Adaptive Scheduling with Parallelism Feedback

Multiprocessor scheduling in a shared multiprogramming environment can be structured as two-level scheduling, where a kernel-level job scheduler allots processors to jobs and a user-level thread scheduler schedules the work of a job on the allotted processors. In this context, the number of processors allotted to a particular job may vary during the job's execution, and the thread scheduler must adapt to these changes in processor resources. For overall system efficiency, the thread scheduler should also provide parallelism feedback to the job scheduler to avoid allotting a job more processors than it can use productively. This paper provides an overview of several adaptive thread schedulers we have developed that provide provably good history-based feedback about the job's parallelism without knowing the future of the job. These thread schedulers complete the job in near-optimal time while guaranteeing low waste. We have analyzed these thread schedulers under stringent adversarial conditions, showing that the thread schedulers are robust to various system environments and allocation policies. To analyze the thread schedulers under this adversarial model, we have developed a new technique, called trim analysis, which can be used to show that the thread scheduler provides good behavior on the vast majority of time steps, and performs poorly on only a few. When our thread schedulers are used with dynamic equipartitioning and other related job scheduling algorithms, they are O(1)-competitive against an optimal offline scheduling algorithm with respect to both mean response time and makespan for batched jobs and nonbatched jobs, respectively. Our algorithms are the first nonclairvoy-ant scheduling algorithms to offer such guarantees.