Caching In Content-Centric Networking Research Articles

COCP: Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking

On-path caching is the prominent module in Content-Centric Networking (CCN), equipped with the capability to handle the demands of future networks such as the Internet of Things (IoT) and vehicular networks. The main focus of the CCN caching module is data dissemination within the network. Most of the existing strategies of in-network caching in CCN store the content at the maximum number of routers along the downloading path. Consequently, content redundancy in the network increases significantly, whereas the cache hit ratio and network performance decrease due to the unnecessary utilization of limited cache storage. Moreover, content redundancy adversely affects the cache resources, hit ratio, latency, bandwidth utilization, and server load. We proposed an in-network caching placement strategy named Coupling Parameters to Optimize Content Placement (COCP) to address the content redundancy and associated problems. The novelty of the technique lies in its capability to minimize content redundancy by creating a balanced cache space along the routing path by considering request rate, distance, and available cache space. The proposed approach minimizes the content redundancy and content dissemination within the network by using appropriate locations while increasing the cache hit ratio and network performance. The COCP is implemented in the simulator (Icarus) to evaluate its performance in terms of the cache hit ratio, path stretch, latency, and link load. Extensive experiments have been conducted to evaluate the proposed COCP strategy. The proposed COCP technique has been compared with the existing state-of-the-art techniques, namely, Leave Copy Everywhere (LCE), Leave Copy Down (LCD), ProbCache, Cache Less for More (CL4M), and opt-Cache. The results obtained with different cache sizes and popularities show that our proposed caching strategy can achieve up to 91.46% more cache hits, 19.71% reduced latency, 35.43% improved path stretch and 38.14% decreased link load. These results confirm that the proposed COCP strategy has the potential capability to handle the demands of future networks such as the Internet of Things (IoT) and vehicular networks.

IMU: A Content Replacement Policy for CCN, Based on Immature Content Selection

In-network caching is the essential part of Content-Centric Networking (CCN). The main aim of a CCN caching module is data distribution within the network. Each CCN node can cache content according to its placement policy. Therefore, it is fully equipped to meet the requirements of future networks demands. The placement strategy decides to cache the content at the optimized location and minimize content redundancy within the network. When cache capacity is full, the content eviction policy decides which content should stay in the cache and which content should be evicted. Hence, network performance and cache hit ratio almost equally depend on the content placement and replacement policies. Content eviction policies have diverse requirements due to limited cache capacity, higher request rates, and the rapid change of cache states. Many replacement policies follow the concept of low or high popularity and data freshness for content eviction. However, when content loses its popularity after becoming very popular in a certain period, it remains in the cache space. Moreover, content is evicted from the cache space before it becomes popular. To handle the above-mentioned issue, we introduced the concept of maturity/immaturity of the content. The proposed policy, named Immature Used (IMU), finds the content maturity index by using the content arrival time and its frequency within a specific time frame. Also, it determines the maturity level through a maturity classifier. In the case of a full cache, the least immature content is evicted from the cache space. We performed extensive simulations in the simulator (Icarus) to evaluate the performance (cache hit ratio, path stretch, latency, and link load) of the proposed policy with different well-known cache replacement policies in CCN. The obtained results, with varying popularity and cache sizes, indicate that our proposed policy can achieve up to 14.31% more cache hits, 5.91% reduced latency, 3.82% improved path stretch, and 9.53% decreased link load, compared to the recently proposed technique. Moreover, the proposed policy performed significantly better compared to other baseline approaches.

A Neighborhood Aware Caching and Interest Dissemination Scheme for Content Centric Networks

Content-Centric Networking (CCN) is a promising framework for the next generation Internet architecture that exploits ubiquitous in-network caching to minimize content delivery latency and reduce network traffic. In this paper, we introduce a neighborhood aware mechanism for content caching, named Neighborhood Aware Caching and Interest Dissemination (NACID) that accounts for the popularity of contents and how close the content copies are in the neighborhood. We use a very low-overhead, Bloom Filter based dissemination of caching information in the neighborhood. Given the neighborhood cached contents, the proposed scheme decides when and how to handle the additional caching of content and its eviction. Simulation results show that NACID performs substantially better than the existing CCN caching policies. We also study different heterogeneous cache memory allocation strategies and show that the simpler homogeneous allocation strategies work almost as well.

A collaborative caching strategy for content-centric enabled wireless sensor networks

The Content-Centric Networking (CCN) is an efficient traffic handling technology by accessing content with its name instead of its physical location and achieving in-network caching. Indeed, CCN caching ensures high content availability, network traffic reduction, and low retrieval latency which reduces congestion and improves end-to-end delay. Moreover, it could greatly improve the efficiency of content delivery in Wireless Sensor Networks (WSNs). In this article, we propose to exploit this feature to enable CCN in WSN environments. The CCN architecture enables the content caching on each sensor-node in WSN and several research studies have been devoted to the caching management issue in such a context. However, caching the content on all the nodes is not a good strategy in terms of resource utilization. It is, therefore, necessary to determine where to cache and how to handle it in order to optimize the resources while realizing a high-interest satisfaction rate. Thus, our objective is to study existing caching strategies and propose a novel one that takes into account the node centrality and its distance from the source of the content. Through extensive simulations, we examine the performance of our scheme under different configurations and demonstrate how it outperforms the traditional caching strategies such as LCE (Leave Copy Everywhere) and LCD (Leave Copy Down).

Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes.

Optimal Content Caching in Content-Centric Networks

Content-Centric Networking (CCN) is a novel architecture that is shifting host-centric communication to a content-centric infrastructure. In recent years, in-network caching in CCNs has received significant attention from research community. To improve the cache hit ratio, most of the existing schemes store the content at maximum number of routers along the downloading path of content from source. While this helps in increased cache hits and reduction in delay and server load, the unnecessary caching significantly increases the network cost, bandwidth utilization, and storage consumption. To address the limitations in existing schemes, we propose an optimization based in-network caching policy, named as opt-Cache, which makes more efficient use of available cache resources, in order to reduce overall network utilization with reduced latency. Unlike existing schemes that mostly focus on a single factor to improve the cache performance, we intend to optimize the caching process by simultaneously considering various factors, e.g., content popularity, bandwidth, and latency, under a given set of constraints, e.g., available cache space, content availability, and careful eviction of existing contents in the cache. Our scheme determines optimized set of content to be cached at each node towards the edge based on content popularity and content distance from the content source. The contents that have less frequent requests have their popularity decreased with time. The optimal placement of contents across the CCN routers allows the overall reduction in bandwidth and latency. The proposed scheme is compared with the existing schemes and depicts better performance in terms of bandwidth consumption and latency while using less network resources.

Content-Centric Network Caching Strategy based on Node Situational Degree

Content-Centric Network Caching Strategy based on Node Situational Degree

PPP: Prefix-Based Popularity Prediction for Efficient Content Caching in Contentcentric Networks

In the Content-Centric Networking (CCN) architecture, popular content can be cached in some intermediate network devices while being delivered, and the following requests for the cached content can be efficiently handled by the caches. Thus, how to design in-network caching is important for reducing both the traffic load and the delivery delay. In this paper, we propose a caching framework of Prefix-based Popularity Prediction (PPP) for efficient caching in CCN. PPP assigns a lifetime (in a cache) to the prefix of a name (of each cached object) based on its access history (or popularity), which is represented as a Prefix-Tree (PT). We demonstrate PPP’s predictability of content popularity in CCN by both traces and simulations. The evaluation results show that PPP can achieve higher cache hits and less traffic load than traditional caching algorithms (i.e., LRU and LFU). Also, its performance gain increases with users of high mobility.

Performances of Probabilistic Caching Strategies in Content Centric Networking

Information centric network (ICN) is progressively becoming the revolutionary paradigm to the traditional Internet with improving data (content) distribution on the Internet along with global unique names. Some ICN-based architecture, such as named data network (NDN) and content centric network (CCN) has recently been developed to deal with prominent advantages to implement the basic idea of ICN. To improve the Internet services, its architecture design is shifting from host-centric (end-to-end) communication to receive-driven content retrieval. A prominent advantage of this novel architecture is that networks are equipped with transparent in-network caching to accelerate the content dissemination and improve the utilization of network resources. The gigantic increase of global network traffic poses new challenges to CCN caching technologies. It requires extensive flexibility for consumers to get information. One of the most imperative commonalities of CCN design is ubiquitous caching. It is broadly accepted that the in-network caching would improve the performance. ICN cache receives on several new characteristics: cache is ubiquitous, cache is transparent to application, and content to be cached is more significant. This paper presents a complete survey of state-of-art CCN-based probabilistic caching schemes aiming to address the caching issues, with certain focus on minimizing cache redundancy and improving the accessibility of cached content.

Neighbor Cooperation Based In-Network Caching for Content-Centric Networking

Neighbor Cooperation Based In-Network Caching for Content-Centric Networking

Degree-based probabilistic caching in content-centric networking

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

Design and evaluation of coordinated in-network caching model for content centric networking

In-network caching has been widely adopted in Content Centric Networking (CCN) to accelerate data delivery, mitigate server load and reduce network traffic. However, the line-speed requirement makes the in-network caching space very limited. With the rapid growth of network traffic, it is significant challenging to decide content placement in such limited cache space. To conquer this conflict, coordinated in-network caching schemes are needed so as to maximize the profit of ubiquitous caching capacities. In particular, in-network caching in CCN is deployed as an arbitrary network topology and naturally supports dynamic request routing. Therefore, content placement scheme and dynamic request routing are tightly coupled and should be addressed together. In this paper, we propose a coordinated in-network caching model to decide the optimal content placement and the shortest request routing path under constraints of cache space and link bandwidth in a systematic fashion. Via extensive simulations, the effectiveness and efficiency of our proposed model has been validated.

RBC-CC: RBC-Based Cascade Caching Scheme for Content-Centric Networking

With the increasing demand of content dissemination, Content-Centric Networking (CCN) has been proposed as a promising architecture for future Internet. In response to the challenges in CCN caching, we develop an online caching scheme (named RBC-CC) exploiting the concept of Routing Betweenness Centrality (RBC) and "prefetching", aiming at significantly reducing costly inter-ISP traffic and largely reducing content access hops. Simulation results demonstrate that the proposed caching scheme can significantly outperform the popular caching approaches in terms of the saving rate of inter-ISP traffic. Besides, RBC-CC performs well in reducing the average access hops and incurs the least cache evictions. Further, we present a thorough analysis regarding the impact of access pattern, cache size, content popularity and population on the caching performance. We then come to the conclusion that our scheme is featured with good stability and scalability as well as its effectiveness.

How Much to Coordinate? Optimizing In-Network Caching in Content-Centric Networks

In content-centric networks, it is challenging how to optimally provision in-network storage to cache contents, to balance the tradeoffs between the network performance and the provisioning cost. To address this problem, we first propose a holistic model for intradomain networks to characterize the network performance of routing contents to clients and the network cost incurred by globally coordinating the in-network storage capability. We then derive the optimal strategy for provisioning the storage capability that optimizes the overall network performance and cost, and analyze the performance gains via numerical evaluations on real network topologies. Our results reveal interesting phenomena; for instance, different ranges of the Zipf exponent can lead to opposite optimal strategies, and the tradeoffs between the network performance and the provisioning cost have great impacts on the stability of the optimal strategy. We also demonstrate that the optimal strategy can achieve significant gain on both the load reduction at origin servers and the improvement on the routing performance. Moreover, given an optimal coordination level $\ell^\ast$ , we design a routing-aware content placement (RACP) algorithm that runs on a centralized server. The algorithm computes and assigns contents to each CCN router to store, which can minimize the overall routing cost, e.g., transmission delay or hop counts, to deliver contents to clients. By conducting extensive simulations using a large-scale trace dataset collected from a commercial 3G network in China, our results demonstrate that our caching scheme can achieve 4% to 22% latency reduction on average over the state-of-the-art caching mechanisms.

Analysis and Enhancements to Probabilistic Caching in Content-Centric Networking

Analysis and Enhancements to Probabilistic Caching in Content-Centric Networking

SFP: Statistical Filtering Policy for Caching in Content-Centric Networking

SFP: Statistical Filtering Policy for Caching in Content-Centric Networking

A game theoretic approach for energy-efficient in-network caching in content-centric networks

Recently, content-centric networking (CCN) has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energy-efficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the routers are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.

A neural network based congestion control algorithm for content-centric networks

Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network. Keywords: Content-Centric Network, Congestion Control, Drop Prediction, Named Data Networking, Neural Network.

Inter-Chunk Popularity-Based Edge-First Caching in Content-Centric Networking

Content-centric networking (CCN) is considered promising for the efficient support of ever-increasing streaming multimedia services. Inter-chunk popularity-based caching is one of the key requirements in CCN multimedia services because some chunks of a content file tend to be requested more frequently than others. For multimedia contents, forepart chunks often have higher popularity than others as users may interrupt and abort before finishing its service. This paper presents a novel cache replication scheme, which places more popular chunks ahead on the edge router, and establishes a cache pipelining on the relaying routers along the path to reduce user-perceived delay. Simulation results show that the proposed scheme incurs less delay and reduces the overall redundant network traffic while guaranteeing a higher cache hit ratio.

Energy-Efficient In-Network Caching for Content-Centric Networking

This work studies on the issue of energy efficient caching for Content-centric Networking (CCN) architecture. We first build the energy consumption model for content distribution in an Internet Service Provider (ISP) and find that the objective of energy optimization can translate into that of minimizing average response hops. We then develop an aging popularity-based in-network caching scheme (APC), where each router implements caching policy using its local information but relying little on coordination. Simulation results show that APC has significant advantage over the widely used Leaving Copies Everywhere (LCE) in terms of the saved energy consumption, cache hit rate and the average number of response hops, so it helps improve user access experience and reduce network traffic other than the energy optimization. Encouragingly, APC can save up to 70% energy.