Modern cluster management systems like Kubernetes and Openstack grapple with hard combinatorial optimization problems: load balancing, placement, scheduling, and configuration. Currently, developers tackle these problems by designing custom application-specific algorithms---an approach that is proving unsustainable, as ad-hoc solutions both perform poorly and introduce overwhelming complexity to the system, making it challenging to add important new features. We propose a radically different architecture, where programmers drive cluster management tasks declaratively, using SQL queries over cluster state stored in a relational database. These queries capture in a natural way both constraints on the cluster configuration as well as optimization objectives. When a cluster reconfiguration is required at runtime, our tool, called Weave, synthesizes an encoding of these queries into an optimization model, which it solves using an off-the-shelf solver. We demonstrate Weave's efficacy by powering three production-grade systems with it: a Kubernetes scheduler, a virtual machine management solution, and a distributed transactional datastore. Using Weave, we expressed complex cluster management policies in under 20 lines of SQL, easily added new features to these existing systems, and significantly improved placement quality and convergence times.


Paper URL: https://arxiv.org/abs/1909.03130


September, 2019


Related projects

Research Areas

  • cluster management
  • constraint programming
  • distributed systems




arXiv preprint arXiv:1909.03130