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Modern cluster managers like Borg, Omega and Kubernetes rely on the state-reconciliation principle to be highly resilient and extensible. In these systems, all cluster-management logic is embedded in a loosely coupled collection of microservices called controllers. Each controller independently observes the current cluster state and issues corrective actions to converge the cluster to a desired state. However, the complex distributed nature of the overall system makes it hard to build reliable and correct controllers – we find that controllers face myriad reliability issues that lead to severe consequences like data loss, security vulnerabilities, and resource leaks. We present Sieve, the first automatic reliability-testing tool for cluster-management controllers. Sieve drives controllers to their potentially buggy corners by systematically and extensively perturbing the controller’s view of the current cluster state in ways it is expected to tolerate. It then compares the cluster state’s evolution with and without perturbations to detect safety and liveness issues. Sieve’s design is powered by a fundamental opportunity in state-reconciliation systems – these systems are based on state-centric interfaces between the controllers and the cluster state; such interfaces are highly transparent and thereby enable fully-automated reliability testing. To date, Sieve has efficiently found 46 serious safety and liveness bugs (35 confirmed and 22 fixed) in ten popular controllers with a low false-positive rate of 3.5%. 

Modern datacenter infrastructures are increasingly architected as a cluster of loosely coupled services. The cluster states are typically maintained in a logically centralized, strongly consistent data store (e.g., ZooKeeper, Chubby and etcd), while the services learn about the evolving state by reading from the data store, or via a stream of notifications. However, it is challenging to ensure services are correct, even in the presence of failures, networking issues, and the inherent asynchrony of the distributed system. In this paper, we identify that partial histories can be used to effectively reason about correctness for individual services in such distributed infrastructure systems. That is, individual services make decisions based on observing only a subset of changes to the world around them. We show that partial histories, when applied to distributed infrastructures, have immense explanatory power and utility over the state of the art. We discuss the implications of partial histories and sketch tooling for reasoning about distributed infrastructure systems.