HedgeRank:对异质性的意识,在边缘的个性化PageRank的节能分区
1School of Software, Soongsil University, Seoul 06978, Republic of Korea.
Micromachines
|September 28, 2023
概括
在边缘设备上,HedgeRank通过在异质硬件上分割计算来优化个性化PageRank (PPR). 这种节能技术显著提高了性能,并减少了图形处理任务的功耗.
科学领域:
- 计算机科学 计算机科学
- 图形算法 图形算法
- 边缘计算 边缘计算
背景情况:
- 在图形分析中,个性化PageRank (PPR) 对源节点的重要性至关重要.
- 在边缘设备上执行PPR对于数据隐私和低延迟是必要的.
- 现有的PPR方法在资源有限的边缘硬件上面临性能和能源低效.
研究的目的:
- 提出HedgeRank,一种新的异质意识分区技术,用于在边缘执行节能的个性化PageRank.
- 为了应对不同边缘设备上PPR不同计算/内存需求的挑战.
主要方法:
- HedgeRank 分区 个性化的 PageRank 子进程.
- 它根据其计算能力和能源效率智能地将这些子进程分配给适当的边缘设备.
- 该技术利用了低功耗和高性能边缘设备的组合.
主要成果:
- HedgeRank提高了个性化PageRank执行时间高达26.7%.
- 与最先进的方法相比,它可以减少高达15.2%的能源消耗.
- 显示了基于边缘的图形处理性能和能源效率的显著改进.
结论:
- 在异质边缘计算系统上部署个性化PageRank,HedgeRank提供了一个有效的解决方案.
- 拟议的分区策略优化了资源利用,以提高效率.
- 直接在边缘设备上实现隐私保护和低延迟图形分析.
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