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Friday, November 16 • 1:10pm - 1:30pm
Optimizing network topologies with monadic execution contexts

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Constellation's goal is to horizontally scale blockchain protocols using a DAG (directed acyclic graph) and reputation/trust model. We’re using Scala, Akka, Cats, Algebird and Kubernetes to build an asynchronous consensus service. We’re building off HoneyBadger ACS and CHECO with inspirations from GraphX, Pregel, and PageRank to scale consensus with a reactive execution graph. Conventional validation is split into granular monadic execution contexts to distribute the workload, and network topology undergoes dynamic rebalancing. Reputation is measured both deterministically from chain data as well as through node to node labels capturing ‘influence’ on the network in a fashion similar to Twitter. This is in contrast with a typical blockchain which relies on either linear state transitions / sharding, and proof of work / proof of stake.

avatar for Ryle Goehausen

Ryle Goehausen

VP of Engineering, Constellation Labs
Scala / Python / Spark / Machine Learning / Performance engineering. Early Databricks and Spark adopter. Working on high performance cryptocurrency and reputation modeling.
avatar for Wyatt Meldman-Floch

Wyatt Meldman-Floch

CTO, Constellation Labs
Software engineer focused on machine learning, distributed systems and functional programming. Founded Constellation Labs, applying methods distributed graph processing to distributed ledger technology. Applying combinatorial models of distributed computing.

Friday November 16, 2018 1:10pm - 1:30pm PST