Geo-Distribution of Actor-Based Services
Abstract
Many service applications use actors as a programming model for the middle tier, to simplify synchronization,
fault-tolerance, and scalability. However, efficient operation of such actors in multiple, geographically distant
datacenters is challenging, due to the very high communication latency. Caching and replication are essential
to hide latency and exploit locality; but it is not a priori clear how to combine these techniques with the actor
programming model.
We present Geo, an open-source geo-distributed actor system that improves performance by caching
actor states in one or more datacenters, yet guarantees the existence of a single latest version by virtue of
a distributed cache coherence protocol. Geo’s programming model supports both volatile and persistent
actors, and supports updates with a choice of linearizable and eventual consistency. Our evaluation on several
workloads shows substantial performance benefits, and confirms the advantage of supporting both replicated
and single-instance coherence protocols as configuration choices. For example, replication can provide fast,
always-available reads and updates globally, while batching of linearizable storage accesses at a single location
can boost the throughput of an order processing workload by 7x.