Cache Performance Optimization of QoC Framework
DOI:
https://doi.org/10.4108/eai.13-7-2018.156594Keywords:
Load balancing, Cache management, QoE, QoC, Video platform, Cache replacement algorithmsAbstract
The main aim of this paper is based on the cache performance test of the QoC: quality of experience framework for cloud computing on the server. QoC framework is based on the server-side design and implementation of the use of hierarchical architecture. Reverse proxy technology is used to build a server cluster, which is composed of front-end access layer to achieve the server for load balancing, improve the performance of the system and the use of built-in distributed cache server. The cluster consists of the cache acceleration layer, which reduces the load of the backend database. The second database server cluster, which is constructed by the database master and slave synchronization technology, forms the data storage layer, which realizes the database read and writes separation and data redundancy. The server-side hierarchical architecture improves the performance and stability of the entire system, and has a high degree of scalability, laying a solid foundation for future expansion of system business logic and increases user volume. This paper presents new cache replacement algorithm for inconsistent video file size and then analyzes the specific needs for the multi-terminal type of QoC framework, and gives the client and server-side outline design; it describes the implementation details of the client and the server-side and finally the whole system of detailed functional and performance testing.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Scalable Information Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
Funding data
-
National Key Research and Development Program of China
Grant numbers 2017YFB0801801