Web Caching Research Articles

Understanding Application-Level Caching in Web Applications

A new form of caching, namely application-level caching, has been recently employed in web applications to improve their performance and increase scalability. It consists of the insertion of caching logic into the application base code to temporarily store processed content in memory and then decrease the response time of web requests by reusing this content. However, caching at this level demands knowledge of the domain and application specificities to achieve caching benefits, given that this information supports decisions such as what and when to cache content. Developers thus must manually manage the cache, possibly with the help of existing libraries and frameworks. Given the increasing popularity of application-level caching, we thus provide a survey of approaches proposed in this context. We provide a comprehensive introduction to web caching and application-level caching, and present state-of-the-art work on designing, implementing, and managing application-level caching. Our focus is not only on static solutions but also approaches that adaptively adjust caching solutions to avoid the gradual performance decay that caching can suffer over time. This survey can be used as a start point for researchers and developers, who aim to improve application-level caching or need guidance in designing application-level caching solutions, possibly with humans out-of-the-loop.

Correction to: pART2: using adaptive resonance theory for web caching prefetching

In the original publication, the second name of the fourth author was incorrect. It should read as ‘Jimmy Xiangji Huang’. The original publication of the article has been updated to reflect the change.

A Two-Level Intelligent Web Caching Scheme with a Hybrid Extreme Learning Machine and Least Frequently Used

The immense increase in data traffic has created several issues for the Internet community, including long delays and low throughput. Most Internet user activity occurs via web access, thus making it a major source of Internet traffic. Due to a lack of effective management schemes, Internet usage is inefficient. Advances in caching mechanisms have led to the introduction of web proxies that have improved real-time communication and cost savings. Although several traditional caching polices have been implemented to increase speed and simplicity, cache replacement accuracy remains a key limitation due to cache storage constraints. Our contribution concerns the algorithmic investigation of intelligent soft computing schemes to enhance a web proxy system to improve precision for reproducibility. This research also proposes a two-level caching scheme; the first level is least frequently used (LFU), and an extreme learning machine (ELM) is used for the second level. A traditional ELM for web caching is further optimized with object similarity factors. The proposed scheme is evaluated and compared to a traditional caching policy and its integration with intelligent caching using a well-known dataset from IRCache. The method is shown to achieve good performance in terms of high hit and byte hit rates.

A Survey on Web Tracking: Mechanisms, Implications, and Defenses

Privacy seems to be the Achilles’ heel of today’s web. Most web services make continuous efforts to track their users and to obtain as much personal information as they can from the things they search, the sites they visit, the people they contact, and the products they buy. This information is mostly used for commercial purposes, which go far beyond targeted advertising. Although many users are already aware of the privacy risks involved in the use of internet services, the particular methods and technologies used for tracking them are much less known. In this survey, we review the existing literature on the methods used by web services to track the users online as well as their purposes, implications, and possible user’s defenses. We present five main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, and other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users. For each of the tracking methods, we present possible defenses. Some of them are specific to a particular tracking approach, while others are more universal (block more than one threat). Finally, we present the future trends in user tracking and show that they can potentially pose significant threats to the users’ privacy.

Quaestor

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching.

Fundamental Storage-Latency Tradeoff in Cache-Aided MIMO Interference Networks

Caching is an effective technique to improve user perceived experience for content delivery in wireless networks. Wireless caching differs from traditional web caching in that it can exploit the broadcast nature of wireless medium and hence opportunistically change the network topologies. This paper studies a cache-aided MIMO interference network with 3 transmitters each equipped with M antennas and 3 receivers each with N antennas. With caching at both the transmitter and receiver sides, the network is changed to hybrid forms of MIMO broadcast channel, MIMO X channel, and MIMO multicast channels. We analyze the degrees of freedom (DoF) of these new channel models using practical interference management schemes. Based on the collective use of these DoF results, we then obtain an achievable normalized delivery time (NDT) of the network, an information-theoretic metric that evaluates the worst-case delivery time at given cache sizes. The obtained NDT is for arbitrary M, N and any feasible cache sizes. It is shown to be optimal in certain cases and within a multiplicative gap of 3 from the optimum in other cases. The extension to the network with arbitrary number of transmitters and receivers is also discussed.

Simulation evaluation of a relative frequency metric for web cache replacement policies

In this paper, we interest in web cache replacement policies namely “frequency-aware” policies that provide, generally, the best results in term of data movement reduction in the network. For the simple reason that they take into account one of the most significant web traffic characteristic “the access frequency”. However, the access frequency suffers from two main problems namely one-timer documents existence and cache pollution. Therefore, our aim is to replace the traditional frequency with a relative frequency; calculated using the access number and the document lifetime in the cache. Although the idea already exists in the literature, we strive to validate the relative frequency efficiency for the web proxy replacement policies. In this work, we implement three replacement policies namely least frequently used (LFU), least frequently used with dynamic aging (LFU-DA) and Greedy dual size frequency (GDSF). As well, their versions enhanced with relative frequency namely LFRU, LFRU-DA and GDSFR respectively are implemented and evaluated using synthetic and real workload. The simulation results show that the relative frequency is more effective, in terms of hit rate and byte hit rate, than the access number; i.e., the traditional frequency. Moreover, the simulation proves that the relative frequency solves the access frequency problems.

Enhanced web cache replacement policy based on data mining and RFSD scoring

By rapid growth of the Internet users and devices, the number of servers also increase simultaneously which causes the exponential increment of the internet traffic and static data. Handling these huge amounts of user requests and efficiently responding to them require high bandwidth links, powerful servers and robust equipment, which despite the availability of these requirements getting the full user satisfaction is extremely difficult and a tough challenge. In order to overcome the mentioned problem, the cache servers are being used as a suitable solution. The performance of web cache server directly depends on its replacement policies. Several cache replacement policies have been proposed in literature each having varied hit rate (HR) and byte hit rate (BHR) performances on different networks. The replacement policy proposed in this paper is a dynamic cache replacement policy which trains itself utilizing previous network logs and by exploiting the data mining clustering algorithm. Once the training step is completed, the proposed policy utilizes the normalization formulas to score each metric of the enquiries including recency, frequency, size and delay. Simulation results showed that the proposed policy has the optimum performance on different networks and it not only improved the performance of web cache server in term of HR and BHR, but also decreases the data retrieval time (Delay Ratio (DR)) of the cache servers.

Network traffic reduction mechanism for collaborative Web activities

PurposeThe purpose of this study is to reduce internet traffic when performing collaborative Web search. Mobile terminals are now in widespread use and people are increasingly using them for collaborative Web search to achieve a common goal. When performing such searches, the authors want to reduce internet traffic as much as possible, for example, to avoid bandwidth throttling that occurs when data usage exceeds a certain quota.Design/methodology/approachTo reduce internet traffic, the authors use a proxy system based on the peer cache mechanism. The proxy shares Web content stored on mobile terminals participating in an ad hoc Bluetooth network, focusing on content that is accessed multiple times from different terminals. Evaluation of the proxy’s effectiveness was measured using experiments designed to replicate realistic usage scenarios.FindingsExperimental results show that the proxy reduces internet traffic by approximately 20 per cent when four people collaboratively search the Web to find good restaurants for a social event.Originality/valueUnlike previous work on co-operative Web proxies, the authors study a form of collaborative Web caching between mobile devices within an ad hoc Bluetooth network created specifically for the purpose of sharing cached content, acting orthogonally to (and independently of) traditional hierarchical Web caching.

Performance evaluation for new web caching strategies combining LRU with score based object selection

The topic of Internet content caching regained relevance over the last years due to the increasing and widespread use of content delivery infrastructures to meet capacity and delay demands of multimedia services. In this study, we evaluate the performance of web caching strategies in terms of the achievable hit rate for realistic scenarios of large user populations. We focus on a class of score gated least recently used (SG-LRU) strategies which combine the simple update effort of the LRU policy with the flexibility to keep the most important content in the cache according to an arbitrarily predefined score function.Caching efficiency is evaluated via simulations assuming Zipf distributed requests, which have been confirmed manifold in access pattern of popular web platforms for video streaming and various other content types. In this paper, we analyze the hit rate gain of alternative web caching strategies for the standard independent request model (IRM) within the complete relevant range of three basic system parameters. The results confirm absolute hit rate gains of 10%–20% over pure LRU as realistic estimation in general, as experienced in other trace-driven case studies for special caching strategies.Moreover, we compare the performance for the independent request model with results for correlated dynamic request pattern over time, based on Wikipedia statistics that are available as daily top-1000 page requests. In this way, we show that the IRM analysis is valid for caches with a large user population, although high dynamics tends to reduce the achievable hit rate below the IRM result for smaller user communities.

Content caching with virtual spatial locality in Cellular Network

Content caching is emerging as a vital mechanism of Wireless Cellular Network. It could reduce the content transmission distance and balance the network load efficiently. Current content caching systems have exploited the content popularity with temporal locality and users’ interests to solving the major questions: when and what to cache? But cannot point out where to cache. Past proposals for addressing these problems rely on online web caching and distributed storage systems, these works focus on wired networks or wireless networks that handsets (also called users) are independent and hence perform inefficiently when faced with complicated correlation in the practical social network, where the request probabilities of the handsets are easily affected by the others. In this paper, we introduce a wireless network content caching algorithm called PreCache that consider the social influence on users’ content requests. Our design exploit social correlation to content popularity, the intuition underlying our design is that the content request probability of handset could be affected by others handsets’ action in a same social network, and thus the content popularity exhibits a feature of spatial locality in the social network, we called the feature as virtual spatial locality. We adopt virtual spatial locality of content popularity for the change of content request probability by using a social influence model. We then design the heuristic algorithm PreCache for the selection of base stations, in which the contents are cached. We evaluate the performance of the PreCache in different network setups. The results show that PreCache could improve the successful content request ratio up to 90%, and gain an average of 2 hop count reduction compared with ProbCache and Random Caching.

Enhancement of web proxy caching using discriminative multinomial Na

The discriminative multinomial Naïve Bayes classifier has been used in the field of web data classification. In this study, we attempt to improve the performance of the web cache replacement policies such as SIZE and GDSF by applying the machine learning technique for enhancing the performance of the web proxy server. In the first part of this paper supervised learning method discriminative multinomial Naïve Bayes (DMNB) classifier is used to train and classify the web log data and forecast the classes of web objects to be revisited or not. In the second part, a discriminative multinomial Naïve Bayes (DMNB) classifier is incorporated with traditional web proxy caching policies to form novel caching approaches known as DMNB-SIZE and DMNB-GDSF. This proposed method significantly improves the performances of SIZE and GDSF in terms of cache hit and byte hit ratio respectively.

Enhancement of web proxy caching using discriminative multinomial Na

The discriminative multinomial Naive Bayes classifier has been used in the field of web data classification. In this study, we attempt to improve the performance of the web cache replacement policies such as SIZE and GDSF by applying the machine learning technique for enhancing the performance of the web proxy server. In the first part of this paper supervised learning method discriminative multinomial Naive Bayes (DMNB) classifier is used to train and classify the web log data and forecast the classes of web objects to be revisited or not. In the second part, a discriminative multinomial Naive Bayes (DMNB) classifier is incorporated with traditional web proxy caching policies to form novel caching approaches known as DMNB-SIZE and DMNB-GDSF. This proposed method significantly improves the performances of SIZE and GDSF in terms of cache hit and byte hit ratio respectively.

INTELLIGENT COOPERATIVE WEB CACHING POLICIES FOR MEDIA OBJECTS BASED ON J48 DECISION TREE AND NAÏVE BAYES SUPERVISED MACHINE LEARNING ALGORITHMS IN STRUCTURED PEER-TO-PEER SYSTEMS

Web caching plays a key role in delivering web items to end users in World Wide Web (WWW).On the other hand, cache size is considered as a limitation of web caching.Furthermore, retrieving the same media object from the origin server many times consumes the network bandwidth. Furthermore, full caching for media objects is not a practical solution and consumes cache storage in keeping few media objects because of its limited capacity. Moreover, traditional web caching policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and Greedy Dual Size (GDS) suffer from caching pollution (i.e. media objects that are stored in the cache are not frequently visited which negatively affects on the performance of web proxy caching). In this work, intelligent cooperative web caching approaches based on J48 decision tree and Naive Bayes (NB) supervised machine learning algorithms are presented. The proposed approaches take the advantages of structured peer-to-peer systems where the contents of peers’ caches are shared using Distributed Hash Table (DHT) in order to enhance the performance of the web caching policy. The performance of the proposed approaches is evaluated by running a trace-driven simulation on a dataset that is collected from IRCache network. The results demonstrate that the new proposed policies improve the performance of traditional web caching policies that are LRU, LFU, and GDS in terms of Hit Ratio (HR) and Byte Hit Ratio (BHR). Moreover, the results are compared to the most relevant and state-of-the-art web proxy caching policies. Ratio (HR) and Byte Hit Ratio (BHR). Moreover, the results are compared to the most relevant and state-of-the-art web proxy caching policies.

Web Users Clustering Based on Fuzzy C-MEANS

The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means. In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.

Dynamic Semantic LFU Policy with Victim tracer (DSLV): A Customizing Technique for Client Cache

This work proposes an augmented facility to the existing LFU-based web cache replacement policy. The proposed policy takes into account three additional factors such as dynamicity of the cached pages, semantic relation between the cached and the incoming pages, and least frequently used web pages and the impact of the victimized pages to decide the pages to be purged. Hence, it is named as DSLV to reveal this strategy. This policy is intended to improve the performance of only the client cache. By introducing dynamicity levels, obsolete pages are filtered from the caching process. The semantic module identifies the semantic relationship between the cached pages. Therefore, the new incoming page and the cached pages can be customized as per the interest of the users and their access pattern. The best variation in LFU policy that avoids cache pollution is adopted. Victims are the pages that are removed from the cache for want of space. These victimized pages are monitored so that the victim module identifies the reason for getting victimized and tries to tune the policy for better performance measures. Simulation results ranked by the performance metrics namely file hit ratio, speedup, delay saving ratio, and number of evictions disclose strong induction for this kind of customized technique for client cache as an efficient enhancement to the already established LFU replacement policy.

On Sample-Path Staleness in Lazy Data Replication

We analyze synchronization issues between two point processes, one modeling data churn at an information source and the other periodic downloads to its replica (e.g., search engine, web cache, distributed database). Due to pull-based synchronization, the replica experiences recurrent staleness, which translates into some form of penalty stemming from its reduced ability to perform consistent computation and/or provide up-to-date responses to customer requests. We model this system under non-Poisson update/refresh processes and obtain sample-path averages of various metrics of staleness cost, generalizing previous results and exposing novel problems in this field.

Intelligent Web Proxy Cache Replacement Algorithm Based on Adaptive Weight Ranking Policy via Dynamic Aging

Rapid growth in network services and vast usage of Internet worldwide have led to an increase in network traffic and created bottleneck over the internet. Such traffic results in an increase of access time of web documents, server response latency, reduced network bandwidth and slow response time for popular websites. Web cache is an essential optimization technique used to reduce response time and improve performance. However due to its limited size and cost of cache comparable to other storage devices, cache replacement algorithm is used to determine and evict page when the cache is full to create space for new pages. Several algorithms had been introduced and their performances are important in producing high speed web caching. However, their performances are not well optimized. This work proposes a hybrid method that optimize cache replacement algorithm using Naive Bayes (NB) based approach. Naive Bayes is an intelligent method that depends on Bayes’ probability theory integrated with Adaptive Weight Ranking Policy (AWRP) via dynamic aging factor to improve the response time and network performance. From the pseudocode of the proposed algorithm, it is observed that the complexity of the algorithm is O(n)which is linear, hence the response time is considergood. Performance evaluations based on hit rate and byte hit rate for new the method over conventional methods with real data will be conducted for validation and verification.

A Novel Probability based Approach for Optimized Prefetching

As the World Wide Web carries on to grow up rapidly in size and popularity, web traffic and network bottlenecks are more important issues in the networked world. The continued enhancement in demand for items on the World Wide Web causes severe overloading in many sites, network congestion, delay in perceived latency and network bottleneck. Many users have no patience in waiting more than a few seconds for downloading a web page, that's why Web traffic reduction system is very necessary in today World Wide Web for accessing the websites efficiently with the facility of existing networks. Web caching is an effective method to improve the performance of the World Wide Web but in today's World Wide Web caching method alone is not enough because of World Wide Web has grown quickly from a simple information-sharing mechanism to a rich collection of dynamic objects and multimedia data. The web prefetching is used to improve the performance of the proxy server. Prefetching predict web object that is expected to be requested in the near future and store them in advance, thus the response time of the user request is reduced. To improve the performance of the proxy server, this paper proposed a new framework which combines the caching system and prefetching technique and also optimize the prefetching with the help of probability. In this paper, we use the dataset for the experiment which is collected from ircache.net proxy server and give the result with the comparison of other technique of prefetching.

An Efficient Resource Management Algorithm for Information Centric Networks

The build-in caching capability is a common and important feature of the innovative Information or Content- Centric Networks (ICN/CCN) architectures to enable effective content distribution. ICN caching has several distinguished characteristics compared with traditional Web caching: ubiquitous, transparent and inner-grained. These unique features bring new challenges to the ICN caching design. Current LRU-Everywhere caching strategy which caches popular objects redundantly along the content delivery path highly degrades the performance of the ICN caching. With particular focus to reduce caching redundancy and improve cached content availability, we propose a novel cache scheme, called An Optimized Cache Replacement Algorithm for Information Centric Networks (OCRICN). In particular, OCRICN managed to allocate the caching resource by giving higher opportunity of caching those objects with higher popularity, smaller size and more retrieving cost. We formulate the content placement problem to benchmark the globally optimized performance. Simulation results show that the performance of our scheme outperforms the widely used schemes in term of cache size, chunk size and access pattern on the caching performance. The results demonstrate that the proposed scheme is efficient in maximizing bandwidth utilization and minimizing average query delay.