A Review on Cache Replacement Strategies in Named Data Network

Named Data Network (NDN) architecture is one of the newest and future-aspired Internet communication systems. Video-on-Demand (VoD) has rapidly emerged as a popular online service. However, it is costly, considering its high bandwidth and popularity. Internet on-demand video traffic has been growing quite fast, and on-demand video streaming has gained much attention. The problem of this study is that the NDN architecture is processing several forms of online video requests simultaneously. However, limited cache and multiple buffering of requested videos result in loss of data packet as a consequence of the congestion in the cache storage network. Addressing this problem is essential as congestion cause network instability. This work emphasizes on the review of cache replacement strategies to deal with the congestion issue in Named Data Networks (NDN) during the VoD delivery in order to determine the performance (strengths and weaknesses) of the cache replacement strategies. Finally, this study proposes the replacement strategies must be enhanced with a new strategy that depends on popularity and priority regarding the congestion. This study would positively benefits both suppliers and users of Internet videos

Measuring named data networks

2020 Spring.Includes bibliographical references.Named Data Networking (NDN) is a promising information-centric networking (ICN) Internet architecture that addresses the content directly rather than addressing servers. NDN provides new features, such as content-centric security, stateful forwarding, and in-network caches, to better satisfy the needs of today's applications. After many years of technological research and experimentation, the community has started to explore the deployment path for NDN. One NDN deployment challenge is measurement. Unlike IP, which has a suite of measurement approaches and tools, NDN only has a few achievements. NDN routing and forwarding are based on name prefixes that do not refer to individual endpoints. While rich NDN functionalities facilitate data distribution, they also break the traditional end-to-end probing based measurement methods. In this dissertation, we present our work to investigate NDN measurements and fill some research gaps in the field. Our thesis of this dissertation states that we can capture a substantial amount of useful and actionable measurements of NDN networks from end hosts. We start by comparing IP and NDN to propose a conceptual framework for NDN measurements. We claim that NDN can be seen as a superset of IP. NDN supports similar functionalities provided by IP, but it has unique features to facilitate data retrieval. The framework helps identify that NDN lacks measurements in various aspects. This dissertation focuses on investigating the active measurements from end hosts. We present our studies in two directions to support the thesis statement. We first present the study to leverage the similarities to replicate IP approaches in NDN networks. We show the first work to measure the NDN-DPDK forwarder, a high-speed NDN forwarder designed and implemented by the National Institute of Standards and Technology (NIST), in a real testbed. The results demonstrate that Data payload sizes dominate the forwarding performance, and efficiently using every fragment to improve the goodput. We then present the first work to replicate packet dispersion techniques in NDN networks. Based on the findings in the NDN-DPDK forwarder benchmark, we devise the techniques to measure interarrivals for Data packets. The results show that the techniques successfully estimate the capacity on end hosts when 1Gbps network cards are used. Our measurements also indicate the NDN-DPDK forwarder introduces variance in Data packet interarrivals. We identify the potential bottlenecks and the possible causes of the variance. We then address the NDN specific measurements, measuring the caching state in NDN networks from end hosts. We propose a novel method to extract fingerprints for various caching decision mechanisms. Our simulation results demonstrate that the method can detect caching decisions in a few rounds. We also show that the method is not sensitive to cross-traffic and can be deployed on real topologies for caching policy detection

Novel applications and contexts for the cognitive packet network

Autonomic communication, which is the development of self-configuring, self-adapting, self-optimising and self-healing communication systems, has gained much attention in the network research community. This can be explained by the increasing demand for more sophisticated networking technologies with physical realities that possess computation capabilities and can operate successfully with minimum human intervention. Such systems are driving innovative applications and services that improve the quality of life of citizens both socially and economically. Furthermore, autonomic communication, because of its decentralised approach to communication, is also being explored by the research community as an alternative to centralised control infrastructures for efficient management of large networks. This thesis studies one of the successful contributions in the autonomic communication research, the Cognitive Packet Network (CPN). CPN is a highly scalable adaptive routing protocol that allows for decentralised control in communication. Consequently, CPN has achieved significant successes, and because of the direction of research, we expect it to continue to find relevance. To investigate this hypothesis, we research new applications and contexts for CPN. This thesis first studies Information-Centric Networking (ICN), a future Internet architecture proposal. ICN adopts a data-centric approach such that contents are directly addressable at the network level and in-network caching is easily supported. An optimal caching strategy for an information-centric network is first analysed, and approximate solutions are developed and evaluated. Furthermore, a CPN inspired forwarding strategy for directing requests in such a way that exploits the in-network caching capability of ICN is proposed. The proposed strategy is evaluated via discrete event simulations and shown to be more effective in its search for local cache hits compared to the conventional methods. Finally, CPN is proposed to implement the routing system of an Emergency Cyber-Physical System for guiding evacuees in confined spaces in emergency situations. By exploiting CPN’s QoS capabilities, different paths are assigned to evacuees based on their ongoing health conditions using well-defined path metrics. The proposed system is evaluated via discrete-event simulations and shown to improve survival chances compared to a static system that treats evacuees in the same way.Open Acces

Future of networking is the future of Big Data, The

2019 Summer.Includes bibliographical references.Scientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science

Performance evaluation of the replacement policies for pending interest table

Information (content) plays an important role in a Named Data Networking (NDN). Hence, an information model is essential in representing information appropriately to supports meaningful information spreading. As a distinction from the current network practice, the NDN shall concentrate on the content itself, rather than the location of the information itself. One important and common feature of NDN is leveraging through its built-in network caches (temporal store) to improve the communication and efficiency of content dissemination. Thus, caching is well thought-out as one of the most crucial features (especially in PIT) of the NDN. Its efficiency is due to it required feature of producing a flexible strategy in deciding what content to store and replace when the PIT overflows. Thus, PIT management in NDN continues being one of the primary concerns of high-speed forwarding. To address this issue, replacement policies, as one of the key factors for determining the effectiveness of a PIT in line with many researcher's haven to propose numerous replacement policies, i.e. LRU, Random and Persistent, which have been projected to attain the improved Interest drop rate, reduce the delay and Interest retransmission as when the PIT is full. However, to the best of our knowledge, there have not been studies that dealt with the performance and evaluation between the mentioned policies under different network topologies. Therefore, in this paper we study the performance of Interest drop rate, delay and Interest retransmission under different network topologies, i.e. Tree, Abilene and Germany when the PIT is full. The significance yearned for this study would be to provide a solid starting point in research directions of new PIT replacement policies for contemporary workload or selectively turning off of fewer used cache ways