一句话：提出 NEOFog 非易失性处理器架构与负载均衡算法，将 WSN 雾内处理能力最高提升 8 倍。
能量采集 WSN 中，节点因位置、采集器角度和电源不同而能量不均，现有方法未考虑非易失性计算带来的新特性。本文提出利用非易失性的优化并实现为 NEOFog 系统架构，分析面向非易失处理的 WSN 在数据与程序分布上的权衡变化，说明非易失处理与非易失 RF 如何改变以计算或通信为中心的方法收益；并提出面向非易失传感系统的负载均衡算法，同时平衡计算与通信需求。评估显示，NEOFog 的 NV-aware 优化将雾内处理能力提升 4.2 倍，虚拟化节点 3 倍复用时可达 8 倍。
Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOFog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how non-volatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOFog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.
DOI 原文 ·
@inproceedings{paperbot4248,
title = {NEOFog},
author = {Kaisheng Ma and Xueqing Li and Mahmut Taylan Kandemir and Jack Sampson and Vijaykrishnan Narayanan and Jinyang Li and Tongda Wu and Zhibo Wang and Yongpan Liu and Yuan Xie},
booktitle = {ASPLOS},
year = {2018},
doi = {10.1145/3173162.3177154}
}