Agamotto: Scheduling of Deadline-Oriented Incremental Query Execution under Uncertain Resource Price

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Abstract

Incremental query processing is widely used in data warehouses and streaming systems. While many optimization techniques are developed to generate incremental query plans, the scheduling support for incremental processing remains preliminary. Typically, execution is triggered with fixed frequencies specified by the user. In this paper, we propose a novel scheduling problem for incremental query execution under a deadline, assuming the resource has a fluctuating and unforeseen price. We propose two naive solutions as well as a prophet scheduler that foresees the future. We present an end-to-end system Agamotto that models future probabilities offline with a Markov Decision Process (MDP) and makes cost-based and dynamic scheduling decisions online. We show how Agamotto can be extended to handle a workflow of dependent queries, so that they can all incrementally execute in an asynchronous fashion. Experiments show that Agamotto consistently outperforms the naive solutions, and the achieved cost is on average 10x closer to the theoretical lower bound provided by the prophet scheduler.