• Title/Summary/Keyword: 웹 캐싱

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A Handover Protocol for the IEEE WAVE-based Wireless Networks (IEEE WAVE 기반의 무선 네트워크를 위한 핸드오버 프로토콜)

  • Choi, Jung-Wook;Lee, Hyuk-Joon;Choi, Yong-Hoon;Chung, Young-Uk
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.10 no.1
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    • pp.76-83
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    • 2011
  • The IEEE WAVE-based communication systems do not provide handover services since most of the application layer messages of a small amount containing text data that are related to safe driving. Multimedia data service such as web pages and CCTV video clips, however, require a seamless handover for continuation of a session via multiple RSUs. In this paper, we propose a new proactive handover protocol based on IEEE WAVE. According to the proposed handover protocol, the OBU notifies the old RSU of its departure from the coverage such that the old RSU forwards to the new RSU the data heading towards the OBU to be cached for the further delivery upon its entry into the new RSU's coverage. The simulation results are presented which shows the performance of the proposed protocol in terms of throughput, delivery ratio and handover delay.

Hashing Method with Dynamic Server Information for Load Balancing on a Scalable Cluster of Cache Servers (확장성 있는 캐시 서버 클러스터에서의 부하 분산을 위한 동적 서버 정보 기반의 해싱 기법)

  • Hwak, Hu-Keun;Chung, Kyu-Sik
    • The KIPS Transactions:PartA
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    • v.14A no.5
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    • pp.269-278
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    • 2007
  • Caching in a cache sorrel cluster environment has an advantage that minimizes the request and response tine of internet traffic and web user. Then, one of the methods that increases the hit ratio of cache is using the hash function with cooperative caching. It is keeping a fixed size of the total cache memory regardless of the number of cache servers. On the contrary, if there is no cooperative caching, the total size of cache memory increases proportional to the number of cache sowers since each cache server should keep all the cache data. The disadvantage of hashing method is that clients' requests stress a few servers in all the cache servers due to the characteristics of hashing md the overall performance of a cache server cluster depends on a few servers. In this paper, we propose the method that distributes uniformly client requests between cache servers using dynamic server information. We performed experiments using 16 PCs. Experimental results show the uniform distribution o

An Analysis of the Overhead of Multiple Buffer Pool Scheme on InnoDB-based Database Management Systems (InnoDB 기반 DBMS에서 다중 버퍼 풀 오버헤드 분석)

  • Song, Yongju;Lee, Minho;Eom, Young Ik
    • Journal of KIISE
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    • v.43 no.11
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    • pp.1216-1222
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    • 2016
  • The advent of large-scale web services has resulted in gradual increase in the amount of data used in those services. These big data are managed efficiently by DBMS such as MySQL and MariaDB, which use InnoDB engine as their storage engine, since InnoDB guarantees ACID and is suitable for handling large-scale data. To improve I/O performance, InnoDB caches data and index of its database through a buffer pool. It also supports multiple buffer pools to mitigate lock contentions. However, the multiple buffer pool scheme leads to the additional data consistency overhead. In this paper, we analyze the overhead of the multiple buffer pool scheme. In our experimental results, although multiple buffer pool scheme mitigates the lock contention by up to 46.3%, throughput of DMBS is significantly degraded by up to 50.6% due to increased disk I/O and fsync calls.

SBR-k(Sized-base replacement-k) : File Replacement in Data Grid Environments (SBR-k(Sized-based replacement-k) : 데이터 그리드 환경에서 파일 교체)

  • Park, Hong-Jin
    • The Journal of the Korea Contents Association
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    • v.8 no.11
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    • pp.57-64
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    • 2008
  • The data grid computing provides geographically distributed storage resources to solve computational problems with large-scale data. Unlike cache replacement policies in virtual memory or web-caching replacement, an optimal file replacement policy for data grids is the one of the important problems by the fact that file size is very large. The traditional file replacement policies such as LRU(Least Recently Used), LCB-K(Least Cost Beneficial based on K), EBR(Economic-based cache replacement), LVCT(Least Value-based on Caching Time) have the problem that they have to predict requests or need additional resources to file replacement. To solve theses problems, this paper propose SBR-k(Sized-based replacement-k) that replaces files based on file size. The proposed policy considers file size to reduce the number of files corresponding to a requested file rather than forecasting the uncertain future for replacement. The results of the simulation show that hit ratio was similar when the cache size was small, but the proposed policy was superior to traditional policies when the cache size was large.

A Hashing Scheme using Round Robin in a Wireless Internet Proxy Server Cluster System (무선 인터넷 프록시 서버 클러스터 시스템에서 라운드 로빈을 이용한 해싱 기법)

  • Kwak, Huk-Eun;Chung, Kyu-Sik
    • The KIPS Transactions:PartA
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    • v.13A no.7 s.104
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    • pp.615-622
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    • 2006
  • Caching in a Wireless Internet Proxy Server Cluster Environment has an effect that minimizes the time on the request and response of Internet traffic and Web user As a way to increase the hit ratio of cache, we can use a hash function to make the same request URLs to be assigned to the same cache server. The disadvantage of the hashing scheme is that client requests cannot be well-distributed to all cache servers so that the performance of the whole system can depend on only a few busy servers. In this paper, we propose an improved load balancing scheme using hashing and Round Robin scheme that distributes client requests evenly to cache servers. In the existing hashing scheme, if a hashing value for a request URL is calculated, the server number is statically fixed at compile time while in the proposed scheme it is dynamically fixed at run time using round robin method. We implemented the proposed scheme in a Wireless Internet Proxy Server Cluster Environment and performed experiments using 16 PCs. Experimental results show the even distribution of client requests and the 52% to 112% performance improvement compared to the existing hashing method.