New grid scheduling and rescheduling methods in the GrADS project

Cooper, Keith D.; Dasgupta, Anshuman; Kennedy, Ken; Koelbel, Charles; Mandal, Anirban; Marin, Gabriel; Mazina, M.; Mellor-Crummey, John; Berman, Fran; Casanova, Henri; Chien, Andrew A.; Dail, Holly; Liu, X.; Olugbile, A.; Sievert, Otto; Xia, Haijun; Johnsson, Lennart; Liu, B.; Patel, Mehul V.; Reed, Dan; Deng, Wei; Mendes, Celso L.; Shi, Zhiao; YarKhan, Asim; Dongarra, Jack

doi:10.1109/ipdps.2004.1303222

Cited by 73 publications

(47 citation statements)

References 21 publications

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“…However, it changes the initial schedule dynamically during the execution. For example, GrADS [31] generates preliminary mapping by using prediction results, but it migrates a task execution to another resource when its initial contract is broken or a better resource is found for execution. Sakellariou et al [105] developed a low-cost rescheduling policy for the mapping of workflows on Grids.…”

Section: Planning Schemementioning

confidence: 99%

“…Most of Grid workflow scheduling systems falls in this category. GrADS [31] optimizes DAG-based workflows using Min-Min, Max-Min and Suffrage heuristics, hoping to obtain minimum completion times. Prodan et.al [101] uses classical genetic algorithms with cycle elimination techniques to minimize non-DAG based workflow execution on Grids.…”

Section: Scheduling Strategymentioning

confidence: 99%

“…In the analytical modeling approach [31][36] [93], a scheduler predicts the performance of tasks in workflow on a given set of resources based on an analytic metric. For example, within GrADS [31], two types of performance models are developed, namely memory hierarchy performance model and computational model. By using these models, one can predict memory requirement and execution time of an application component for a resource according to the associated problem size.…”

Section: Performance Estimationmentioning

confidence: 99%

“…GrADS is a collaboration between several American Universities. GrADS supports application development either by assembling domain-specific components from a high-level toolkit or by creating a module by relatively low-level (e.g., MPI ) code [31].…”

Section: Workflow Management In Gradsmentioning

confidence: 99%

“…New Grid scheduling and rescheduling methods [31] are introduced in GrADS. These scheduling methods are guided by an objective function to minimize the overall job completion time (makespan) of the workflow application.…”

Section: Workflow Management In Gradsmentioning

confidence: 99%

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A Taxonomy of Workflow Management Systems for Grid Computing

2005

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With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.

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Section: Planning Schemementioning

confidence: 99%

Section: Scheduling Strategymentioning

confidence: 99%

Section: Performance Estimationmentioning

confidence: 99%

Section: Workflow Management In Gradsmentioning

confidence: 99%

Section: Workflow Management In Gradsmentioning

confidence: 99%

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A Taxonomy of Workflow Management Systems for Grid Computing

2005

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A performance‐oriented adaptive scheduler for dependent tasks on grids

Bittencourt

E.R.M.

2007

Concurrency and Computation

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SUMMARYA scheduler must consider the heterogeneity and communication delays when scheduling dependent tasks on a grid. The task-scheduling problem is NP-Complete in general, which led us to the development of a heuristic for the associated optimization problem. In this work we present a dynamic adaptive approach to schedule dependent tasks onto a grid based on the Xavantes grid middleware. The developed dynamic approach is applied to the Path Clustering Heuristic, and introduces the concept of rounds, which take turns sending tasks to execution and evaluating the performance of the resources. The adaptive extension changes the size of rounds during the process execution, taking task attributes and resources performance as parameters, and it can be adopted in other task schedulers. The experiments show that the dynamic round-based and adaptive schedule can minimize the effects of performance losses while executing processes on the grid.

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A probabilistic strategy for temporal constraint management in scientific workflow systems

Liu

Chen

et al. 2011

Concurrency and Computation

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SUMMARYIn scientific workflow systems, it is critical to ensure the timely completion of scientific workflows. Therefore, temporal constraints as a type of QoS (Quality of Service) specification are usually required to be managed in scientific workflow systems. Specifically, temporal constraint management includes two basic tasks: setting temporal constraints at workflow build-time and updating temporal constraints at workflow run-time. For constraint setting, the current work mainly adopts user-specified temporal constraints without considering the system performance. Hence, it may result in frequent temporal violations which deteriorate the overall workflow execution effectiveness. As regards constraint updating, although not well investigated, so far is in fact of great importance to workflow management tasks such as workflow scheduling and exception handling. In this paper, with a systematic analysis of the above issues, we propose a probabilistic strategy for temporal constraint management which utilizes a novel probability-based temporal consistency model. Specifically for constraint setting, a negotiation process between the client and the service provider is designed to support the setting of coarse-grained temporal constraints and then automatically derive the fine-grained temporal constraints; for constraint updating, the probability time deficit/redundancy propagation process is proposed to update run-time fine-grained temporal constraints when workflow execution is either ahead of or behind the schedule. The effectiveness of our strategy is demonstrated through a case study on an example scientific workflow process in our scientific workflow system.

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New grid scheduling and rescheduling methods in the GrADS project

Cited by 73 publications

References 21 publications

A Taxonomy of Workflow Management Systems for Grid Computing

A Taxonomy of Workflow Management Systems for Grid Computing

A performance‐oriented adaptive scheduler for dependent tasks on grids

A probabilistic strategy for temporal constraint management in scientific workflow systems

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