通过协作移动,区域和云计算进行社会大数据管理
Afzal Badshah1, Ameen Banjar2, Safa Habibullah2
1Department of Software Engineering, University of Sargodha, Sargodha, Punjab, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
这项研究提出了一个协作移动,区域和云计算结构,用于从智能设备管理社会大数据 (SBD). 该方法减少了网络压力,提高了性能,降低了社交媒体平台的成本.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络工程 网络工程
背景情况:
- 智能设备产生大量的社会大数据 (SBD),压倒主流网络并降低性能.
- 目前的网络基础设施与SBD的大量和本地化性质作斗争,导致成本和延迟增加.
- 很大一部分SBD来自本地地区,不需要立即远程服务器迁移.
研究的目的:
- 提出一个集成移动边缘计算 (MEC),区域计算 (RC) 和云计算 (CC) 的协作计算框架.
- 解决SBD在社交媒体平台 (SMP) 造成的性能下降和成本问题.
- 为SBD优化数据访问和传输策略.
主要方法:
- 开发了一种混合计算模型,将MEC,RC和CC结合起来,用于SBD管理.
- 实施了分层数据存储和访问策略,优先考虑最近的可用资源.
- 计划大数据传输到非高峰时段,以缓解高峰时段的网络拥堵.
主要成果:
- 在社交网络平台上显著减少网络延迟和响应时间.
- 通过优化数据处理和减少对远程云服务器的依赖,实现了巨大的成本节约.
- 有效地缓解了SBD对主流和社交网络基础设施的绩效影响.
结论:
- 拟议的协作MEC,RC和CC结构有效地管理SBD挑战.
- 这种混合方法提高了社交媒体平台的性能,并降低了运营成本.
- 优化数据位置和传输时间表对于处理智能设备生态系统的需求至关重要.
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