We present IReS, the Intelligent Resource Scheduler that is able to abstractly describe, optimize and execute any batch analytics workflow with respect to a multi-objective policy. Relying on cost and performance models of the required tasks over the available platforms, IReS allocates distinct workflow parts to the most advantageous execution and/or storage engine among the available ones and decides on the exact amount of resources provisioned. Moreover, IReS efficiently adapts to the current cluster/engine conditions and recovers from failures by effectively monitoring the workflow execution in real-time. Our current prototype has been tested in a plethora of business driven and synthetic workflows, proving its potential of yielding significant gains in cost and performance compared to statically scheduled, single-engine executions. IReS incurs only marginal overhead to the workflow execution performance, managing to discover an approximate pareto-optimal set of execution plans within a few seconds.
CITATION STYLE
Doka, K., Mytilinis, I., Papailiou, N., Giannakouris, V., Tsoumakos, D., & Koziris, N. (2019). Multi-engine Analytics with IReS. In Lecture Notes in Business Information Processing (Vol. 337, pp. 133–154). Springer. https://doi.org/10.1007/978-3-030-24124-7_9
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