Cache Replacement Method Research Articles

Intelligent Cooperative Caching at Mobile Edge based on Offline Deep Reinforcement Learning

Cooperative edge caching enables edge servers to jointly utilize their cache to store popular contents, thus drastically reducing the latency of content acquisition. One fundamental problem of cooperative caching is how to coordinate the cache replacement decisions at edge servers to meet users’ dynamic requirements and avoid caching redundant contents. Online deep reinforcement learning (DRL) is a promising way to solve this problem by learning a cooperative cache replacement policy using continuous interactions (trial and error) with the environment. However, the sampling process of the interactions is usually expensive and time-consuming, thus hindering the practical deployment of online DRL-based methods. To bridge this gap, we propose a novel Delay-awarE Cooperative cache replacement method based on Offline deep Reinforcement learning (DECOR), which can exploit the existing data at the mobile edge to train an effective policy while avoiding expensive data sampling in the environment. A specific convolutional neural network is also developed to improve the training efficiency and cache performance. Experimental results show that DECOR can learn a superior offline policy from a static dataset compared to an advanced online DRL-based method. Moreover, the learned offline policy outperforms the behavior policy used to collect the dataset by up to 35.9%.

An adaptive cache management approach in ICN with pre-filter queues

In Information-Centric Networking (ICN), transmission does not depend on the ends of communication, but on the content itself. In-network cache plays an important role in ICN, it empowers nodes in ICN better mobility. It also shortens serving path, lightens load of server, reduces traffic and time delay, its efficiency seriously affects performance of entire network, and thus cache management in ICN draws much attention of researchers recently. Although there are a lot approaches have been proposed, a cache management scheme with better adaptability and less cost remains to be studied. To this end, we propose State-value-and-Cache-rate-Method (SCMethod) to manage cache resources in ICN, which comprises cache deployment policy and cache replacement policy. In cache level, we add pre-filter queues in front of cache queue to filter out popular content to store in cache. In the perspective of node, according to factors that node’s state and relative location on data forwarding path, we define node state and cache rate to select nodes with maximum of state value or minimum cache rate to cache content, and effectively mitigate data redundancy. In order to increase dynamics and adaptive of system for mobility of nodes, we employ cache hit ratio as feedback to dynamically adjust the number of pre-filter queues in every node. With pre-filter queues, we improve Least Recently Used (LRU) cache replacement method. We conduct extensive experiments in simulator Icarus (Saino et al., 2014) with both tree topology and realistic internet topologies, define four metrics to quantitatively evaluate performance of SCMethod and verify its efficacy of reducing latency and load. Simulation results demonstrate that SCMethod we proposed outperform several classic cache schemes of ICN.

Popularity prediction caching based on logistic regression in vehicular content centric networks

To improve the network performance caused by mobility and sporadic connectivity in the vehicular network, vehicular content centric network (VCCN) is proposed by applying CCN into the vehicular network. The open in-network caching of CCN makes nodes cache contents cooperatively to facilitate information access. To improve the network performance such as access delay and hit ratio, road side units (RSUs) should try to cache more popular contents and provide better service for mobile users. This paper aims to propose a novel cache replacement policy - popularity prediction content caching (PPCC) for VCCN. In PPCC, we incorporate the future popularity of contents into our decision making. By learning the popularity of contents, we propose a cache replacement method based on logistic regression for RSUs in order to store those frequently access contents. The input data are related to the inherent characters of the received interests and the output is the predicted content popularity which guarantees that only popular contents are cached in the network infrastructures (i.e., RSUs). Simulation evaluations demonstrate that our scheme is very effective with higher cache hit, lower access latency and higher caching efficiency compared to other state-of-the-art schemes.

Proactive cache replacement technique for mobile networks based on genetic programming

In the mobile environment, the movement of users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges to information access. Caching is considered one of the most important concepts to deal with these challenges. There are two general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date; and cache replacement method chooses the cached item(s) which should be deleted from the cache when the cache is full. The aim of this work is to propose a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were performed using Network Simulator 2 to evaluate the effectiveness of the proposed approach, and the results are compared with the existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.

An intelligent database proactive cache replacement policy for mobile communication system based on genetic programming

SummaryIn the mobile environment, the movement of the users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges in information access. Caching is considered one of the most important concepts to deal with these challenges. There are 2 general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date and cache replacement method chooses the cached element(s) that would be removed from the cache once the cache stays full. The aim of this work is to introduce a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, and validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were carried by NS2 simulator to assess the efficiency of the proposed method, and the outcomes are judged against existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.

PPC: Popularity Prediction Caching in ICN

Thriving future network conceives embedding the ubiquitous in-network caching. The fine-grained cache behavior reveals an intimate relationship among contents in the same stream—sequenced contents have similar cache behavior. This letter presents a novel cache replacement method named popularity prediction caching (PPC) for chunk-level cache by discovering the relevance among video chunks in information centric network from the perspective of user watching behavior. PPC predicts and caches the future most popular chunks and evicts those with least future popularity in a linear complexity. Simulations in a GEANT model show that PPC outperforms cache policy based on content popularity, least recently used, least frequently used, and first in first out.

Trend-Aware Video Caching Through Online Learning

This paper presents Trend-Caching, a novel cache replacement method that optimizes cache performance according to the trends of video content. Trend-Caching explicitly learns the popularity trend of video content and uses it to determine which video it should store and which it should evict from the cache. Popularity is learned in an online fashion and requires no training phase, hence it is more responsive to continuously changing trends of videos. We prove that the learning regret of Trend-Caching (i.e., the gap between the hit rate achieved by Trend-Caching and that by the optimal caching policy with hindsight) is sublinear in the number of video requests, thereby guaranteeing both fast convergence and asymptotically optimal cache hit rate. We further validate the effectiveness of Trend-Caching by applying it to a movie.douban.com dataset that contains over 38 million requests. Our results show significant cache hit rate lift compared to existing algorithms, and the improvements can exceed 40% when the cache capacity is limited. Furthermore, Trend-Caching has low complexity.

An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking

Named Data Networking (NDN) is a candidate next-generation Internet architecture designed to overcome the fundamental limitations of the current IP-based Internet, in particular strong security. The ubiquitous in-network caching is a key NDN feature. However, pervasive caching strengthens security problems namely cache pollution attacks including cache poisoning (i.e., introducing malicious content into caches as false-locality) and cache pollution (i.e., ruining the cache locality with new unpopular content as locality-disruption).In this paper, a new cache replacement method based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to mitigate the cache pollution attacks in NDN. The ANFIS structure is built using the input data related to the inherent characteristics of the cached content and the output related to the content type (i.e., healthy, locality-disruption, and false-locality). The proposed method detects both false-locality and locality-disruption attacks as well as a combination of the two on different topologies with high accuracy, and mitigates them efficiently without very much computational cost as compared to the most common policies.

Segment-Based Proxy Caching for Streaming Media Delivery

At present the majority of streaming media files is large and it requires a lot of network bandwidth and disk bandwidth. We propose an adaptive segment-based method, the cache replacement method and the multi technologies combined optimized transmission policy. Simulation results indicate that they are highly efficient methods for use of caching proxy server resources, reducing the startup latency and save of bandwidth of backbone network.

Research on Small World Model in P2P Networks

Peer-to-Peer (P2P) networks define a series of rules and applications, where peers are absolutely equal. Peers can both supply server to others and consume server from others. But the efficient data retrieval in a peer-to-peer system is a challenge problem, such as freenet, which aims to provide freedom of speech through a peer-to-peer network with strong protection of anonymity. Therefore, this paper designed an enhanced cache replacement method to improve the network. After simulations, it shows this method can increase the availability and decrease the average number of hops per request. Finally, this paper test the performance of Freenet with enhance cache replacement method in stimulate network environment. The results give a reference for research on P2P networks.

C2BF: Cache-based Counting Bloom Filter for Precise Matching in Network Packet Processing

Bloom filter is widely used in network packet processing due to its fast lookup speed and small memory cost. However, the non-negligible false positive rate and the difficulty of online update still prevent it from extensive utilization. In this paper, we propose a cache-based counting Bloom filter architecture, C2BF, which is not only easy to update online but also benefical for fast verification for precise matching. We also present a high speed hardware C2BF architecture with off-chip memory and fast cache replacement method. This paper includes three contributions: 1) compressed CBF implementation and its updating algorithm; 2) pattern grouping for higher cache hit rate; 3) onchip cache organization and replacement policy. Experiments show that our prototype of C2BF reduces more than 70% of the verification processing time with cache design compared with traditional schemes without cache.

Cache replacement method based on lowest access cost for location dependent query

Because of the user's mobility and the location dependency of data,new challenge has been brought to cache replacement strategy for Location Dependent Query(LDQ).Based on the detailed analysis of the space location characteristics of Location Dependent Data(LDD) and several typical replacement strategies of location dependent cache,the authors proposed a prioritized approach cache replacement based on the lowest access cost(PLAC),the PLAC took some important factors into account such as access probabilities,update rates,data distance,valid scope.To ensure the maximum utilization of limited cache,the PLAC cache replacement strategy decided which data would be replaced according to the value of the lowest cost function.The contrast experiments show that the PLAC increases cache hit rate and shortens query average response time more effectively than other location dependent cache replacement strategies.

캐쉬메카니즘을 이용한 시맨틱 스키마 데이터 처리

In semantic web information system like ontology that access distributed information from network, efficient query processing requires an advanced caching mechanism to reduce the query response time. P2P network system have become an important infra structure in web environment. In P2P network system, when the query is initiated, reducing the demand of data transformation to source peer is important aspect of efficient query processing. Caching of query and query result takes a particular advantage by adding or removing a query term. Many of the answers may already be cached and can be delivered to the user right away. In web environment, semantic caching method has been proposed which manages the cache as a collection of semantic regions. In this paper, we propose the semantic caching technique in cluster environment of peers. Especially, using schema data filtering technique and schema similarity cache replacement method, we enhanced the query processing efficiency.

A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end users in an appropriate time frame. Web caching was introduced in the 1990s to help decrease network traffic, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5–10% increase in Correct Classification Ratio (CCR) than NNPCR. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper, we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.