Proxy Caching Research Articles

Caching technologies for Tier-2 sites: A UK perspective

Pressures from both WLCG VOs and externalities have led to a desire to "simplify" data access and handling for Tier-2 resources across the Grid. This has mostly been imagined in terms of reducing book-keeping for VOs, and total replicas needed across sites. One common direction of motion is to increasing the amount of remote-access to data for jobs, which is also seen as enabling the development of administratively-cheaper Tier-2 subcat-egories, reducing manpower and equipment costs. Caching technologies are often seen as a "cheap" way to ameliorate the increased latency (and decreased bandwidth) introduced by ubiquitous remote-access approaches, but the usefulness of caches is strongly dependant on the reuse of the data thus cached. We report on work done in the UK at four GridPP Tier-2 sites - ECDF, Glasgow, RALPP and Durham - to investigate the suitability of transparent caching via the recently-rebranded XCache (Xrootd Proxy Cache) for both ATLAS and CMS workloads, and to support workloads by other caching approaches (such as the ARC CE Cache).

Advancing throughput of HEP analysis work-flows using caching concepts

High throughput and short turnaround cycles are core requirements for efficient processing of data-intense end-user analyses in High Energy Physics (HEP). Together with the tremendously increasing amount of data to be processed, this leads to enormous challenges for HEP storage systems, networks and the data distribution to computing resources for end-user analyses. Bringing data close to the computing resource is a very promising approach to solve throughput limitations and improve the overall performance. However, achieving data locality by placing multiple conventional caches inside a distributed computing infrastructure leads to redundant data placement and inefficient usage of the limited cache volume. The solution is a coordinated placement of critical data on computing resources, which enables matching each process of an analysis work-flow to its most suitable worker node in terms of data locality and, thus, reduces the overall processing time. This coordinated distributed caching concept was realized at KIT by developing the coordination service NaviX that connects an XRootD cache proxy infrastructure with an HTCondor batch system. We give an overview about the coordinated distributed caching concept and experiences collected on prototype system based on NaviX.

PENERAPAN TEKNOLOGI CACHE SERVER BERBASIS IoT DENGAN RASPBERRY Pi3 MENGGUNAKAN METODE FORWARD CHAINNING

With the rapid technological development, the growing number of Internet clients over time, the internet access response is slow and it takes filtering of a few websites that can harm users. For that required cache proxy server to speed up internet access time and website filtering. The use of proxy servers usually still use the standard configuration on the proxy server. This configuration can be set or modified (tuning) in such a way as to create a proxy server that is reliable and have better performance. The play on the proxy server is done by changing some parameters that are inside the proxy server application and adapted to the needs of usage on the internet network. The application used to create this proxy server is squid that runs on raspberry pi3 with a rasbian operating system. Squid applications are mostly used in local and internet networks for storing web cache, blocking multiple websites, managing bandwidth up to access restrictions.

Optimizing an Efficient Use of Internet Bandwidth for Higher Learning Institutions in Ghana

This paper examines the availability of Internet services in Higher Learning Institutions (HLIs) for developing countries such as Ghana, with respect to the cost and usage. We looked at the source of Internet services and the bandwidth package for some Higher Learning Institutions in Accra, Ghana. One of the key objectives of this study is to design and simulates an efficient Internet bandwidth usage for Higher Learning Intuitions. We examined the cost of bandwidth package offered by the Internet service providers to these higher institutions. In looking at the cost we also included the bandwidth packages offered, the type of Internet connection and the service level agreement with the ISPs. In the study, we looked at how the Internet is distributed to these institutions and the equipment used in the distribution of the Internet services. Ipswich Whatsup Gold software was used to monitor the network infrastructure for one of the institutions in the study. We went further to monitor the bandwidth consumption for Ghana Technology University College, one of the institutions in the study, to monitor their bandwidth consumption and traffic flow in the network. In designing an efficient bandwidth management for the Higher Learning Institution, we model and configured a network in Packet tracer using a proxy cache box. This technique allows for an efficient bandwidth planning capacity to manage the bandwidth consumptions. At the end of the study, we modeled a network using cache box to restrict and minimize bandwidth usage and thus making it less expensive for the Higher Learning Institutions.

A sequential Kriging method assisted by trust region strategy for proxy cache size optimization of the streaming media video data due to fragment popularity distribution

The Kriging method based on machine learning is an attractive tool. In this work, a sequential Kriging method assisted by trust region strategy (SKM-TRS) is proposed to solve unconstrained black-box problems. In this SKM-TRS, the complex and expensive objective function is approximated by Kriging model. And then, a sub-optimization problem, which is constructed by Kriging and a distance factor, is minimized by the improved trust region strategy to determine next update point during each iteration cycle. The proposed method is verified by ten well-known benchmark optimization problems and a proxy cache size optimization of the streaming media video data due to fragment popularity distribution. The final test results demonstrate the efficiency and robustness of the SKM-TRS in contrast with Efficient Global Optimization (EGO), Trust Region Implementation in Kriging-based optimization with Expected improvement (TRIKE) and an Adaptive Metamodel based Global Optimization algorithm (AMGO).

The Development of the Education Related Multimedia Whitelist Filter using Cache Proxy Log Analysis

The purpose of this study was to show the development of the education related multimedia whitelist filter using cache proxy log analysis. Regulation of bandwidth utilization should be used according to needs and main goals or process business of the organization. For Higher Education Institutions, access to education and science-related contents must be prioritized. Large size content access needs a right regulation, especially for multimedia contents. This research focuses on the development of whitelist filter based on an analysis of internet files access logs taken from squid proxy logs from April to November 2017. This technique can recognize 1,98% of the logs are direct links related to education and 0 % are indirect links to the education-related multimedia files and 98,02 % are links to non-education related multimedia files.

Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

This paper reports on how intelligent Greedy-Dual approaches based on supervised machine learning were used to improve the web proxy caching performance. The proposed intelligent Greedy-Dual approaches predict the significant web objects’ demand for web proxy caching using Naive Bayes (NB), decision tree (C4.5), or support vector machine (SVM) classifiers. Accordingly, the proposed intelligent Greedy-Dual approaches effectively make the cache replacement decision based on the trained classifiers. The trace-driven simulation results indicated that in terms of byte hit ratio and/or hit ratio, the performance of each of the conventional Greedy-Dual-Size-Frequency (GDSF) and Greedy-Dual-Size (GDS) was noticeably enhanced by applying the proposed Greedy-Dual approaches on five real datasets.

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

The increased usage of World Wide Web leads to increase in network traffic and create a bottleneck over the internet performance. For most people, the accessing speed or the response time is the most critical factor when using the internet. Reducing response time was done by using web proxy cache technique that storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. But, the cache size is limited, so cache replacement algorithms are used to remove pages from the cache when it is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomised Policy, etc. may discard essential pages just before use. Furthermore, using conventional algorithms cannot be well optimized since it requires some decision to evict intelligently before a page is replaced. Hence, this paper proposes an integration of Adaptive Weight Ranking Policy (AWRP) with intelligent classifiers (NB-AWRP-DA and J48-AWRP-DA) via dynamic aging factor. To enhance classifiers power of prediction before integrating them with AWRP, particle swarm optimization (PSO) automated wrapper feature selection methods are used to choose the best subset of features that are relevant and influence classifiers prediction accuracy. Experimental Result shows that NB-AWRP-DA enhances the performance of web proxy cache across multi proxy datasets by 4.008%,4.087% and 14.022% over LRU, LFU, and FIFO while, J48-AWRP-DA increases HR by 0.483%, 0.563% and 10.497% over LRU, LFU, and FIFO respectively. Meanwhile, BHR of NB-AWRP-DA rises by 0.9911%,1.008% and 11.5842% over LRU, LFU, and FIFO respectively while 0.0204%, 0.0379% and 10.6136 for LRU, LFU, FIFO respectively using J48-AWRP-DA.

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

The increased usage of World Wide Web leads to increase in network traffic and create a bottleneck over the internet performance. For most people, the accessing speed or the response time is the most critical factor when using the internet. Reducing response time was done by using web proxy cache technique that storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. But, the cache size is limited, so cache replacement algorithms are used to remove pages from the cache when it is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomised Policy, etc. may discard essential pages just before use. Furthermore, using conventional algorithms cannot be well optimized since it requires some decision to evict intelligently before a page is replaced. Hence, this paper proposes an integration of Adaptive Weight Ranking Policy (AWRP) with intelligent classifiers (NB-AWRP-DA and J48-AWRP-DA) via dynamic aging factor. To enhance classifiers power of prediction before integrating them with AWRP, particle swarm optimization (PSO) automated wrapper feature selection methods are used to choose the best subset of features that are relevant and influence classifiers prediction accuracy. Experimental Result shows that NB-AWRP-DA enhances the performance of web proxy cache across multi proxy datasets by 4.008%,4.087% and 14.022% over LRU, LFU, and FIFO while, J48-AWRP-DA increases HR by 0.483%, 0.563% and 10.497% over LRU, LFU, and FIFO respectively. Meanwhile, BHR of NB-AWRP-DA rises by 0.9911%,1.008% and 11.5842% over LRU, LFU, and FIFO respectively while 0.0204%, 0.0379% and 10.6136 for LRU, LFU, FIFO respectively using J48-AWRP-DA.

Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

Site in a box: Improving the Tier 3 experience

The Pacific Research Platform is an initiative to interconnect Science DMZs between campuses across the West Coast of the United States over a 100 gbps network. The LHC @ UC is a proof of concept pilot project that focuses on interconnecting 6 University of California campuses. It is spearheaded by computing specialists from the UCSD Tier 2 Center in collaboration with the San Diego Supercomputer Center. A machine has been shipped to each campus extending the concept of the Data Transfer Node to a cluster in a box that is fully integrated into the local compute, storage, and networking infrastructure. The node contains a full HTCondor batch system, and also an XRootD proxy cache. User jobs routed to the DTN can run on 40 additional slots provided by the machine, and can also flock to a common GlideinWMS pilot pool, which sends jobs out to any of the participating UCs, as well as to Comet, the new supercomputer at SDSC. In addition, a common XRootD federation has been created to interconnect the UCs and give the ability to arbitrarily export data from the home university, to make it available wherever the jobs run. The UC level federation also statically redirects to either the ATLAS FAX or CMS AAA federation respectively to make globally published datasets available, depending on end user VO membership credentials. XRootD read operations from the federation transfer through the nearest DTN proxy cache located at the site where the jobs run. This reduces wide area network overhead for subsequent accesses, and improves overall read performance. Details on the technical implementation, challenges faced and overcome in setting up the infrastructure, and an analysis of usage patterns and system scalability will be presented.

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.

Transparent Proxy Cache server using Raspberry Pi

Objectives: The online traffic surge rates have rapid growth in the recent years. The rate has gone high, making the service providers unable to provide the quality of service they assure. The surging cannot be controlled and the only solution lays on the other side, so the providers are forced to choose another methodology of delivering the service in quality. Methods/ Statistical Analysis: In this paper the process of implementing an alternative is proposed. The work is concentrated on creating a Raspberry Pi to work as a transparent cache server with the smart phones enacting as the client. Findings: The issues of streaming videos are ending up being more critical to customers as they pay for over-the-top substance yet, still experience exceptionally less than impressive streams. To get a quick response and decrease access latency, utilizing a cache memory is prudent. A transparent proxy cache server wipes out numerous disadvantage of ordinary methodology. Transparent proxy cache server can be conveyed in two levels, one at switch level and another at router level. The attention is primarily on http result reserve which stores URLs of Previously accessed query results. These caches might be deployed in various machines, acting about as a proxy cache cluster, or exists together in the same machine. In this proposed framework we introduce a straight forward web store middle server, to upgrade the execution. The only work to be done regarding the promise of content availability is continuous monitoring of the cache. Application/Improvement: The proposed methodology can serve the right speed of content delivery by facilitating access of data from the cache while giving no hint that the data has been retrieved through IP-spoofing of the server which here is a small sized computer the Raspberry Pi.

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.

Distributed proxy cache technology based on autonomic computing in smart cities

With the rapid growth of Internet services in smart cities, large network flow has caused network congestion among users, thereby deteriorating network service quality and user experience. In this study, a distributed proxy cache management platform based on autonomous decision is proposed. The platform realizes highly efficient automatic cache management by taking the advantages of autonomous perception and decision. First, the platform establishes the autonomous management framework of proxy cache on the basis of the idea of feedback loop. Next, the URL is distributed evenly among cache nodes through the hash ring storage mechanism with virtual nodes. Cache status adjustment strategies are made based on historical status sequences and the gray prediction model. A method that is used to predict content hot-rank based on domain name set is proposed to reduce loads of cache nodes. Second, the platform designs a distributed proxy cache management system by depending on the autonomous management framework of proxy cache and the cache management mechanism of autonomous decision, and the Cache Hot Spots Migrate algorithm is suggested for the dynamic migration and integration of virtual nodes on the hash ring. A system knowledge base is established through status sequence and corresponding adjustment strategies. Finally, experimental results on the hash ring, content hot-rank prediction, and adjustment of autonomous decision of cache status are analyzed, thereby confirming that the distributed proxy cache management mechanism based on autonomous decision could manage cache nodes effectively and automatically, and could improve the overall performance of the system.

A Novel Cache Replacement Policy for Web Proxy Caching System Using Web Usage Mining

Network congestion remains one of the main barriers to the continuing success of the internet and Web based services. In this background, proxy caching is one of the most successful solutions for civilizing the performance of Web since it reduce network traffic, Web server load and improves user perceived response time. Here, the most popular Web objects that are likely to be revisited in the near future are stored in the proxy server thereby it improves the Web response time and saves network bandwidth. The main component of Web caching is it cache replacement policy. It plays a key role in replacing existing objects when there is no room for new one especially when cache is full. Moreover, the conventional replacement policies are used in Web caching environments which provide poor network performance. These policies are suitable for memory caching since it involves fixed sized objects. But, Web caching which involves objects of varying size and hence there is a need for an efficient policy that works better in Web cache environment. Moreover, most of the existing Web caching policies have considered few factors and ignored the factors that have impact on the efficiency of Web proxy caching. Hence, it is decided to propose a novel policy for Web cache environment. The proposed policy includes size, cost, frequency, ageing, time of entry into the cache and popularity of Web objects in cache removal policy. It uses the Web usage mining as a technique to improve Web caching policy. Also, empirical analyses shows that proposed policy performs better than existing policies in terms of various performance metrics such as hit rate and byte hit rate.

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 Naïve 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.

The Evolution of Cloud Computing in ATLAS

The ATLAS experiment at the LHC has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This paper describes the overall evolution of cloud computing in ATLAS. The current status of the virtual machine (VM) management systems used for harnessing Infrastructure as a Service resources are discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for managing VM images across multiple clouds, a system for dynamic location-based discovery of caching proxy servers, and the usage of a data federation to unify the worldwide grid of storage elements into a single namespace and access point. The usage of the experiment's high level trigger farm for Monte Carlo production, in a specialized cloud environment, is presented. Finally, we evaluate and compare the performance of commercial clouds using several benchmarks.

A Review of Web Caching Techniques and Caching Algorithms for Effective and Improved Caching

Internet today has become a victim of its own success. As the internet is reaching a global community, the World Wide Web is becoming a global-scale data dissemination system. There has been an increase in user latency, bandwidth utilization and server loads because of the increased number of World Wide Web users. Web caching is a technology for overcoming such performance bottlenecks by storing copies of popular web objects closer to users instead of deliberately accessing them from origin servers. Our study aims to review few caching architectures. These architectures include proxy caching, cooperative caching, adaptive caching, push caching and active caching. Furthermore, as it has been repeatedly observed, same data is transmitted over same network links time and again to thousands of users. Such redundancies desire the need for caching algorithms that optimally utilize the finite cache space. Chapter 1 discusses the introduction to the study and requirement of such solutions as we further proceed to discuss those solutions in Chapter 2. Chapter 3 discusses about metrics and factors that influence caching performance and Chapter 4 discusses algorithms that are used for caching