal ICN forwarding.

NB-Cache: Non-Blocking In-Network Caching for High-Performance Content Routers

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

Mobility Management in Vehicular Named Data Networking

The inherent features of the vehicular ad hoc network (VANET) make it challenging to achieve efficient data delivery. As a new data-centric communication paradigm, the named data networking (NDN) may help to enhance the efficiency of data retrieval in VANET, namely vehicular NDN (VNDN). However, in VNDN, vehicles assume roles of providers, content routers, and consumers, and high mobility leads to frequent reverse-path disruptions and location changes of providers and consumers. This ultimately causes frequent failures of vehicular data delivery. In this letter, we propose a mobility management framework in VNDN to address the reverse-path disruption issue and the provider and consumer mobility support issues. The simulation results show that this framework effectively improves the success rates of data delivery.

Heterogeneous CCN Cache Allocation Strategy With Collaboration Support

Content-Centric Networking (CCN) is regarded as a promising network architecture which has received the distinct interest from many global research communities thanks to its friendly structure, especially its caching technology has attracted the most widespread attention. However, the current caching strategies have some limitations, such as redundant content copies, low cache utilization rate and unbalanced node load. In order to further optimize cache, this paper investigates the classical cache allocation problem, i.e., distributing the cache capacity across some Content Routers (CRs) under a constrained and fixed total cache budget. At first, both topology information and traffic characteristics as two factors to determine the importance of CR, where the evaluation of network topology depends on degree centrality, betweenness centrality and closeness centrality while that of traffic characteristics depends on node load and interest preference. In particular, given the data redundancy due to the high-dimensional feature, increasing the complexity of computation, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to reduce the dimensions of data. Then, for the CR with cache capacity, its Content Store (CS) is divided into collaborative region and non-collaborative region by a heuristic algorithm to facilitate the optimal performance, and the CS with collaboration needs to interact the cached information with its neighborhoods. Finally, the performance evaluation is driven by the real dataset over GTS-CE topology. The experimental results reveal that the proposed collaboration-supported cache allocation strategy is more efficient than two baselines, i.e., increasing cache hit ratio by 7.79%, increasing cache utilization rate by 11.31%, decreasing routing delay by 49.38% and decreasing load balance by 50.98%.

CCJRF-ICN: A Novel Mechanism for Coadjuvant Caching Joint Request Forwarding in Information Centric Networks

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra's shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures.

An Incrementally Deployable IP-Compatible-Information-Centric Networking Hierarchical Cache System

The major advantage of information-centric networking (ICN) lies in in-network caching. Ubiquitous cache nodes reduce the user’s download latency of content and the drain of network bandwidth, which enables efficient content distribution. Due to the huge cost of updating an entire network infrastructure, it is realistic for ICN to be integrated into an IP network, which poses new challenges to design a cache system and corresponding content router. In this paper, we firstly observed that the behavior pattern of data requests based on a name resolution system (NRS) makes an ICN cache system implicitly form a hierarchical and nested structure. We propose a complete design and an analytical model to characterize an uncooperative hierarchical ICN caching system compatible with IP. Secondly, to facilitate the incremental deployment of an ICN cache system in an IP network, we designed and implemented a cache-supported router with multi-terabyte cache capabilities. Finally, the simulation and measurement results show the accuracy of proposed analytical model, the significant gains on hit ratio, and the access latency of the hierarchical ICN cache system compared with a flat cache system based on naming routing, as well as the high performance of the implemented ICN router.

Efficient Coded-Block Delivery and Caching in Information-Centric Networking

Information-centric networking (ICN) provides request aggregation and caching strategies that can improve network performance by reducing content server loads and network traffic. Incorporating network coding into ICN can offer several benefits, but a consumer may receive the same coded block from multiple content routers since the coded block may be cached by any of the content routers on its forwarding path. In this paper, we introduce a request-specific coded-block scheme to avoid linear dependency of blocks that are utilizing in-network caching. Additionally, a non-cooperative coded caching and replacement strategy is designed to guarantee that the cached blocks can be reused. Our experimental results show that the proposed scheme has superior performance to conventional CCN and two network coding-based ICN schemes.

Efficient Road Safety Data Retrieval in VNDN

With the substantial growth in vehicle population, the road safety problems, such as congestions and accidents, have become serious. To improve driving safety, efficient and rapid road safety data retrieval becomes a significant measure. As a new type of network, the named data networking (NDN) can effectively improve the data retrieval efficiency. Therefore, NDN might help improve the road safety data retrieval efficiency if it is introduced to the vehicular network, namely vehicular NDN. However, in the vehicular network, each vehicle works as a content router and high mobility of vehicles leads to frequent changes in network topology, which makes it hard to maintain forwarding information and achieve aggregation. Moreover, a large number of vehicles are involved in reverse paths, which leads to frequent reverse path disruptions and even data retrieval failures. To reduce the data retrieval latency and cost and improve the success rate, we propose an efficient road safety data retrieval approach that achieves push-based and pull-based data retrieval. In the approach, a forwarding network is constructed to improve the network stability. Based on the forwarding network, only a small number of vehicles are involved in pushing data, and consumers employ aggregation to pull data without depending on forwarding information. Moreover, the disrupted reverse paths can be effectively repaired to guarantee the successful receipt of data. Finally, this method is evaluated, and the experimental results show that it achieves the aforementioned objectives.

An effective service-oriented networking management architecture for 5G-enabled internet of things

The development of a 5G-enabled Internet of Things has led to a dramatic increase in network traffic load, which has presented tremendous challenges to network management. In this paper, a service-oriented network architecture is proposed to support the effective management of 5G-enabled IoT systems. This architecture effectively reduces the traffic load and simplifies network management by introducing a service aggregation and caching (SAaC) scheme. Specifically, SAaC first breaks through the data-centric network architecture by converting data into services. Then, SAaC significantly reduces traffic load and energy consumption by service aggregation. Finally, SAaC introduces service caching, and each content router caches new services locally after aggregating received services so that user requests are handled at the network layer. Experimental results demonstrate that compared with traditional solutions, the SAaC scheme improves the request response time by 20.52%–56.09%, reduces the traffic load by 10.85%–37.67%, and reduces energy consumption by more than 50%.

Hierarchical Information-Centric Networking Framework

In information-centric networking, one content publishing operation involves a great number of content routers, and different name prefixes pollute forwarding tables and result in explosion of forwarding entries. Moreover, flooding is employed to achieve information-centric communications, and this flooding incurs huge costs. Taking these issues into account, this paper proposes a hierarchical information-centric networking framework and aims to reduce the content communication costs and latency. This framework proposes the hierarchical information-centric networking architecture and name structure to achieve forwarding aggregation and avoid forwarding table pollution. This framework is evaluated, and the data show that it reduces the content communication cost and latency by nearly 49.2% and 9.5%, respectively.

A Heuristic Transferring Strategy for Heterogeneous-Cached ICN

The in-network caching is a considerably significant feature of Information-Centric Networking (ICN), especially the heterogeneous-cached ICN has been widely investigated since it accords with the actual network deployment. For the heterogeneous-cached ICN, although there have been many proposals to improve network performance, it is very difficult for these approaches to reach the optimal network performance with multiple metrics consideration. Therefore, in this paper, we propose a heuristic transferring strategy which selects some Content Routers (CRs) and combines them to facilitate the optimal network performance under a constrained total cache budget. At first, we quantify energy consumption, CR load, cache hit ratio and throughput, because the optimal network performance depends on four objects, i.e., minimizing energy consumption and CRs load as well as maximizing cache hit ratio and throughput. Then, based on the given network constraints and objects, we convert the CR transferring problem into 0-1 Knapsack Problem (KP01). Finally, in order to effectively obtain the optimal solution, we propose a heuristic approach based on Ant Colony Optimization (ACO) and expectation efficiency to solve KP01. The simulation is driven by the real YouTube dataset from campus network measurement over GTS and Deltacom topologies, and the experimental results demonstrate that the proposed strategy is more efficient compared to three baselines.

New Efficient Caching Strategy based on Clustering in Named Data Networking

<p>The Named Data Networking NDN is one of the most proposed architecture for the new model of Internet communications based on contents distribution, called Information-Centric Network ICN. It is widely accepted by the research community since it has become dominant in ICN design that resolves TCP-IP based Internet problems such as bandwidth, delay, location dependent and congestion. Based on location host IP addresses, TCP-IP designed for Peer-to-Peer communication P2P. NDN architecture is oriented Content Centric Networking CCN, where the data is stored on routers and distributed to users from the nearest router. Cache capacities of routers are limited compared to forwarded contents. To move from TCP-IP model to CCN model, many papers propose several new contents distribution based architecture ICN. In this paper, we propose a novel strategy to optimize the use of network resources inspired from Network clustering and cluster head selection in MANETs. Specifically, the improved K-medoids cluster algorithm is used to divide the global network in clusters, where for each cluster; three routers are selected as content routers. The first is the main caching router as well as the second and the third are the secondary caching router. The caching router selection process relies on three relevant criteria consisting of the distance between a node and its cluster centroid, the number of neighbors, and the congestion level. Two Multi Attribute Decision–Making methods MADM are applied, namely TOPSIS and AHP. Performance analysis of our proposed strategy with the established criteria showed � its effectiveness and strong potential.<em></em></p>

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.

Mobility management in content-centric networking

The content-centric networking depends on a forwarding information base (FIB) to forward Interest towards providers and utilizes reverse paths in a pending Interest table (PIT) to send Data back to consumers. If consumers change their locations, reverse paths may disrupt and consequently content-centric communications fail. If providers change their locations, each relevant content router (CR) has to update FIB. This may lead to content-centric communication disruption and FIB pollution. Taking these issues into account, we propose a provider and consumer mobility support scheme and aim to improve the Interest and Data delivery rates. The proposed provider handover algorithm supports the provider mobility by updating FIB in a unicast way. Since only the previous CR and serving CR are involved in updating FIB, the handover is achieved without FIB pollution. As a result, the provider handover cost and latency are reduced and the Interest delivery rate is improved. The proposed consumer handover algorithm supports the consumer mobility by updating PIT in a unicast way. Based on the updated PIT, Data can be sent back to consumers even if they change their locations. Since consumers retrieve Data via one communication process, the Data delivery rate is improved and the retransmission of Interest is avoided. Compared with the existing solution, this scheme reduces the handover cost and latency by nearly 72% and 51% respectively, and increases the delivery rate by nearly 11.5%.

Cache nFace: a simple countermeasure for the producer-consumer collusion attack in Named Data Networking

We propose, in this paper, a countermeasure against the producer-consumer collusion attack in Named Data Networking (NDN). In this attack, malicious nodes act in collusion by generating content requests at high rate and thus changing content popularity. The goal of the attack is to reduce in-network caching efficiency by increasing the probability of legitimate consumers to retrieve contents directly from the producer. The proposed countermeasure, called Cache nFace, mitigates this attack by dividing the cache of a node into sub-caches. Each sub-cache only stores contents requested through one specific network interface. Our assumption is that malicious requests do not arrive simultaneously at all interfaces of a content router very often. Results show that cache nFace reduces up to 50% the effectiveness of the attack and outperforms another proposal found in the literature in all the analyzed scenarios.