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斯适应马尔科夫模型与在云中的基于波动分析的大数据流模型进行了大修
M Ananthi1, Annapoorani Gopal2, K Ramalakshmi3
1Department of Computer Science and Business Systems, Sri Sairam Engineering College, Chennai, India.
Big data
|October 30, 2023
概括
对大数据流的准确资源预测是具有挑战性的. 斯适应马尔科夫模型 (GAMM) 修复波动分析 (OFA) 框架提高了基于云的系统的效率并减少了错误.
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算 云计算
- 数据流数据流.
背景情况:
- 在大数据流应用中准确预测资源使用情况仍然是一个复杂的挑战.
- 现有的资源扩展技术往往遭受效率低下的扩展,不准确的预测,高延迟和长时间的运行时间.
研究的目的:
- 开发一个高效的框架,用于云系统中的大数据流.
- 以降低错误率有效管理时间有限的大数据流应用程序.
主要方法:
- 介绍了高斯适应的马尔科夫模型 (GAMM) 修订波动分析 (OFA) 框架.
- 利用特征提取的门关策略,使非线性数据分布和波动分析的脂肪收解决方案成为可能.
- 开发一个分层架构,以简化流媒体应用程序中的资源预测.
主要成果:
- 拟议的GAMM-OFA流模型证明了针对不同措施的验证和可比结果.
- 该框架旨在提高大数据流资源管理的效率.
结论:
- GAMM-OFA框架提供了一种新的方法来解决现有的大数据流资源管理的局限性.
- 这项研究有助于在基于云的流媒体环境中更有效,更准确地预测资源.
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