Cologne: A Declarative Distributed Constraint Optimization Platform

TL;DR

Cologne is a declarative platform that simplifies and accelerates distributed constraint optimization by integrating declarative networking with constraint solving, demonstrated through cloud and wireless network case studies.

Contribution

Introduces Cologne, a novel declarative platform combining distributed Datalog with constraint solving for efficient distributed optimization.

Findings

Supports diverse policy-based optimizations in distributed systems

Reduces code complexity significantly compared to imperative approaches

Achieves high efficiency with low overhead and rapid convergence

Abstract

This paper presents Cologne, a declarative optimization platform that enables constraint optimization problems (COPs) to be declaratively specified and incrementally executed in distributed systems. Cologne integrates a declarative networking engine with an off-the-shelf constraint solver. We have developed the Colog language that combines distributed Datalog used in declarative networking with language constructs for specifying goals and constraints used in COPs. Cologne uses novel query processing strategies for processing Colog programs, by combining the use of bottom-up distributed Datalog evaluation with top-down goal-oriented constraint solving. Using case studies based on cloud and wireless network optimizations, we demonstrate that Cologne (1) can flexibly support a wide range of policy-based optimizations in distributed systems, (2) results in orders of magnitude less code compared to imperative implementations, and (3) is highly efficient with low overhead and fast convergence times.