Parallel job scheduling policies to improve fairness : a case study.

Balancing fairness, user performance, and system performance is a critical concern when developing and installing parallel schedulers. Sandia uses a customized scheduler to manage many of their parallel machines. A primary function of the scheduler is to ensure that the machines have good utilization and that users are treated in a 'fair' manner. A separate compute process allocator (CPA) ensures that the jobs on the machines are not too fragmented in order to maximize throughput. Until recently, there has been no established technique to measure the fairness of parallel job schedulers. This paper introduces a 'hybrid' fairness metric that is similar to recently proposed metrics. The metric uses the Sandia version of a 'fairshare' queuing priority as the basis for fairness. The hybrid fairness metric is used to evaluate a Sandia workload. Using these results, multiple scheduling strategies are introduced to improve performance while satisfying user and system performance constraints

OStrich: Fair Scheduling for Multiple Submissions

International audienceCampaign Scheduling is characterized by multiple job submissions issued from multiple users over time. This model perfectly suits today's systems since most available parallel environments have multiple users sharing a common infrastructure. When scheduling individually the jobs submitted by various users, one crucial issue is to ensure fairness. This work presents a new fair scheduling algorithm called OStrich whose principle is to maintain a virtual time-sharing schedule in which the same amount of processors is assigned to each user. The completion times in the virtual schedule determine the execution order on the physical processors. Then, the campaigns are interleaved in a fair way by OStrich. For independent sequential jobs, we show that OStrich guarantees the stretch of a campaign to be proportional to campaign's size and the total number of users. The stretch is used for measuring by what factor a workload is slowed down relative to the time it takes on an unloaded system. The theoretical performance of our solution is assessed by simulating OStrich compared to the classical FCFS algorithm, issued from synthetic workload traces generated by two different user profiles. This is done to demonstrate how OStrich benefits both types of users, in contrast to FCFS

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

Fairness-aware scheduling on single-ISA heterogeneous multi-cores

Single-ISA heterogeneous multi-cores consisting of small (e.g., in-order) and big (e.g., out-of-order) cores dramatically improve energy- and power-efficiency by scheduling workloads on the most appropriate core type. A significant body of recent work has focused on improving system throughput through scheduling. However, none of the prior work has looked into fairness. Yet, guaranteeing that all threads make equal progress on heterogeneous multi-cores is of utmost importance for both multi-threaded and multi-program workloads to improve performance and quality-of-service. Furthermore, modern operating systems affinitize workloads to cores (pinned scheduling) which dramatically affects fairness on heterogeneous multi-cores. In this paper, we propose fairness-aware scheduling for single-ISA heterogeneous multi-cores, and explore two flavors for doing so. Equal-time scheduling runs each thread or workload on each core type for an equal fraction of the time, whereas equal-progress scheduling strives at getting equal amounts of work done on each core type. Our experimental results demonstrate an average 14% (and up to 25%) performance improvement over pinned scheduling through fairness-aware scheduling for homogeneous multi-threaded workloads; equal-progress scheduling improves performance by 32% on average for heterogeneous multi-threaded workloads. Further, we report dramatic improvements in fairness over prior scheduling proposals for multi-program workloads, while achieving system throughput comparable to throughput-optimized scheduling, and an average 21% improvement in throughput over pinned scheduling