Resource scheduling on grid: handling uncertainty

Real, Rodrigo; Yamin, Adenauer; Silva, L. da; Frainer, Guilherme; Augustin, Iara; Barbosa, Jorge Luís Victória; Geyer, Cláudio F. R.

doi:10.1109/grid.2003.1261717

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Besides, it is under implementation two helpful functionalities: (a) transfer of local files (user can use files stored in local filesystem); (b) on demand file transfer (not all input files need to be specified). We also intend to use the TiPS scheduling information [17], available as an EXEHDA service to improve the AppMan scheduling decisions. Our main longer term goal will be to have all the GRAND functionalities implemented and tested with real applications on a real grid environment.…”

Section: Resultsmentioning

confidence: 99%

Hierarchical submission in a Grid environment

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Dutra

Nascimento

et al. 2005

Proceedings of the 3rd International Workshop on Middleware for Grid Computing - MGC '05

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One of the challenges in grid computing research is to provide means to automatically submit, manage, and monitor applications which spread a large number of tasks. The usual way of managing these tasks is to represent each one as an explicit node in a graph, and this is the approach taken by many grid systems up to date. This approach can quickly saturate the machine where the application is launched, as we increase the number of tasks. In this work we present and validate a novel architectural model, GRAND (Grid Robust ApplicatioN Deployment), whose main objective is to deal with the problem of memory and load saturation of the submission machine. GRAND is implemented at a middleware level, aiming at providing a distributed task submission through a hierarchical organization. This paper provides an overview of the GRAND submission model as well our implementation. Initial results show that our approach can be much more effective than other approaches in the literature.

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Section: Resultsmentioning

confidence: 99%

Hierarchical submission in a Grid environment

Vargas

Dutra

Nascimento

et al. 2005

Proceedings of the 3rd International Workshop on Middleware for Grid Computing - MGC '05

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“…Since the events may happen at arbitrary times, the problem is, given a pos- sible combination of events, which schedule will maximize the probability of achieving the desired objectives. Consider two cases: (1) sen1 and sen2 are congested at time t 1 . Whenever tk4 will transfer data, the storage is overloaded immediately; (2) sen1 is congested at time t 1 and sen2 is congested at time t 2 ; the st1 will act as in case (1).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The evaluation function will map the population candidate into a partially ordered set, which will be input to the next step, i.e., population selection. DBN is used to obtain the probability of achieving the desired effects at certain time slices for a given strategy P {O 1 …”

Section: Scheduling Optimization With Gamentioning

confidence: 99%

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Grid Scheduling Optimization Under Conditions of Uncertainty

Zeng

Zhang

Wei

2007

Lecture Notes in Computer Science

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Abstract.One of the biggest challenges in building grid schedulers is how to deal with the uncertainty in what future computational resources will be available. Current techniques for Grid scheduling rarely account for resources whose performance, reliability, and cost vary with time simultaneously. In this paper we address the problem of delivering a deadline based scheduling in a dynamic and uncertain environment represented by dynamic Bayesian network based stochastic resource model. The genetic algorithm is used to find the optimal and robust solutions so that the highest probability of satisfying the user's QoS objectives at a specified deadline can be achieved. It is shown via a simulation that the new methodology will not only achieving a relatively high probability of scheduling workflow with multiple goals successfully, but also be resilient to environment changes.

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“…In [12], a module that takes into account the available capacity of a grid is introduced. The module was based on the middleware "Support Infrastructure to Mobile Applications" (ISAM).…”

Section: Related Workmentioning

confidence: 99%

Scheduling grid tasks under uncertain demands

Batista

Drummond

Fonseca

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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The uncertainty of the demands of grid applications can cause unpredicted performance and, consequently, can make ineffective schedules derived for target demand values. To produce effective results, schedulers need to take into account the difficulty in estimating the demands of applications. In this paper, a scheduler based on fuzzy optimization is proposed to deal with such uncertainties. It is shown, via numerical results, that the proposed scheduler presents advantages when compared to classical schedulers.

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Resource scheduling on grid: handling uncertainty

Cited by 10 publications

References 7 publications

Hierarchical submission in a Grid environment

Hierarchical submission in a Grid environment

Grid Scheduling Optimization Under Conditions of Uncertainty

Scheduling grid tasks under uncertain demands

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