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P-CLS: a Popularity-Driven Caching Location and Searching Scheme in Content Centric Networking

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P-CLS: a Popularity-Driven Caching Location and Searching Scheme in Content Centric Networking P-CLS: A Popularity-driven Caching Location and Searching Scheme in Content Centric Networking Yuemei Xu∗, Shuai Ma†, Yang Li‡, Fu Chen∗ and Song Ci§ ∗ Department of Computer Science, Beijing Foreign Studies University, China † Viterbi School of Engineering, University of Southern California, USA ‡ Institute of Information Engineering, Chinese Academy of Science, Beijing, China §Department of Computer and Electronics Engineering, University of Nebraska-Lincoln, USA Abstract—Content Centric Networking (CCN) is an emerging idea that most users only focus on the accessing data, rather next-generation network infrastructure around content dissemi- than the physical locations from which the data are retrieved. nation and retrieval, shifting from physical locations to named In-network caching, as an intrinsic component of CCN, data. The built-in caching capacity of CCN, termed as in-network caching, promises to enable fast and effective content distribution enables each router in CCN to equip a content store (CS) mod- at a global scale. Because of the in-network caching, the caching ule (we call these routers as C-routers for short), attempting to strategy of content location potentially affects how to make maximize the probability of content sharing while satisfying content searching decisions, while content searching in return users’ requests as close to end-users as possible. decides in which routers content are stored. The relationship Due to In-network caching, two important issues between content caching location and content searching has not been fully exploited in CCN or further used for the whole need to be addressed in CCN. First, C-routers become avail- network performance improvement. This paper exploits the able containers to cache content for the purpose of satisfying content caching location and content searching mechanism of the subsequent requests. Thus we need to decide which C- CCN, and proposes a Popularity-driven content Caching Loca- routers in the content delivery path should cache the content, tion and Searching scheme (P-CLS) in CCN. P-CLS leverages in order to maximize the utilization of CS while maintaining content access popularity to realize diverse content distribution and reduce content caching redundancy, which also overcomes the content diversity of network. That is what to cache, what the oscillation and frequent replacement phenomenon in the to replace and where to cache, and is defined as a content existing content caching and searching (CLS) scheme. Extensive caching location problem. Second, content replicas in C- simulations via hierarchical and arbitrary caching topologies routers change with time, which is because when a C-router show that the proposed scheme outperforms the existing caching needs to cache content but its CS is full, it will carry out algorithms. a content replacement strategy (e.g., LRU or LFU) to evict I. INTRODUCTION content for the new coming one. Due to the high volatility of content in CS, C-routers take efforts to find and select an Millions of multimedia data (e.g., user-generated content, appropriate content replica location to forward requests. It is Video-on-demand, HTTP web pages) are generated and shared known as content searching problem. by content producers and consumers. This trend has been The content caching location and content searching prob- posing high stress on network bandwidth and content stor- lems have not been fully exploited in CCN and are usually age, resulting in network congestion and server overload. To studied separately. On one hand, two research lines exist in address these issues, Content Delivery Network (CDN) and content caching location problem, namely on-path and off- application-specific solutions like peer-to-peer (P2P) are pop- path content caching. For example, Leave Copy Everywhere ular. However, CDN may experience sub-optimal performance (LCE), Leave Copy Down (LCD) and Move Copy Down due to the traffic engineering of Internet service provides (MCD) are all the on-path content caching strategies, where (ISPs) [1] and P2P systems incur a lot of inter-ISP traffic content may be cached by any on-path cache or a subset and are unstable in terms of content availability and download of traverse caches in its delivery path. In contrast, off-path performance [2]. content caching often calculates the (near) optimal content To overcome limitations of CDN and P2P, accelerate net- placement off-line. Off-path strategies can achieve better net- work data delivery and reduce server load, Content Centric work performance than on-path ones but at the cost of time Networking (CCN) has been proposed as a predominant next- complexity. On the other hand, content searching in CCN generation Internet architecture [3], which is funded upon the is usually implemented coordinately in a distributed manner, ∗This work is supported by the Fundamental Research Funds for the relying on local cache management policies as well as the Central Universities (No.023600-500110002), the National Natural Science relative position of caches in the network to achieve good Foundation of China (No.61502038, No.61170209) and Program for New performance. As the Internet traffic grows dramatically, it is Century Excellent Talents in University (No.NCET-13-0676). Corresponding email: [email protected]. still very challenging to forward user requests towards a “best” (e.g. closest) available replica in CCN. 978-1-4673-8590-9/15/$31.00 ©2015 IEEE Recently, there is a tend to exploit the content caching popular content to edge network closer to end-users. Recently, location and content searching problems tightly, suggesting a large body of researches focus on how to improve the that content searching potentially decides request forwarding efficiency of in-network caching in CCN, such as exploring op- path and in which C-routers content are stored, while content timal replication strategy [7], [8], explicit cooperative caching caching location in return affects how to make content search- [9], [10], implicit cooperative caching [11]–[13], cache-aware ing decisions for forwarding requests appropriately. Among routing [14] and bandwidth and storage sharing mechanism these researches, Li et al. proposes an implicit coordinate [15], [16]. All of these studies consistently reveal that content content location and searching scheme (CLS) and shows caching location and content searching mechanism are the promising potential in this research direction [4]. However, essential parts of in-network caching, therefore need to be CLS scheme is designed in a hierarchical caching topology, sophisticatedly designed and deliberated. but not the arbitrary caching topology of CCN, which will lead to a oscillation and frequent replacement phenomenon A. Content Caching Location when applying it in CCN. The reason lies in that CLS makes An implicit and transparent approach towards content the content caching down or up decisions only considering caching location called Leave Copy Everywhere (LCE) [17] the current hitting event while ignoring the global content is used in CCN by default. LCE places content copies at all popularity. the intermediate C-routers from the hitting C-router to the re- In this paper, we focus on the content caching location and quested end-user. LCE can realize fast content delivery but will searching problem in an arbitrary caching topology of CCN, lead to unnecessary content caching redundancy. LCD [2] and and novelly propose a Popularity-driven content Caching Lo- MCD [2] are implicit coordinated content caching strategies cation and Searching scheme (P-CLS). Researches [5], [6] re- frequently used in Web caching systems or CDN, and have veal that content popularity is (by far) the most important fac- been introduced to CCN. In LCD, the requested content hit at tor affecting the network performance, compared with content l level will be pulled down to l-1 level. Simultaneously, the request distributions, content catalog size, cache replacement l level node keeps the requested content. MCD is similar to strategies and etc. P-CLS distributes content towards end-users LCD and the only difference is that the l level node will delete considering content popularity as well as the content caching the requested content after it has been moved to l-1 level. location. Studies [18] show that LCD works better than MCD as The main characteristics of P-CLS are summarized as it can cache one more copy on-path to serve clients from follows: other branches. Furthermore, LCD also outperforms LCE and 1) Popularity-based: Through incorporating the content greatly reduces content caching redundancy [18], and thus is popularity factor into the content caching location and used for comparison in our simulations. searching decisions, P-CLS not only realizes to dis- B. Content Searching tribute content to these C-routers which request them most frequently, but also prevents the oscillation and Along the research line of content searching, a best-effort frequent replacement phenomenon of CLS. approach called Breadcrumbs is proposed to use the content 2) Content Diversity: In P-CLS, there is always one and caching location information to improve the efficiency of at most one copy of a content cached on the whole path content searching [19]. In Breadcrumbs, a trail for the purpose between a server and an edge C-router. Thus, P-CLS can of storing content forwarding history is created and maintained make more efficient cache utilization while guaranteeing at each router when the content is downloaded. Thus the content diversity in the whole network. subsequent request for the same content may be routed towards 3) Trail-based Searching: P-CLS creates a trail to store the nearest content copy directed by such a trail. Sourlas et the content caching history during the content cached up al. propose an intra-domain cache-aware content searching and down. The trail and content popularity information scheme that computes the paths with minimum transportation helps to find a “best” (e.g. closest) available replica cost based on the information item demands and the caching arbitrary caching topology of CCN.
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