An autonomous and efficient controller-based routing scheme for networking Named-Data mobility

Named Data Networking (NDN) has emerged as a promising information-centric networking paradigm that addresses the limitations of the traditional IP-based Internet architecture. The core principle of NDN relies on content naming instead of host addressing, to provide efficient, secure, and scalable content delivery. Routing is a critical component of NDN and is responsible for discovering and maintaining optimal paths to named content. This paper presents a comprehensive review of routing techniques in NDN, focusing on the design principles, algorithms, and performance metrics, especially in wired network architecture. We first summarize the NDN architecture and discuss its key components. We then delve into the fundamental routing concepts in NDN and categorize and examine various routing techniques, including link state, distance vector, and centralized approaches based on Software Defined Network. We also summarize the relevant research efforts proposed to address NDN routing challenges by focusing more on wired network architecture. Finally, we identify open research issues and future directions in NDN routing, emphasizing the need for scalable, efficient, and secure routing techniques that can fulfill the growing demands of the modern Internet. In conclusion, this review serves as a valuable reference for researchers and practitioners in NDN, offering a comprehensive understanding of the current state-of-the-art routing techniques, limitations, and potential future advancements.

Named Data Networking (NDN) is one of the most promising future Internet architectures, which supports communications based on content names but not the content locations. Clients can retrieve content only by sending an Interest packet, which includes the content name. However, independent Interest packets will cause heavy loads on the routers, particularly in the Internet of Things (IoT) scenarios which will induce huge number of small-sized content exchanges. The proposed scheme enables clients to retrieve many different contents by sending an aggregated Interest packet. In addition, the Data packet can also be aggregated by the intermediate routers to improve the efficiency of NDN communications.

Named Data Networking (NDN) is one of the strong competitors of the next generation network architecture, meeting the needs of today’s users for the network. One problem with NDN is that the content requester does not know if there is any content in the network when it sends out the interest package, and it does not know where the target is. This problem is currently solved by the domain name resolution service, but the existing solution does not apply to the NDN architecture. Because the content in the NDN is identified by a human-readable name, the NDN architecture does not require an additional name resolution system. In this paper, we propose a blockchain-based Content Name Search Mechanism (BCNSM) by binding the content producer and the human-readable content name as the unique identifier of the content, and then using the blockchain as a trusted organization to store content information, and dynamically stores the information through the smart contract, thereby mapping the content name and the storage location to each other. BCNSM provides users with content name search services and name to the content provider resolution services. Then the model was built for the BCNSM using the colored petri net, and the model process is verified by the model simulation to meet the expectations, and the state space analysis proves that the BCNSM has no deadlock. Finally, a prototype of the smart contract was implemented in Ethereum’s testnet, and the storage and Gas overhead of content name registration and content cache address update were tested.

Information-Centric Networking (ICN) is a new communication paradigm that replaces the host addresses by the name of content; Named Data Networking (NDN) is a promising ICN architecture that has attracted research attention in recent years. NDN is a receiver-driven architecture implements pull-based communication in the form of one-interest-one-data. This model poses different challenges, especially from the transport layer perspective. In contact to IP-based networks where the congestion is handled in an end-to-end manner, NDN cannot apply the same concept, while most of the existing solutions are based on hop-by-hop connection. In this paper, we present a new congestion control mechanism for NDN based on link quality estimation. We focus our efforts to provide fast data transmission, decrease packet dropping rate, and maximize the link utilization. The simulation results show that our solution outperforms the NDN schemes in terms of throughput and drop packets.

Named Data Networking (NDN) is a recently designed Internet architecture that benefits data names instead of locations and creates essential changes in the abstraction of network services from "delivering packets to specific destinations” to "retrieving data with special names" makes. This fundamental change creates new opportunities and intellectual challenges in all areas, especially network routing and communication, communication security, and privacy. The focus of this dissertation is on the forwarding aircraft feature introduced by NDN. Communication in NDN is done by exchanging interest and data packets. Consumers send interest packets to request data, routers send them based on the data prefix name, and manufacturers respond with data packets exactly how interest is received. During this process, routers maintain information about the status of suspended interests. As regards NDN is still in its early stage, none of these strong efficiency features have done systematically designed, measured, or tracked in data retrieval and multi-path detection. This paper tries to improve transport performance by combining label switching with adaptive transport methods. For this purpose, in addition to RTT, another timer called T is used, an RTT factor for interface rating. And uses the BDP to choose the best interface to respond to interest. During this process, response time, throughput, and data recovery time are improved by 12%, 10%, and 5%, respectively.

Software Defined Networking (SDN) is the more reliable and flexible networking architecture. Due to its programmable nature, it is getting more popular in every research domain of Computing. Moreover, Named Data Networking (NDN) architecture is also more beneficial and emerging technology of Internet. NDN focuses on the content name instead of its location and replaces the usage of IP addresses. Lot of research has been made to integrate SDN and NDN due to their benefits of better management-ability, flexibility, less complexity, centralized control, decoupled control and data planes etc. Mobile Ad Hoc Network (MANET) is also getting more popular due to huge advancement in Mobile communication devices. Combining SDN and NDN in MANET will resolve lots of problems (i.e. forwarding, mobility, management etc.) which are difficult to tackle with single architecture. Broadcast Storm is always a challenging issue in Ad Hoc networks like MANET. In this paper, we proposed a Controller based Selective Forwarding (CSF) scheme by merging SDN, NDN and MANET to overcome the Broadcast Storm Problem (BSP) and other issues caused by BSP. CSF is evaluated using different simulation parameters which shows superiority over the Native ND-MANET in reference to BSP.

Named Data Network (NDN) is a next-generation network architecture that shifts the traditional data communications paradigm Unlike conventional networks that rely on IP addresses, NDN delivers content based on data names rather than specific locations. In NDN, consumers express their requests by sending interest packets containing content names. These names are then propagated through the network nodes, which forward them to the appropriate destinations. The forwarding strategy in an NDN network plays a crucial role in ensuring efficient data delivery. This strategy includes a set of rules that determine the next hop for each interest packet. These rules are designed to optimize the forwarding process, minimizing delays and improving network efficiency. However, if the forwarding strategy is implemented without accurately identifying the appropriate face (i.e., the network interface) to forward interests toward the producer or the nearest cache node, it can lead to significant delays and packet drops. This, in turn, negatively impacts Quality of Service (QoS) parameters and the overall performance of the NDN network. This study applies supervised learning to classify consumer-requested interests to overcome this issue. This technique leverages several related variables to accurately classify these interests. The outcomes of the conducted research demonstrated that raw data from the mini-NDN output can be processed and transformed into a usable dataset. This data is then utilized to train a classification model with supervised learning. In a scenario with 9 NDN nodes and varying numbers of interests, distributed both uniformly and according to Zipf's law, the Random Forest model performs effectively, achieving an accuracy rate of 86.2% with an error rate of 14.8%.

ABSTRACTSoftware‐defined networking (SDN), a cornerstone of future‐generation networks, is adopted in Named Data Networks (NDN) for large‐scale deployment. The forwarding strategies proposed for SDN‐based NDN primarily use the centralized controller to optimize Interest forwarding and Data delivery. The nodes direct the Interests to the controller to discover the content source(s) and suppress the suboptimal responses. To support such content discovery and delivery, the controller experiences frequent path calculation and trades excessive control messages to install the paths in the nodes due to rapid cache admission and replacement. Besides, the typical NDN forwarding solutions are not viable to realize or need considerable modifications in SDN‐based NDN. To that end, the proposed strategy optimizes Interest forwarding and Data delivery using a Single‐State Q‐learning‐based technique, namely, SDN‐Q. In SDN‐Q, each content source learns to suppress the suboptimal responses, with the learning task offloaded to the controller. The controller communicates the learning decision to the nodes. Each node only retains the action (decision) to entertain an incoming Interest. Once an Interest hits, the source either replies with the Data or remains silent and sends the Interest's information (meta‐data) to the controller for the learning task. Thus, SDN‐Q enables the NDN nodes to remain light‐loaded. Each node can instantly answer an Interest request without redirecting it to the controller. Additionally, we optimize Interest forwarding using a hop‐based scoped‐flooding approach. The proof‐of‐concept implementation in software (simulation) reveals that the proposed system outperforms the competing strategies by reducing the traffic load, latency, and control messages in SDN‐based NDN (at most by 40%, 7%, and four (4) times respectively), without negotiating packet delivery ratio.

Named-Data Networking (NDN) is a novel clean-slate architecture for Future Internet. It has been designed to take into account a new use of the Internet and especially accessing content for a large number of users, and it integrates several features such as in-network caching, security or multipath. As NDN relies on content names instead of host address, it cannot rely on traditional Internet routing, and it is therefore essential to propose a routing scheme adapted for NDN. To this end, in this paper, we present SRSC, our SDN-based Routing Scheme for CCN/NDN and its implementation. SRSC relies on the SDN paradigm. A controller is responsible to forward decisions and to set up rules into NDN nodes. We implement SRSC into NDNx and we also deploy an NDN testbed within a virtual environment and real ISP topology in order to evaluate the performances of our proposal with real-world experiments. We demonstrate the feasibility of SRSC and its ability to forward Interest messages in a fully deployed NDN environment, while keeping low overhead and computation time and high caching performances.

Named Data Networking (NDN) proposes a contentcentric 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 dependent upon on-path caching and is therefore inefficient. This approach reduces data transfer efficiency by ignoring the cached content available on the adjacent off-path routers in the network. In this paper, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. Furthermore, we develop a centralized SDNenabled control for the multipath forwarding strategy (S-MP) that distributes Interests efficiently by using the global knowledge of the NDN network states. We perform extensive evaluations of our proposed methods on an at-scale WAN testbed spanning six geographically separated sites. Our solutions outperform the existing NDN forwarding strategies by a significant margin. We show that 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 gains of 12.2x to 18.4x with in-network caching. In addition, for the S-MP case, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, for with- and without in-network caching respectively.

As a key component in implementing Named Data Networking (NDN), Pending Interest Table (PIT) requires an efficient exact-matching algorithm for a scalable and fast PIT lookup. A Bloom filter (BF) is a memory-efficient data structure for performing exact matching operations. In this paper, three different BF-based PIT architectures are proposed: PIT using functional Bloom filters (FBF-PIT), PIT using counting Bloom filters with return values (rCBF-PIT), and a refined rCBF-PIT with signatures (R-rCBF-PIT). The proposed BF-based PITs incrementally allocate a new BF for storing multiple incoming faces of Interest packets with the same content name. For a Data packet lookup, the proposed PIT architectures simultaneously access every BF structure to find matching faces and delete the faces (i.e., matching Interest packet information). The functional Bloom filter (FBF) used in an FBF-PIT is a key-value data structure that stores values only without keys. However, because the number of non-reusable conflict cells in the FBF increases as the number of stored packets increases in the FBF-PIT, the indeterminable rate increases. To decrease the indeterminable rate, we propose the rCBF-PIT, which uses counting Bloom filters with return values (rCBFs), allowing reusable conflict cells. False positives for Interest packets lead to incorrect deletions that can cause false negatives for incoming Data packets. Because most of the false positives occur in the first BF structure, we finally propose the R-rCBF-PIT, in which the first rCBF is replaced with an rCBF with a signature field. The proposed PITs also provide an aging mechanism using a valid bit and a hit bit for entry expiration. Simulation results show that rCBF-PIT and R-rCBF-PIT both reduce the indeterminable rate by more than 81% compared with FBF-PIT. The results also show that R-rCBF-PIT resolves false negatives caused by incorrect deletions by including the signature fields in the first rCBF.

A Contextual Multi-Armed Bandit approach for NDN forwarding

The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. Named Data Networking (NDN) has recently appeared as a new paradigm to solve many shortcomings in the current TCP/IP architecture. Its main characteristics like stateful forwarding and in-network caching made NDN networks an efficient environment for data delivery where the data is retrieved based on content names rather than IP addresses. The NDN, by its nature, defends against the well-known Distributed Denial of Service (DDoS) attacks that take place in the traditional TCP/IP architecture. However, a special kind of DoS attack called Collusive Interest Flooding Attack (CIFA) has appeared to overwhelm the resources of NDN routers by filling their Pending Interest Tables (PIT) with long-lasting malicious entries. The network throughput and consumer satisfaction rate are highly affected by CIFA. A lightweight yet efficient stateless CIFA detection algorithm is proposed in this research utilizing the non-parametric CUSUM algorithm; a change point detection approach that detects the point in time when a transition occurs in the network. The proposed algorithm is characterized by its low computational overhead, highly accurate detection, and quick response. To detect the malicious name prefixes and eliminate the CIFA effect, a mitigation algorithm that uses the average response time vales of all name prefixes is proposed in this research. Experimental results show that this approach detects CIFA after 199.5 ms from when an attack is launched in the large-scale topology. In addition, the mitigation approach effectively reduces the PIT utilization and increases the average consumer satisfaction rate.

Named Data Networking (NDN) has a built-in caching capability that is enabled with the help of its Content Store. Caching in NDN has several benefits, such as reducing overhead on the producer side, avoiding a single point of failure, and reducing network load. The primary caching policy of the NDN architecture is to leave copies everywhere. However, this scheme induces significant cache redundancy. Existing advanced cache techniques either periodically share the entire list of cached content at a node or make a caching decision without knowing the cached content at other nodes in the network. We propose an intelligent cache policy that estimates cache distribution and topology through a collaborative network of NDN routers, guided by Interest and Data packets. Additionally, we develop an NDN simulator to facilitate the testing and evaluation of our machine learning caching strategy. This paper details the cache policy, the design and functionality of our NDN simulator, and the experiments comparing our approach to baseline caching strategies.

As an important infrastructure of the current Internet, the Domain Name System (DNS) universally supports most of Internet applications. The DNS is established on top of Internet Protocol (IP) to provide an addressing of Internet resources. However, the IP based current Internet communication model is considered as inappropriate for supporting an explosive increase of Internet devices and contents in the Future Internet. To address the communication model issue, Named Data Networking (NDN) has been proposed that allows a consumer only cares about what he wants but not where the content is. Although NDN designs an ideal communication model for the content-intensive Future Internet, it assumes a clean-slate deployment, which would take a long time for deployment. The coexistence of the NDN paradigm and the current Internet is thus expected. In this vein, we propose a new scheme that utilizes current DNS protocols for content naming and addressing in a NDN environment. The proposed scheme helps to accelerate the NDN evolution while providing a means of utilizing the DNS for scalable and secure NDN.