JobScheduling

The Job Scheduling Executor takes the information gained from all previous optimizers and makes a scheduling decision for the jobs.

Subsequent to this jobs are added into a Task Queue and pilot agents can be submitted.

All issues preventing the successful resolution of a site candidate are discovered here where all information is available.

This Executor will fail affected jobs meaningfully.

class DIRAC.WorkloadManagementSystem.Executor.JobScheduling.JobScheduling

Bases: DIRAC.WorkloadManagementSystem.Executor.Base.OptimizerExecutor.OptimizerExecutor

The specific Optimizer must provide the following methods: - optimizeJob() - the main method called for each job and it can provide: - initializeOptimizer() before each execution cycle

class JobLog(log, jid)

Bases: object

class LogWrap(log, jid, funcName)

Bases: object

__init__(log, jid, funcName)

__init__(log, jid)

deserializeTask(taskStub)

disableFastTrackForTask()

classmethod ex_enabled()

classmethod ex_getExtraArguments()

classmethod ex_getMind()

classmethod ex_getOption(optName, defaultValue=None)

classmethod ex_getProperty(optName)

classmethod ex_optimizerName()

classmethod ex_setMind(mindName, **extraArgs)

classmethod ex_setOption(optName, value)

classmethod ex_setProperty(optName, value)

fastTrackDispatch(jid, jobState)

freezeTask(freezeTime)

classmethod initialize()

classmethod initializeOptimizer(): Initialization of the optimizer.

isTaskFrozen()

property jobLog

optimizeJob(jid, jobState)

Banned sites are removed from the destination list.
Get input files
Production jobs are sent directly to TQ
Check if staging is necessary

processTask(jid, jobState)

retrieveOptimizerParam(name)

serializeTask(cjs)

setNextOptimizer(jobState=None)

storeOptimizerParam(name, value)