DAL: A Locality-Optimizing Distributed Shared Memory System

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.