12-16 April 2010
Uppsala University
Europe/Stockholm timezone

Improved Task Scheduling for Distributed Data Analysis Systems

Apr 15, 2010, 10:00 AM
20m
Room X (Uppsala University)

Room X

Uppsala University

Oral Scientific results obtained using distributed computing technologies Data Management

Speaker

Mr Jakub MOSCICKI (CERN)

Description

In this work, we present an evaluation of a late-binding scheduler for HEP distributed analysis, where data is distributed globally with multiple replicas in a non-uniform way. We employ the late-binding technique in order to optimize job placements thereby minimizing the per-job time-to-completion. We evaluate different scheduling and prioritization strategies, and evaluate the approach using a prototype system implemented with the Ganga end-user tool and the DIANE scheduling framework applied to ATLAS distributed analysis.

Detailed analysis

In the context of ATLAS distributed analysis, the presently used
workload management systems do not optimally place jobs. At worst jobs
are pre-assigned to a single execution site at submit time, and at
best jobs are assigned to run on a limited number of closely-located
sites (so-called "Atlas Clouds"). This preassignment of jobs to sites leads to two suboptimal
behaviours:

  1. While a job is waiting in a queue at a site, the data and resource
    availability can change and therefore the job's placement at that site
    becomes less and less ideal.

  2. The placement of an entire task (all jobs in the task) at a single
    or just a few sites can lead to other sites sitting idle or being
    utilized by lower priority jobs.

With the recent startup of the LHC, the urgency of achieving
scientific results in a timely manner is on the critical path.
Enabling quasi-interactive analysis on the grid is therefore
essential.

Impact

We analyze the impact of job scheduling strategies on handling of job output and
subsequent implications for overall data-management strategy for ATLAS collaboration.

To achieve a quasi-interactive distributed analysis system, we
consider a few metrics:

  1. Time-to-completion. This is a measure of both overall performance
    for all users at all sites, and for individual users. The variance of
    this measure is used to evaluate the stability of the schedules.
    Timely delivery of partial results is also desirable to permit users
    to take corrective actions as soon as possible.

  2. The correlation of job priority with time-to-completion. This is a measure of how well the priority values
    are respected by the workload management system.

  3. Fairness to sites is an important property of the scheduling system as sites are rewarded for running jobs
    successfully. Therefore, sites should receive a share of the global jobs proportional to their quality, where quality relates to their
    efficiency and the popularity of their resident data.

Conclusions and Future Work

Making effective use of resources for distributed user analysis is an important challenge with the potential to improve the user experience
substantially. In the HEP community alone, physicists numbering in the
thousands use the grid facilities to run jobs numbering in the hundreds
of thousands daily.

In the current economic model of the Grid based on public funding and SLAs,
resource utilization of a site is a primary metric of its success. Therefore it is a responsibility of the user communities to apply job distribution strategies which reward sites according to their quality of service

URL for further information http://cern.ch/diane
Keywords data management, scheduling

Primary authors

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