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

The effect of real workloads and stochastic workloads on the performance of allocation and scheduling algorithms in 2D mesh multicomputers

The performance of the existing non-contiguous processor allocation strategies has been traditionally carried out by means of simulation based on a stochastic workload model to generate a stream of incoming jobs. To validate the performance of the existing algorithms, there has been a need to evaluate the algorithms' performance based on a real workload trace. In this paper, we evaluate the performance of several well-known processor allocation and job scheduling strategies based on a real workload trace and compare the results against those obtained from using a stochastic workload. Our results reveal that the conclusions reached on the relative performance merits of the allocation strategies when a real workload trace is used are in general compatible with those obtained when a stochastic workload is used

Understanding User Behavior: From HPC to HTC

AbstractIn this paper, we investigate the differences and similarities in user job submission behavior in High Performance Computing (HPC) and High Throughput Computing (HTC). We consider job submission behavior in terms of parallel batch-wise submissions, as well as delays and pauses in job submission. Our findings show that modeling user-based HTC job submission behavior requires knowledge of the underlying bags of tasks, which is often unavailable. Furthermore, we find evidence that subsequent job submission behavior is not influenced by the different complexities and requirements of HPC and HTC jobs

Extending Scojo-PECT by migration based on system-level checkpointing

In recent years, a significant amount of research has been done on job scheduling in high performance computing area. Parallel jobs have different running time and require a different number of processors, thus jobs need to be scheduled and packed to improve system utilization. Scojo-PECT is a job scheduler which provides service guarantees by using coarse-grain time sharing. However, Scojo-PECT does not provide process migration. We extend the Scojo-PECT by migrating parallel jobs based on system-level checkpointing. We investigate different cases in the Scojo-PECT scheduling algorithm where migration based on system-level checkpointing can be used to improve resource utilization and reduce job response time. Our experimental results show reduction of relative response times on medium jobs over the results of the original Scojo-PECT scheduler and the long jobs do not suffer any disadvantage

A Queue Simulation Tool for a High Performance Scientific Computing Center

The NASA Center for Computational Sciences (NCCS) at the Goddard Space Flight Center provides high performance highly parallel processors, mass storage, and supporting infrastructure to a community of computational Earth and space scientists. Long running (days) and highly parallel (hundreds of CPUs) jobs are common in the workload. NCCS management structures batch queues and allocates resources to optimize system use and prioritize workloads. NCCS technical staff use a locally developed discrete event simulation tool to model the impacts of evolving workloads, potential system upgrades, alternative queue structures and resource allocation policies

The swiss army knife of job submission tools: grid-control

Grid-control is a lightweight and highly portable open source submission tool that supports virtually all workflows in high energy physics (HEP). Since 2007 it has been used by a sizeable number of HEP analyses to process tasks that sometimes consist of up 100k jobs. grid-control is built around a powerful plugin and configuration system, that allows users to easily specify all aspects of the desired workflow. Job submission to a wide range of local or remote batch systems or grid middleware is supported. Tasks can be conveniently specified through the parameter space that will be processed, which can consist of any number of variables and data sources with complex dependencies on each other. Dataset information is processed through a configurable pipeline of dataset filters, partition plugins and partition filters. The partition plugins can take the number of files, size of the work units, metadata or combinations thereof into account. All changes to the input datasets or variables are propagated through the processing pipeline and can transparently trigger adjustments to the parameter space and the job submission. While the core functionality is completely experiment independent, integration with the CMS computing environment is provided by a small set of plugins.Comment: 8 pages, 7 figures, Proceedings for the 22nd International Conference on Computing in High Energy and Nuclear Physic