DAL: A Locality-Optimizing Distributed Shared Memory System


Gábor Németh, Dániel Géhberger, and Péter Mátray, Ericsson Research


Latency-sensitive applications like virtualized telecom and industrial IoT systems require a service for ultrafast state externalization to become cloud-native. In this paper we propose a distributed shared memory system, called DAL, which achieves the lowest possible latency by transparently co-locating individual data items with applications working on them. Upon changes in data access patterns, the system automatically adapts data locations to keep the number of remote operations at a minimum. By avoiding the costs of network transport and using shared memory communication, the system can achieve 1 μs data access latency. We envision DAL as a platform component which enables latency-sensitive applications to take advantage of the cloud.

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@inproceedings {203318,
author = {G{\'a}bor N{\'e}meth and D{\'a}niel G{\'e}hberger and P{\'e}ter M{\'a}tray},
title = {{DAL}: A Locality-Optimizing Distributed Shared Memory System},
booktitle = {9th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 17)},
year = {2017},
address = {Santa Clara, CA},
url = {https://www.usenix.org/conference/hotcloud17/program/presentation/nemeth},
publisher = {{USENIX} Association},