Performance evaluation of information-centric networking for multimedia services

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The rapid development in multimedia services has shifted the major function of the current Internet from host-centric communication to service-oriented content dissemination. Motivated by this significant change, Information-Centric Networking (ICN) has emerged as a new networking paradigm, which aims at providing natural support for efficient information retrieval over the Internet. As a crucial characteristic of ICN, in-network caching enables users to efficiently access popular content from ubiquitous caches to improve the Quality-of-Experience (QoE). Therefore, in-network caching for ICN has received considerable attention in recent years and many cache schemes and models have been proposed. However, there is a lack of research into ICN cache models under practical environments such as arbitrary topology and multimedia services exhibiting bursty nature. To bridge the gap, this paper proposes a new analytical model to gain valuable insight into the caching performance of ICN with arbitrary topology and bursty content requests. The accuracy of the proposed model is validated by comparing the analytical results with those obtained from simulation experiments. The analytical model is then used as a cost-efficient tool to investigate the impact of key network and content parameters on the performance of caching in ICN

Evaluation of Caching Strategies in Content-Centric Networking (CCN) for Mobile and Social Networking Environment

Users of the Internet are still using the basic network communication model that was created way back 1960s. The grand idea of migration from host-centric to information-centric has made Content-Centric Networking (CCN) one of the eminent candidates for the future internet. The extension of caching technology as one of the components in the networking itself require deeper thought than just plug and play of current web or server caching techniques. While most studies are focusing on new caching strategies, this study will highlight the gaps by comparing common caching strategies in different predicted scenario of the future. The evaluation was done using simulation tools known as SocialCCNSim focusing on six relevant caching strategies: Leave Copy Everywhere (LCE), Leave Copy Down (LCD), ProbCache, Cache “Less for More”, MAGIC and Randomly Copy One (RCOne) in different network topologies: Tree and Diamond. Rank is given based on metrics such as Cache Hit, Stretch, Diversity and Eviction operations that represented the most commonly used metrics in networking. Results show that all caching strategies have their own behavior toward different network topology. However, Cache “Less for More” considered the best with balanced result for both performance and resource utilization metrics

Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs

Information-Centric Fog Computing enables a multitude of nodes near the end-users to provide storage, communication, and computing, rather than in the cloud. In a fog network, nodes connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within that network topology. The centrality is then used to distinguish nodes in the fog network, or to prioritize some nodes over others to participate in the caching fog. We argue that, for an Information-Centric Fog Computing approach, graph centrality is not an appropriate metric. Indeed, a node with low connectivity that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce acontent-based centrality (CBC) metric which takes into account how well a node is connected to the content the network is delivering, rather than to the other nodes in the network. To illustrate the validity of considering content-based centrality, we use this new metric for a collaborative caching algorithm. We compare the performance of the proposed collaborative caching with typical centrality based, non-centrality based, and non-collaborative caching mechanisms. Our simulation implements CBC on three instances of large scale realistic network topology comprising 2,896 nodes with three content replication levels. Results shows that CBC outperforms benchmark caching schemes and yields a roughly 3x improvement for the average cache hit rate

Offloading Content with Self-organizing Mobile Fogs

Mobile users in an urban environment access content on the internet from different locations. It is challenging for the current service providers to cope with the increasing content demand from a large number of collocated mobile users. In-network caching to offload content at nodes closer to users alleviate the issue, though efficient cache management is required to find out who should cache what, when and where in an urban environment, given nodes limited computing, communication and caching resources. To address this, we first define a novel relation between content popularity and availability in the network and investigate a node's eligibility to cache content based on its urban reachability. We then allow nodes to self-organize into mobile fogs to increase the distributed cache and maximize content availability in a cost-effective manner. However, to cater rational nodes, we propose a coalition game for the nodes to offer a maximum "virtual cache" assuming a monetary reward is paid to them by the service/content provider. Nodes are allowed to merge into different spatio-temporal coalitions in order to increase the distributed cache size at the network edge. Results obtained through simulations using realistic urban mobility trace validate the performance of our caching system showing a ratio of 60-85% of cache hits compared to the 30-40% obtained by the existing schemes and 10% in case of no coalition

Mediator-assisted multi-source routing in information-centric networks

Among the new communication paradigms recently proposed, information-centric networking (ICN) is able to natively support content awareness at the network layer shifting the focus from hosts (as in traditional IP networks) to information objects. In this paper, we exploit the intrinsic content-awareness ICN features to design a novel multi-source routing mechanism. It involves a new network entity, the ICN mediator, responsible for locating and delivering the requested information objects that are chunked and stored at different locations. Our approach imposes very limited signalling overhead, especially for large chunk size (MBytes). Simulations show significant latency reduction compared to traditional routing approaches

Content-Centric Networking at Internet Scale through The Integration of Name Resolution and Routing

We introduce CCN-RAMP (Routing to Anchors Matching Prefixes), a new approach to content-centric networking. CCN-RAMP offers all the advantages of the Named Data Networking (NDN) and Content-Centric Networking (CCNx) but eliminates the need to either use Pending Interest Tables (PIT) or lookup large Forwarding Information Bases (FIB) listing name prefixes in order to forward Interests. CCN-RAMP uses small forwarding tables listing anonymous sources of Interests and the locations of name prefixes. Such tables are immune to Interest-flooding attacks and are smaller than the FIBs used to list IP address ranges in the Internet. We show that no forwarding loops can occur with CCN-RAMP, and that Interests flow over the same routes that NDN and CCNx would maintain using large FIBs. The results of simulation experiments comparing NDN with CCN-RAMP based on ndnSIM show that CCN-RAMP requires forwarding state that is orders of magnitude smaller than what NDN requires, and attains even better performance

Performance Evaluation of Caching Policies in NDN - an ICN Architecture

Information Centric Networking (ICN) advocates the philosophy of accessing the content independent of its location. Owing to this location independence in ICN, the routers en-route can be enabled to cache the content to serve the future requests for the same content locally. Several ICN architectures have been proposed in the literature along with various caching algorithms for caching and cache replacement at the routers en-route. The aim of this paper is to critically evaluate various caching policies using Named Data Networking (NDN), an ICN architecture proposed in literature. We have presented the performance comparison of different caching policies naming First In First Out (FIFO), Least Recently Used (LRU), and Universal Caching (UC) in two network models; Watts-Strogatz (WS) model (suitable for dense short link networks such as sensor networks) and Sprint topology (better suited for large Internet Service Provider (ISP) networks) using ndnSIM, an ns3 based discrete event simulator for NDN architecture. Our results indicate that UC outperforms other caching policies such as LRU and FIFO and makes UC a better alternative for both sensor networks and ISP networks