Provably Efficient Two-Level Adaptive Scheduling

Abstract

Multiprocessor scheduling in a shared multiprogramming environment can be structured in two levels, where a kernel-level job scheduler allots processors to jobs and a user-level thread scheduler maps the ready threads of a job onto the allotted processors. This paper presents two-level scheduling schemes for scheduling "adaptive" multithreaded jobs whose parallelism can change during execution. The AGDEQ algorithm uses dynamic-equipartioning (DEQ) as a job-scheduling policy and an adaptive greedy algorithm (A-Greedy) as the thread scheduler. The ASDEQ algorithm uses DEQ for job scheduling and an adaptive work-stealing algorithm (A-Steal) as the thread scheduler. AGDEQ is suitable for scheduling in centralized scheduling environments, and ASDEQ is suitable for more decentralized settings. Both two-level schedulers achieve O(1)-competitiveness with respect to makespan for any set of multithreaded jobs with arbitrary release time. They are also O(1)competitive for any batched jobs with respect to mean response time. Moreover, because the length of the scheduling quantum can be adjusted to amortize the cost of context-switching during processor reallocation, our schedulers provide control over the scheduling overhead and ensure effective utilization of processors.

Provably-Efficient Online Adaptive Scheduling of Parallel Jobs Based on Simple Greedy Rules

“…In our previous work [18], we show that AGDEQ (the combination of DEQ and A-GREEDY) is O(1)-competitive with respect to mean response time for batched jobs when ≤ P. The following lemma from [18] bounds the mean response time of a batched job set with ≤ P.…”

“…In the first part where ≤ P, GRAD always uses DEQ as job scheduler. In this case, we apply the result in [18], and show that GRAD is O(1)-competitive. In the second part where > P, GRAD uses both RR and DEQ.…”

“…On the other hand, if the quantum length is set to be small enough to capture frequent changes in instantaneous parallelism, the proportion of time spent reallotting processors among the jobs increases, resulting in a high scheduling overhead. Our previous work in [18] presents a two-level adaptive scheduler AGDEQ, which uses DEQ as the job scheduler, and A-GREEDY as the thread scheduler. Instead of using instantaneous parallelism, AGDEQ uses the job's utilization in the past as feedback.…”

Provably Efficient Online Nonclairvoyant Adaptive Scheduling

“…One major issue of parallel job scheduling is how to efficiently share multiple processors among a number of competing jobs, while ensuring each job a required quality of services (see e.g. [15,8,7,11,9,18,24,19,25,22,31,28,23,13,14,5]). Efficiency and fairness are two important performance measures, where efficiency is often quantified in terms of makespan and mean response time.…”

“…This paper presents a scheduling algorithm -GRAD, which offers provable efficiency in terms of makespan and mean response time by allotting each job a fair share of processor resources. Our algorithm is nonclairvoyant [11,9,18,13], i.e. it assumes nothing about the release time, the execution time, and the parallelism profile of jobs.…”