Abstract
A recently introduced cloud simulation framework is extended to support self-organizing and self-management local strategies in the cloud resource hierarchy. This dynamic hardware resource allocation system is evolving toward the goals defined by local strategies, which are determined as maximization of: energy efficiency of cloud infrastructures, task throughput, computational efficiency and resource management efficiency. Heterogeneous hardware resources are considered that are except from commodity CPU servers, hardware accelerators such as GPUs, MICs and FPGAs, thus forming a heterogeneous cloud infrastructure. Energy consumption and task execution models for the heterogeneous accelerators are also proposed, in order to demonstrate the energy efficiency of the proposed resource allocation system. Implementation details of the new functionalities on the parallel cloud simulation framework are discussed, while numerical results are given for the scalability and utilization of the cloud elements using the self-organization and self-management framework with two VM placement strategies.
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References
Barroso LA, Clidaras J, Hölzle U (2013) The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth Lect Comput Architect 8(3):1–154
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
Casanova H, Giersch A, Legrand A, Quinson M, Suter F (2014) Versatile, scalable, and accurate simulation of distributed applications and platforms. J Parallel Distrib Comput 74(10):2899–2917. doi:10.1016/j.jpdc.2014.06.008. http://www.sciencedirect.com/science/article/pii/S0743731514001105
Chronopoulos A, Andonie R, Benche M, Grosu D (2001) A class of loop self-scheduling for heterogeneous clusters. In: Proceedings 42nd IEEE Symposium on Foundations of Computer Science, pp 282–291. doi:10.1109/CLUSTR.2001.959989
Dagum L, Menon R (1998) Openmp: an industry-standard api for shared-memory programming. J IEEE Comput Sci Eng 5(1):46–55
Filelis-Papadopoulos C, Gravvanis G, Morrison J (2017) Cloudlightning simulation and evaluation roadmap. In: Proceedings of the 1st International Workshop on Next Generation of Cloud Architectures, cloudNG:17, pp 2:1–2:6. ACM, New York, NY. doi:10.1145/3068126.3068128
Filelis-Papadopoulos C, Grylonakis E, Kyziropoulos P, Gravvanis G, Morrison J (2016) Characterization of hardware in self-managing self-organizing cloud environment. In: Proceedings of the 20th Pan-Hellenic Conference on Informatics, PCI ’16, pp 56:1–56:6. ACM, New York, NY. doi:10.1145/3003733.3003749
Filelis-Papadopoulos C, Xiong H, Spătaru A, Castañé GG, Dong D, Gravvanis GA, Morrison JP (2017) A generic framework supporting self-organisation and self-management in hierarchical systems. In: International Symposium on Parallel and Distributed Computing 2017. ISPDC’17, to appear. IEEE
Filelis-Papadopoulos CK, Gravvanis GA, Kyziropoulos PE (2017) A framework for simulating large scale cloud infrastructures. Future Gener Comput Syst. doi:10.1016/j.future.2017.06.017. http://www.sciencedirect.com/science/article/pii/S0167739X17303230
Giannoutakis KM, Makaratzis AT, Tzovaras D, Filelis-Papadopoulos CK, Gravvanis GA (2017) On the power consumption modeling for the simulation of heterogeneous hpc clouds. In: Proceedings of the 1st International Workshop on Next Generation of Cloud Architectures, CloudNG:17, pp 1:1–1:6. ACM, New York, NY. doi:10.1145/3068126.3068127
Gupta A, Milojicic D (2011) Evaluation of hpc applications on cloud. In: Proceedings of the 2011 Sixth Open Cirrus Summit, OCS ’11, pp 22–26. IEEE Computer Society, Washington, DC
Han Y, Chronopoulos AT (2013) A hierarchical distributed loop self-scheduling scheme for cloud systems. In: 2013 12th IEEE International Symposium on Network Computing and Applications (NCA), pp 7–10. IEEE
Han Y, Chronopoulos AT (2014) A resilient hierarchical distributed loop self-scheduling scheme for cloud systems. In: 2014 IEEE 13th International Symposium on Network Computing and Applications, pp 80–84. doi:10.1109/NCA.2014.18
Hassani R, Aiatullah M, Luksch P. Improving hpc application performance in public cloud. IERI Proced 10
He Q, Zhou S, Kobler B, Duffy D, McGlynn T (2010) Case study for running HPC applications in public clouds. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC ’10, pp 395–401. ACM, New York, NY (2010). doi:10.1145/1851476.1851535
Kliazovich D, Bouvry P, Khan SU (2012) Greencloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput 62(3):1263–1283. doi:10.1007/s11227-010-0504-1
Lawson G, Sosonkina M, Shen Y (2015) Towards modeling energy consumption of xeon phi. CoRR abs/1505.06539 (2015). http://dblp.uni-trier.de/db/journals/corr/corr1505.html#LawsonSS15
Lusk E, Doss N, Skjellum A (1996) A high-performance, portable implementation of the mpi message passing interface standard. Parallel Comput 22:789–828
Lynn T, Xiong H, Dong D, Momani B, Gravvanis G, Filelis-Papadopoulos C, Elster A, Khan MMZM, Tzovaras D, Giannoutakis K, Petcu D, Neagul M, Dragon I, Kuppudayar P, Natarajan S, McGrath M, Gaydadjiev G, Becker T, Gourinovitch A, Kenny D, Morrison J (2016) Cloudlightning: a framework for a self-organising and self-managing heterogeneous cloud. In: Proceedings of the 6th International Conference on Cloud Computing and Services Science, vol 1: CLOSER, pp 333–338. doi:10.5220/0005921503330338
Makaratzis AT, Giannoutakis KM, Tzovaras D (2017) Energy modeling in cloud simulation frameworks. Future Gener Comput Syst. doi:10.1016/j.future.2017.06.016. http://www.sciencedirect.com/science/article/pii/S0167739X17303229
Mehrotra P, Djomehri J, Heistand S, Hood R, Jin H, Lazanoff A, Saini S, Biswas R (2012) Performance evaluation of amazon ec2 for nasa HPC applications. In: Proceedings of the 3rd Workshop on Scientific Cloud Computing, ScienceCloud ’12, pp 41–50. ACM, New York, NY. doi:10.1145/2287036.2287045
Openstack. https://www.openstack.org/ (2017). Accessed 22 May, 2017
Penmatsa S, Chronopoulos AT, Karonis NT, Toonen BR (2007) Implementation of distributed loop scheduling schemes on the teragrid. In: 2007 IEEE International Parallel and Distributed Processing Symposium, pp 1–8. doi:10.1109/IPDPS.2007.370551
Rao J, Wang KAZXAXC (2013) Optimizing virtual machine scheduling in numa multicore systems. In: 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA), pp 306–317
SPEC (2008) Server power and performance characteristics. http://www.spec.org/power_ssj2008/. Accessed 22 May 2017
Xi S, Li C, Lu C, Gill C, Xu M, Phan L, Lee I, Sokolsky O (2015) Rt-open stack: CPU resource management for real-time cloud computing. In: 2015 IEEE 8th International Conference on Cloud Computing, pp 179–186. doi:10.1109/CLOUD.2015.33
Acknowledgements
This work was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through CloudLightning project (http://www.cloudlightning.eu) under Grant Agreement No. 643946. The authors acknowledge the Greek Research and Technology Network (GRNET) for the provision of the National HPC facility ARIS under Project PR002040-ScaleSciComp.
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Filelis-Papadopoulos, C.K., Giannoutakis, K.M., Gravvanis, G.A. et al. Large-scale simulation of a self-organizing self-management cloud computing framework. J Supercomput 74, 530–550 (2018). https://doi.org/10.1007/s11227-017-2143-2
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DOI: https://doi.org/10.1007/s11227-017-2143-2