Network-aware meta-scheduling in advance with autonomous self-tuning system

The provision of Quality of Service (QoS) in Grid environments is still an open issue that needs attention from the research community. One way of contributing to the provision of QoS in Grids is by performing meta-scheduling of jobs in advance, that is, jobs are scheduled some time before they are actually executed. In this way, it becomes more likely that the appropriate resources are available to run the job when needed, so that QoS requirements of jobs are met (i.e. jobs are finished within a deadline).This paper presents a framework built on top of Globus and the GridWay meta-scheduler to provide QoS by means of performing meta-scheduling in advance. Thanks to this, QoS requirements of jobs are met. This framework manages idle/busy periods of resources in order to choose the most suitable resource for each job, and uses red–black trees for this task. Besides, no prior knowledge on the duration of jobs is required, as opposed to other works using similar techniques.This framework uses heuristics that consider the network as a first level resource. Furthermore, this framework presents an autonomous behaviour so that it adapts to the dynamic changes of the Grid resources. The autonomous behaviour is obtained by means of computing a trust for each resource and performing job rescheduling. All this set of features make this framework suitable for real Grids. Finally, a performance evaluation using a real testbed is presented that illustrates the efficiency of this approach to meet the QoS requirements of users.

Research highlights► Scheduling in advance increases the chances that QoS requirements of jobs are met. ► The framework does not require prior knowledge on the duration of jobs. ► Heuristics considering network and CPU status are used for estimating job duration. ► Autonomic behaviour is implemented to adjust to the dynamic behaviour of the Grid. ► The framework is implemented as an extension to the GridWay meta-scheduler.