To balance multiple scheduling performance requirements on parallel computer systems, traditional job schedulers use many parameters that can be configured to define job or queue priorities. Offering many parameters seems flexible, but in reality tuning the values for the parameters is highly challenging. To simplify the task of resource management, we propose goal-oriented policies, which allow system administrators to specify high-level performance objectives, rather than tuning low-level scheduling parameters. We study the design of goal-oriented policies, including (1) appropriate multi-objective models for specifying trade-offs between objectives, (2) efficient search algorithms for searching the best schedule at each scheduling decision point, and (3) appropriate performance measures to be optimized in the objectives with respect to two common performance requirements: preventing starvation and favoring shorter jobs. We compare goal-oriented policies with widely used backfill policies. Policies are evaluated by simulation using ten monthly workloads that ran on a Linux cluster (IA-64) from NCSA. Our results show that by automatically optimizing performance according to the given objectives through search, goal-oriented policies can simultaneously outperform FCFS-backfill and LXF-backfill, which are designed in favor of the maximum wait and average slowdown, respectively.

This paper is concerned with the design of goal-oriented scheduling policies that deal with multiple goals on production parallel systems. Several objective models are compared, including two new models that allow an explicit specification of tradeoff among different goals and a hierarchical model studied in our previous paper. We focus on two performance goals commonly placed on production systems: starvation prevention and favoring shorter jobs. In this paper, we study how to best formulate high-level performance goals, in an attempt to provide guideline for defining the objectives

To balance performance goals and allow administrators to declaratively specify high-level performance goals, we apply complete search algorithms to design on-line job scheduling policies for workloads that run on parallel computer systems. We formulate a hierarchical two-level objective that contains two goals commonly placed on parallel computer systems: (1) minimizing the total excessive wait; (2) minimizing the average slowdown. Ten monthly workloads that ran on a Linux cluster (IA-64) from NCSA are used in our simulation of policies. A wide range of measures are used for performance evaluation, including the average slowdown, average wait, maximum wait, and new measures based on excessive wait. For the workloads studied, our results show that the best search-based scheduling policy (i.e., DDS/lxf/dynB) reported here simultaneously beats both FCFS-backfill and LXF-backfill, each roughly providing a lower bound on maximum wait and the average slowdown, respectively, among backfill policies

This paper addresses open job scheduling questions for the challenge workloads that run on the large scale parallel systems at supercomputer centers. Simulation results for six recent one-month job traces from the NCSA Origin 2000 (O2K) system are used to evaluate (I) the experimentally tuned NCSA LSF* policy, (2) the FCFS-backfill policy, (3) the Priority-backfill policy with alternative priority functions and with limited pre-emption to provide immediate service to each arriving job, and (4) the spatial equipartitioning (EQspatial) policy with an optional modification to reduce the maximum waiting time for the largest jobs in the challenge workloads. Measurements on the O2K validate the simulation results for two of the policies. The priority-backfill policy with immediate service and a starvation-free priority measure that favors short jobs is shown to be the most promising if jobs cannot adapt to changing processor allocations at runtime, but EQspatial provides significantly better 95th percentile waiting time.

The performance potential of run-to-completion (RTC) parallel processor scheduling policies is investigated by examining whether (1) application execution rate characteristics such as average parallelism (avg) and processor working set (PWS) and/or (2) limited preemption can be used to improve the performance of these policies. We address the first question by comparing policies (previous as well as new) that differ only in whether or not they use execution rate characteristics and by examining a wider range of the workload parameter space than previous studies. We address the second question by comparing a simple two-level queueing policy with RTC scheduling in the second level queue against RTC policies that don't allow any preemption and against dynamic equiallocation(EQ).