Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation

Author:

de la Torre Luis,Halappanavar Mahantesh

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Asner, D.M., Dart, E., Hara, T.: Belle II: experiment network and computing. Technical report arXiv:1308.0672. PNNL-SA-97204, August 2013. Contributed to CSS2013 (Snowmass)

2. Friese, R.D., Halappanavar, M., Sathanur, A.V., Schram, M., Kerbyson, D.J., de la Torre, L.: Towards efficient resource allocation for distributed workflows under demand uncertainties. In: Proceedings of the 21st Workshop on Job Scheduling Strategies for Parallel Processing (2015)

3. Gao, P.X., Curtis, A.R., Wong, B., Keshav, S.: It’s not easy being green. ACM SIGCOMM Comput. Commun. Rev. 42(4), 211–222 (2012)

4. Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5(2), 287–326 (1979)

5. Halappanavar, M., Schram, M., de la Torre, L., Barker, K., Tallent, N.R., Kerbyson, D.J.: Towards efficient scheduling of data intensive high energy physics workflows. In: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science. WORKS ’15, pp. 3:1–3:9. ACM, New York, NY, USA (2015)

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