Information-centric mobile caching network frameworks and caching optimization: a survey <s> Research issues based on mobile Caching network <s> The self-tuning, low-overhead, scan-resistant adaptive replacement cache algorithm outperforms the least-recently-used algorithm by dynamically responding to changing access patterns and continually balancing between workload recency and frequency features. Caching, a fundamental metaphor in modern computing, finds wide application in storage systems, databases, Web servers, middleware, processors, file systems, disk drives, redundant array of independent disks controllers, operating systems, and other applications such as data compression and list updating. In a two-level memory hierarchy, a cache performs faster than auxiliary storage, but it is more expensive. Cost concerns thus usually limit cache size to a fraction of the auxiliary memory's size. <s> BIB001 </s> Information-centric mobile caching network frameworks and caching optimization: a survey <s> Research issues based on mobile Caching network <s> There have been many recent papers on data-oriented or content-centric network architectures. Despite the voluminous literature, surprisingly little clarity is emerging as most papers focus on what differentiates them from other proposals. We begin this paper by identifying the existing commonalities and important differences in these designs, and then discuss some remaining research issues. After our review, we emerge skeptical (but open-minded) about the value of this approach to networking. <s> BIB002
ICN enables caching of content pieces that can be reused by other end users requesting the same content. Ubiquitous in-network caching means caching all content fragments that traverse it, that is to say, if a matching request is received while a fragment is still in its cache store, it will be forwarded to the requester from that network element, avoiding the requester going all the way to the hosting server. As one of the typical architectures in ICN, CCN BIB002 advocates such content caching. Such a universal caching strategy is unnecessarily costly and sub-optimal. Since the motivation of caching in networks aims to realize the following goals: (1) lower the content delivery latency whereby a cached content near the client can be fetched faster than from the server, (2) reduce traffic and congestion since content traverses fewer links when there is a cache hit locally and (3) alleviate server load as every cache hit means serving one less request, it is significant to model the behavior of the caching system, understand the fundamentals of caching strategies pertinent to caching network topology and network scalability, and design the optimal caching policies based on the different caching frameworks, then make optimization analysis on the caching network based on the specific optimization objectives in order to enhance the overall content delivery performance in terms of network bandwidth cost, retrieval delay reduction and server burden alleviation  . In this section, we focus on research issues analyzing the performance and optimization of informationcentric mobile caching networks. The research methods include mathematic modeling and simulation. In research issues based on mathematic modeling, optimization theory, Markov chain and game BIB001 theory are usually used. In research papers based on simulation, discrete event simulation, trace-based simulation and test bed/experiment are used. The taxonomy of research methods for information centric mobile caching network is summarized in Fig. 5 . The main contributions of research issues can be classified into two respects, one is modeling behaviors and evaluating performances of caching system, the other is the optimization on information centric mobile caching network. The optimization objective is either network centric objective or user centric objective. Modeling behavior and performance analysis of caching system is surveyed in 4.1, and optimization of mobile caching network based on network centric and user centric objectives is analyzed in 4.2.