Impressionism in Cloud Computing - A Position Paper on Capacity Planning in Cloud Computing Environments

Ivan Carrera Izurieta, Cláudio Resin Geyer

2013

Abstract

Cloud computing is a model that relies on virtualization and can lower costs to the user by charging only for the computational resources used by the application. There is a way to use the advantages of cloud computing in data-intensive applications like MapReduce and it is by using a virtual machine (VM) cluster in the cloud. An interesting challenge with VM clusters is determining the size of the VMs that will compose the cluster, because with an appropriate cluster and VM size, users will be able to take a full advantage of resources, i.e., reducing costs by using idle resources and gaining performance. This position paper is intended to bring to consideration the necessity for accurate capacity planning at user level, in order to take fully advantage of cloud resources and will focus specially for data-intensive applications users.

References

  1. Abad, C. L., Roberts, N., Lu, Y., and Campbell, R. H. (2012). A storage-centric analysis of mapreduce workloads: File popularity, temporal locality and arrival patterns. In Workload Characterization (IISWC), 2012 IEEE International Symposium on, pages 100- 109. IEEE.
  2. Anjos, J., Kolberg, W., Geyer, C. R., and Arantes, L. B. (2012). Addressing data-intensive computing problems with the use of mapreduce on heterogeneous environments as desktop grid on slow links. In Computer Systems (WSCAD-SSC), 2012 13th Symposium on, pages 148-155. IEEE.
  3. Boutaba, R., Cheng, L., and Zhang, Q. (2012). On cloud computational models and the heterogeneity challenge. Journal of Internet Services and Applications, 3(1):77-86.
  4. Carissimi, A. (2008). Virtualizac¸a˜o: da teoria a soluc¸o˜es. Minicursos do Simpósio Brasileiro de Redes de Computadores-SBRC, 2008:173-207.
  5. Herodotou, H., Dong, F., and Babu, S. (2011). No one (cluster) size fits all: automatic cluster sizing for dataintensive analytics. In Proceedings of the 2nd ACM Symposium on Cloud Computing, page 18. ACM.
  6. Ibrahim, S., Jin, H., Lu, L., Qi, L., Wu, S., and Shi, X. (2009). Evaluating mapreduce on virtual machines: The hadoop case. In Cloud Computing, pages 519- 528. Springer.
  7. Jain, R. (1991). The art of computer systems performance analysis, volume 182. John Wiley & Sons Chichester.
  8. Mell, P. and Grance, T. (2011). The nist definition of cloud computing (draft). NIST special publication, 800:145.
  9. Mietzner, R. and Leymann, F. (2008). Towards provisioning the cloud: On the usage of multi-granularity flows and services to realize a unified provisioning infrastructure for saas applications. In Services-Part I, 2008. IEEE Congress on, pages 3-10. IEEE.
  10. Sangroya, A., Serrano, D., and Bouchenak, S. (2012). Benchmarking dependability of mapreduce systems. In Reliable Distributed Systems (SRDS), 2012 IEEE 31st Symposium on, pages 21-30. IEEE.
  11. Wang, P., Huang, W., and Varela, C. A. (2010). Impact of virtual machine granularity on cloud computing workloads performance. In Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on, pages 393-400. IEEE.
  12. Yelick, K., Coghlan, S., Draney, B., Canon, R. S., et al. (2011). The magellan report on cloud computing for science. US Department of Energy Office of Science, Office of Advanced Scientific Computing Research (ASCR) December.
  13. Zaharia, M., Konwinski, A., Joseph, A. D., Katz, R., and Stoica, I. (2008). Improving mapreduce performance in heterogeneous environments. In Proceedings of the 8th USENIX conference on Operating systems design and implementation, pages 29-42.
Download


Paper Citation


in Harvard Style

Carrera Izurieta I. and Resin Geyer C. (2013). Impressionism in Cloud Computing - A Position Paper on Capacity Planning in Cloud Computing Environments . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8565-60-0, pages 333-338. DOI: 10.5220/0004567803330338


in Bibtex Style

@conference{iceis13,
author={Ivan Carrera Izurieta and Cláudio Resin Geyer},
title={Impressionism in Cloud Computing - A Position Paper on Capacity Planning in Cloud Computing Environments},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2013},
pages={333-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004567803330338},
isbn={978-989-8565-60-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Impressionism in Cloud Computing - A Position Paper on Capacity Planning in Cloud Computing Environments
SN - 978-989-8565-60-0
AU - Carrera Izurieta I.
AU - Resin Geyer C.
PY - 2013
SP - 333
EP - 338
DO - 10.5220/0004567803330338