Abstract
Recent developments in Big Data frameworks are moving towards reservation based approaches as a mean to manage the increasingly complex mix of computations, whereas preemption techniques are employed to meet strict jobs deadlines. Within this work we propose and evaluate a new planning algorithm in the context of reservation based scheduling. Our approach is able to achieve high cluster utilization while minimizing the need for preemption that causes system overheads and planning mispredictions.
- G. Ananthanarayanan, C. Douglas, R. Ramakrishnan, S. Rao, and I. Stoica. True elasticity in multi-tenant data-intensive compute clusters. In Proceedings of the Third ACM Symposium on Cloud Computing, page 24. ACM, 2012. Google Scholar
Digital Library
- C. Curino, D. Difallah, C. Douglas, S. Krishnan, R. Ramakrishnan, and S. Rao. Reservation-based scheduling: if youre late dont blame us! (extended version). Technical report, Microsoft Tech-Report: MSR-TR-2013-108, 2013.Google Scholar
- C. Curino, D. E. Difallah, C. Douglas, S. Krishnan, R. Ramakrishnan, and S. Rao. Reservation-based scheduling: If you're late don't blame us! In Proceedings of the ACM Symposium on Cloud Computing, pages 1--14. ACM, 2014. Google Scholar
Digital Library
- V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, et al. Apache hadoop yarn: Yet another resource negotiator. In Proceedings of the 4th annual Symposium on Cloud Computing, page 5. ACM, 2013. Google Scholar
Digital Library
Index Terms
(auto-classified)Preemption-aware planning on big-data systems
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