Most cloud services and distributed applications rely on hashing algorithms that allow dynamic scaling of a robust and efficient hash table. Examples include AWS, Google Cloud and BitTorrent. Consistent and rendezvous hashing are algorithms that minimize key remapping as the hash table resizes. While memory errors in large-scale cloud deployments are common, neither algorithm offers both efficiency and robustness. Hyperdimensional Computing is an emerging computational model that has inherent efficiency, robustness and is well suited for vector or hardware acceleration. We propose Hyperdimensional (HD) hashing and show that it has the efficiency to be deployed in large systems. Moreover, a realistic level of memory errors causes more than 20% mismatches for consistent hashing while HD hashing remains unaffected.
翻译:大多数云服务和分布式应用程序都依赖散列算法,这种算法允许动态缩放稳健高效的散列表,例如AWS、谷歌云和BitTorrent。一致和会合散列是随着散列表的调整而尽量减少关键重新绘图的算法。虽然大规模云层部署中的记忆错误很常见,但这两种算法都无法提供效率和稳健性。多维计算法是一个新兴的计算模型,具有内在效率、稳健性,并且非常适合矢量或硬件加速。我们提议采用超维(HD)散列法,并表明在大型系统中部署该算法的效率。此外,实际的记忆错误水平导致20%以上的连续散列不匹配,而HD hashing则不受影响。