به سوی برنامه ریزی شغلی متعادل و پایدار برای ابررایانه های تولید
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20362||2013||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Parallel Computing, Volume 39, Issue 12, December 2013, Pages 753–768
Job scheduling on production supercomputers is complicated by diverse demands of system administrators and amorphous characteristics of workloads. Specifically, various scheduling goals such as queuing efficiency and system utilization are usually conflicting and thus need to be balanced. Also, changing workload characteristics often impact the effectiveness of the deployed scheduling policies. Thus it is challenging to design a versatile scheduling policy that is effective in all circumstances. In this paper, we propose a novel job scheduling strategy to balance diverse scheduling goals and mitigate the impact of workload characteristics. First, we introduce metric-aware scheduling, which enables the scheduler to balance competing scheduling goals represented by different metrics such as job waiting time, fairness, and system utilization. Second, we design a scheme to dynamically adjust scheduling policies based on feedback information of monitored metrics at runtime. We evaluate our design using real workloads from supercomputer centers. The results demonstrate that our scheduling mechanism can significantly improve system performance in a balanced, sustainable fashion.
Job scheduling is a critical task on large-scale computing platforms. The job scheduling policy directly influences the satisfaction of both users and system owners. Moreover, the success of a job scheduling policy is largely determined by the satisfaction of these stakeholders. Users are concerned with fast job turnaround and fairness, while system owners are interested in system utilization. Also, production supercomputing centers are starting to face new challenges in scheduling, such as avoiding failure interrupts and achieving energy efficiency. All these considerations, which are quantified by system metrics, are related but often conflict with one another. Even worse, the priorities differ from machine to machine and from time to time, further complicating the design of a comprehensive job scheduling policy. Traditional scheduling policies can achieve specific scheduling goals but not balance them well. For example, using “first-come, first served” (FCFS) achieves good job fairness but results in poor response times and resource fragmentation. On the other hand, using “short-job first” (SJF) achieves best response time in theory but violates job fairness and causes job starvation. Essentially, these approaches attempt to favor a fixed combination of some priorities while ignoring others. Some traditional schedulers provide mechanisms to switch between these policies when particular boundary conditions are encountered; however, this approach provides only a coarse ability to refine goal-based priorities. Moreover, user satisfaction and system performance are not considered in a holistic fashion. Typically, job prioritizing and resource allocation are separated into two subsequent phases in decision making. This division greatly constrains the resource allocation process. For example, when a high-priority job suffers from insufficient resources, it reserves the resources while draining others; yet, these resources could be used to execute other low-priority jobs, thereby improving system performance. This kind of resource draining causes external fragmentation. While backfilling helps in this case, it only mitigates fragmentation already created by this division . Another issue of job scheduling concerns dynamic workload. Even though we may identify a policy to achieve our integrated goals well for one workload, the policy may fail for a different workload. Although event-driven simulation can be used to evaluate the aggregate effect of a scheduling policy on a historical workload trace, it cannot provide much guidance when workload properties change dynamically at runtime. Motivated by these issues, we propose an adaptive metric-aware job scheduling mechanism. Our objectives are twofold. First, we develop a metric-aware job scheduling mechanism to prioritize jobs and allocate resources in an integrated fashion. Here, “metric-aware” means we take the targeted performance metrics into account when configuring a specific scheduling policy. Moreover, the prioritized jobs are allowed to be altered in a limited fashion during the resource allocation phase. This approach improves flexibility in schedule creation. Second, we introduce an adaptive tuning mechanism into job scheduling, which allows the scheduling policy to change dynamically at runtime based on workload characteristics. By monitoring the performance metrics at runtime, the scheduler can adjust its scheduling policy to favor the metrics that are less satisfied recently, thereby mitigating the impact of changing workload characteristics on the scheduling policy. For example, if the system utilization rate is below a certain threshold (e.g., a longer-term average), our scheduling system will adjust its policy to favor system utilization more than other metrics. We implemented our design in the production resource management system called Cobalt . We evaluated our design using recent real job traces from multiple production supercomputers. The experimental results show that our approach can achieve significant and sustainable performance improvement compared with traditional scheduling strategies. The remainder of this paper is organized as follows. Section 2 discusses some related work. Section 3 reviews the motivation of our work and describes our methodology. Section 4 illustrates our experimental results. Section 5 summarizes the paper and briefly discusses future work.