• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.227-232
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• 2008

General web caches save documents temporarily into themselves on the basis of those documents. And when a corresponding document exists within the cache on user's request. web cache sends the document to corresponding user. On the contrary. when there is not any document within the cache, web cache requests a new document to the related server to copy the document into the cache and then turn it back to user. Here, web cache uses a replacement policy to change existing document into a new one due to exceeded capacity of cache. Typical replacement policy includes document-based LRU or LFU technique and other various replacement policies are used to replace the documents within cache effectively. However. these replacement policies function only with regard to the time and frequency of document request. not considering the popularity of each web site. Based on replacement policies with regard to documents on frequent requests and the popularity of each web site, this paper aims to present the document replacement policies with regard to the popularity of each web site, which are suitable for latest network environments to enhance the hit-ratio of cache and efficiently manage the contents of cache by effectively replacing documents on intermittent requests by new ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.78-87
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• 2015

Content-Centric Networking(CCN) is a new networking paradigm to search for the routing information needed to find a data from the content name, unlike conventional IP networks. In CCN, the mobility management, one of the CCN challenges, is consists of consumer mobility and content provider mobility. Among both, in the case of the content provider mobility, it requires too much overhead and time to update routing information on the corresponding routers. In this paper, we propose Popularity-based Pushing CCN(PoPCoN) which considers the content popularity to support effective mobility of content provider in CCN. Our proposed algorithm shortens content download time for the consumer and reduces the network overhead during mobility as compared to the existing approaches.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.49-55
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• 2023

In this study, models for predicting the popularity of mukbang content on YouTube were proposed, and factors influencing the popularity of mukbang content were identified through post-analysis. To accomplish this, information on 22,223 pieces of content was collected from top mukbang channels in terms of subscribers using APIs and Pretty Scale. Machine learning algorithms such as Random Forest, XGBoost, and LGBM were used to build models for predicting views and likes. The results of SHAP analysis showed that subscriber count had the most significant impact on view prediction models, while the attractiveness of a creator emerged as the most important variable in the likes prediction model. This confirmed that the precursor factors for content views and likes reactions differ. This study holds academic significance in analyzing a large amount of online content and conducting empirical analysis. It also has practical significance as it informs mukbang creators about viewer content consumption trends and provides guidance for producing high-quality, marketable content.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.9
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• pp.530-538
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• 2002

In this paper, we propose a new caching strategy for web servers. The proposed algorithm collects on]y the statistics of the requested file, for example the popularity, when a request arrives. And, at times, only files with higher popularity are cached all together. Because the cache remains unchanged until the cache is made newly, web server can use very efficient data structure for cache to determine whether a file is in the cache or not. This increases greatly tile efficiency of cache manipulation. Furthermore, the experiment that is performed with real log files built by web servers shows that the cache hit ratio and the cache hit ratio are better than those produced by LRU. The proposed algorithm has a drawback such that the cache hit ratio may decrease when the popularity of files that is not in the cache explodes instantaneously. But in our opinion, such explosion happens infrequently, and it is easy to implement the web servers to adapt them to such unusual cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1498-1507
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• 2017

According to recent report, global mobile data traffic is expected to increase by 11 times from 2016 to 2020. Moreover, this growth is expected to be driven mainly by mobile video traffic which is expected to account for about 70% of the total mobile data traffic. To cope with enormous mobile traffic, we need to understand video traffic's characteristic. Recently, the repetitive requests of some popular content such as popular YouTube videos cause a enormous network traffic overheads. If we constitute a network with the nodes capable of content caching based on the content popularity, we can reduce the network overheads by using the cached content for every request. Through device-to-device, multicast, and helpers, the video throughput can improve about 1.5~2 times and prefix caching reduces the playback delay by about 0.2~0.5 times than the conventional method. In this paper, we introduce some recent work on content popularity-based caching techniques in wireless networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5673-5691
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• 2019

Information centric mobile network can significantly improve the data retrieving efficiency by caching contents at mobile edge. However, the cache pollution attack can affect the data obtaining process severely by requiring unpopular contents deliberately. To tackle the problem, we design an algorithm of mitigating cache pollution attacks in information centric mobile network. Particularly, the content popularity distribution statistic is proposed to detect abnormal behavior. Then a probabilistic caching strategy based on abnormal behavior is applied to dynamically maintain the steady-state distribution for content visiting probability and achieve the purpose of defense. The experimental results show that the proposed scheme can achieve higher request hit ratio and smaller latency for false locality content pollution attack than the CacheShield approach and the baseline approach where no mitigation approach is applied.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.113-120
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• 2012

Today, Internet users can easily create and share the digital contents with others through various online content sharing services such as YouTube. So, many portal sites are flooded with lots of user created contents (UCC) in various media such as texts and videos. Estimating popularity of UCC is a crucial concern to both users and the site administrators. This paper proposes a method to predict the popularity of Internet articles, a kind of UCC, using the dynamics of the online contents themselves. To analyze the dynamics, we regarded the access counts of Internet posts as the popularity of them and analyzed the variation of the access counts. We derived a model to predict the popularity of a post represented by the time series of access counts, which is based on an exponential function. According to the experimental results, the difference between the actual access counts and the predicted ones is not more than 10 for 20,532 posts, which cover about 90.7% of the test set.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.148-157
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• 2002

Caching in meta-search engines can improve the response time of users' request. We describe the cache scheme in our meta-search engine in terms of its architecture and operational flow. In particular, we propose a popularity-driven cache algorithm that utilizes popularities of queries to determine cached data to be purged. The popularity is a value that represents the normalized occurrence frequency of user queries. This paper presents how to collect popular queries and how to calculate query popularities. An empirical performance evaluation of the popularity-driven caching with the traditional schemes (i.e., least recently used (LRU) and least frequently used (LFU)) has been carried out on a collection of real data. In almost all cases, the proposed replacement policy outperforms LRU and LFU.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1189-1202
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• 1999

As compared with VOD data, NOD article data has the following characteristics: it is created at any time, has a short life cycle, is selected as not one article but several articles by a user, and has high access locality in time. Because of these intrinsic features, user access patterns of NOD article data are different from those of VOD. Thus, building NOD system using the existing techniques of VOD system leads to poor performance. In this paper, we analysis the log file of a currently running electronic newspaper, show that the popularity distribution of NOD articles is different from Zipf distribution of VOD data, and suggest a new popularity model of NOD article data MS-Zipf(Multi-Selection Zipf) distribution and its approximate solution. Also we present a life cycle model of NOD article data, which shows changes of popularity over time. Using this life cycle model, we develop LLBF (Largest Life-cycle Based Frequency) prefetching algorithm and analysis he performance by simulation. The developed LLBF algorithm supports the similar level in hit-ratio to the other prefetching algorithms such as LRU(Least Recently Used) etc, while decreasing the number of data replacement in article prefetching and reducing the overhead of the prefetching in system performance. Using the accurate user access patterns of NOD article data, we could analysis correctly the performance of NOD server system and develop the efficient policies in the implementation of NOD server system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.