Average Cache Hit Ratio Research Articles

NDN-Based Coded Caching Strategy for Satellite Networks

To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node’s coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colony ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network’s management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.

Cooperation Based Proactive Caching in Multi-Tier Cellular Networks

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.

Caching With Finite Buffer and Request Delay Information: A Markov Decision Process Approach

Edge caching has become a promising technology in future wireless networks owing to its remarkable ability to reduce peak data traffic. However, the storage resource can be limited in practice hence only a small amount of files can be cached. How to improve the cache hit ratio in finite-buffer caching based on the prediction of user demands has become an important problem. In this paper, we study caching policies with finite buffer by exploiting the prediction of a user's request time, referred to as request delay information (RDI). Based on RDI, we maximize the average cache hit ratio through a Markov decision process (MDP) approach. Specifically, we formulate an MDP problem and apply a modified value iteration algorithm to find an optimal caching policy. Moreover, we provide an upper bound and a lower bound for the cache hit ratio, as well as an analytical cache hit ratio with small buffers. To address the issue that the state space can be prohibitively large in practice, we present a low-complexity heuristic caching policy that is shown to be asymptotically optimal. Simulation results show that introducing RDI may bring significant cache hit ratio gain when the buffer size is limited.

An on-demand off-path cache exploration based multipath forwarding strategy

Information-Centric Networking (ICN), such as Named Data Networking (NDN), intrinsically supports multipath forwarding and in-network caching. In order to take full advantage of both multipath forwarding and in-network caching, it requires sophisticated coordination among consumers, publishers and in-network caches for consumers to learn the fine-grained packet-level cache reachability information. The complexity introduced by the coordination as well as the cache organization hinders the full potential of NDN. Network coding enabled NDN architecture, referred to as NC-NDN, was proposed to significantly simplify the coordination design. However, the NC-NDN proposed so far only exploits on-path caches, which leaves much room for performance improvement. This work enhances NC-NDN with an On-demand Off-path Cache Exploration based Multipath Forwarding strategy, dubbed as O2CEMF, to make the best of the multipath forwarding to efficiently utilize off-path cached content. Under O2CEMF, each network node reactively explores nearby off-path cacheswhen consumers begin to request a generation of content and then maintains the reachability information at the more coarse-grained generation level of granularity instead. Then each consumer can simultaneously retrieve diverse content segments from the original content publisher(s) and the explored capable off-path caches. Our experimental studies validate that our proposed O2CEMF strategy improves the content delivery performance in terms of the average cache hit ratio, the average hops that an Interest traverses, and the average time for retrieving content at an affordable cost as compared to that in the present NC-NDN without exploiting off-path caches.

A game theoretical distributed approach for opportunistic caching strategy

Wireless network virtualization (WNV) has been a hot topic recently, which can provide the customized service to construct virtual networks for various requirements quickly and flexibly by resource sharing. However, most of the traffic is generated by duplicate downloads for a few popular contents. Meanwhile, due to the isolation in the existing WNV, these popular contents cannot be shared among various requests, and thus the congestion and overhead would be caused in the remote data center. Therefore, in order to decline the congestion and overhead from the remote data center, the caching problem is addressed in this paper, which aims to overhear and cache content opportunistically to provide the quicker response for the future requests by in-network nodes. To well understand the impact of content caching, we formulate this issue as a Markov chain, and analyze the content response ratio according to the steady-state mathematically. Furthermore, we define the content caching strategy decision as an optimization problem, and use potential game to decide caching strategy based on Nash Equilibrium in a distributed manner, denoted as Game based Opportunistic Caching Strategy (GOCS). The experimental results show the effectiveness of GOCS in terms of average response hops, average cache hit ratio, data center load reduction ratio, and average resource consumption.

A segment version transcoding for client-centric wireless mobile media streaming

An object segment similarity function is taken into account from the continuous media frames to measure the individual streaming profit of certain segment versions of a media object. Therefore, a new segment version-based transcoding (SVT) mechanism is derived for a quality of service (QoS) of client-centric media streaming in wireless mobile networks. The derived function utilizes the fuzzy similarity of certain segment versions of an object. This mechanism provides the effectiveness of reduction of the stream startup latency among segment versions, and the average access of each version. Thus, the proposed segment version transcoding mechanism reduces packet loss which in turn increases streaming performance and throughput. The performance of the partitioned segment versions is simulated and some segment versions are completed. The simulation results show that the proposed mechanism outperforms the other mechanisms in average cache hit ratio and in average startup latency ratio.

Incrementally Updatable Bloom Filter and Network Application

Bloom filters are widely used for various network applications. Because of the limited size of on-chip memory and the large volume of network traffic, Bloom filters are often required to update their contents incrementally. Two techniques have been used for this purpose: cold cache and double buffering. Cold cache outperforms double buffering in terms of the average cache ratio. However, double buffering works much better than cold cache for the worst-case cache hit ratio. In this paper, we propose a new updating scheme for Bloom filters, which updates the contents of Bloom filter incrementally while the assigned memory space is fully utilized. The proposed scheme works better than cold cache in terms of the average cache hit ratio. At the same time, it outperforms double buffering for the worst-case cache hit ratio.