Dynamic resource brokering for multi-user query execution

We propose a new framework for resource allocation based on concepts from microeconomics. Specifically, we address the difficult problem of managing resources in a multiple-query environment composed of queries with widely varying resource requirements. The central element of the framework is a resource broker that realizes a profit by "selling" resources to competing operators using a performance-based "currency." The guiding principle for brokering resources is profit maximization. In other words, since the currency is derived from the performance objective, the broker can achieve the best performance by making the scheduling and resource allocation decisions that maximize profit. Moreover, the broker employs dynamic techniques and adapts by changing previous allocation decisions while queries are executing. In a first validation study of the framework, we developed a prototype broker that manages memory and disk bandwidth for a multi-user query workload. The performance objective for the prototype broker is to minimize slowdown with the constraint of fairness. Slowdown measures how much higher the response time is in a multi-user environment than a single-user environment, and fairness measures how even is the degradation in response time among all queries as the system load increases, Our simulation results show the viability of the broker framework and the effectiveness of our query admission and resource allocation policies for multi-user workloads.