ABSTRACT
Cloud computing systems have become not only popular, but extensively used. They are supported and exploited by both industry and academia. Cloud providers have diversified and so did the software offered by their systems. Infrastructure as a Service (IaaS) clouds are now available from single virtual machine use cases, such as a personal server, to specialized high performance or machine learning engines. This popularity has been brought by the low-cost and risk-free feature of renting computing resources instead of buying them, in a large, one-time investment. Furthermore, clouds permit their clients the use of elasticity.
Elasticity is the most relevant feature of cloud computing. It refers to the clients' ability to easily change the number of rented resources in a live environment. This permits the entire system to handle differences in load. Most cloud clients serve web applications or services to third parties. In these cases, load differences can be correlated to the number of users for the service and elasticity is used to handle differences in what is called user load. Most of the scientific literature approaches elasticity looking solely at user load. To give a clearer understanding, the majority of cloud frameworks in use today work as follows: they start a number of worker nodes, and tasks are assigned to them for execution. Only when the user load changes, the number of workers is adjusted, if any.
In this paper, we propose an alternative approach, where the number of workers depends on the actual requirements coming from the different execution steps of an application. We show such an idea can be achieved for several workflows from different fields and that it can bring significant benefits to execution time and cost.
- M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica et al., "A view of cloud computing," Communications of the ACM, vol. 53, no. 4, pp. 50--58, 2010. Google ScholarDigital Library
- R. Buyya, C. S. Yeo, and S. Venugopal, "Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities," in High Performance Computing and Communications, 2008. HPCC'08. 10th IEEE International Conference on. Ieee, 2008, pp. 5--13. Google ScholarDigital Library
- P. Mell and T. Grance, "The nist definition of cloud computing," 2011.Google Scholar
- R. Sakellariou and H. Zhao, "A hybrid heuristic for dag scheduling on heterogeneous systems," in Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International. IEEE, 2004, p. 111.Google Scholar
- N. R. Herbst, S. Kounev, and R. H. Reussner, "Elasticity in cloud computing: What it is, and what it is not." in ICAC, 2013, pp. 23--27.Google Scholar
- Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-art and research challenges," Journal of internet services and applications, vol. 1, no. 1, pp. 7--18, 2010.Google ScholarCross Ref
- G. Galante and L. C. E. de Bona, "A survey on cloud computing elasticity," in Utility and Cloud Computing (UCC), 2012 IEEE Fifth International Conference on. IEEE, 2012, pp. 263--270. Google ScholarDigital Library
- Y. Guo, M. Ghanem, and R. Han, "Does the cloud need new algorithms? an introduction to elastic algorithms," in Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on. IEEE, 2012, pp. 66--73. Google ScholarDigital Library
- 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. ACM, 2012, p. 24. Google ScholarDigital Library
- T. Knauth and C. Fetzer, "Scaling non-elastic applications using virtual machines," in Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011, pp. 468--475. Google ScholarDigital Library
- D. Moran, L. M. Vaquero, and F. Galán, "Elastically ruling the cloud: specifying application's behavior in federated clouds," in Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011, pp. 89--96. Google ScholarDigital Library
- U. Sharma, P. Shenoy, S. Sahu, and A. Shaikh, "A cost-aware elasticity provisioning system for the cloud," in Distributed Computing Systems (ICDCS), 2011 31st International Conference on. IEEE, 2011, pp. 559--570. Google ScholarDigital Library
- G. Copil, D. Moldovan, H.-L. Truong, and S. Dustdar, "Sybl: An extensible language for controlling elasticity in cloud applications," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 2013, pp. 112--119.Google ScholarDigital Library
- D. M. Shawky and A. F. Ali, "Defining a measure of cloud computing elasticity," in Systems and Computer Science (ICSCS), 2012 1st International Conference on. IEEE, 2012, pp. 1--5.Google Scholar
- S. Islam, K. Lee, A. Fekete, and A. Liu, "How a consumer can measure elasticity for cloud platforms," in Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering. ACM, 2012, pp. 85--96. Google ScholarDigital Library
- J. Kuhlenkamp, M. Klems, and O. Röss, "Benchmarking scalability and elasticity of distributed database systems," Proceedings of the VLDB Endowment, vol. 7, no. 12, pp. 1219--1230, 2014. Google ScholarDigital Library
- Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, "Cloudscale: elastic resource scaling for multi-tenant cloud systems," in Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 2011, p. 5. Google ScholarDigital Library
- A. Naskos, E. Stachtiari, A. Gounaris, P. Katsaros, D. Tsoumakos, I. Konstantinou, and S. Sioutas, "Cloud elasticity using probabilistic model checking," arXiv preprint arXiv:1405.4699, 2014.Google Scholar
- A. Ali-Eldin, J. Tordsson, and E. Elmroth, "An adaptive hybrid elasticity controller for cloud infrastructures," in Network Operations and Management Symposium (NOMS), 2012 IEEE. IEEE, 2012, pp. 204--212.Google Scholar
- R. Han, M. M. Ghanem, L. Guo, Y. Guo, and M. Osmond, "Enabling cost-aware and adaptive elasticity of multi-tier cloud applications," Future Generation Computer Systems, vol. 32, pp. 82--98, 2014.Google ScholarCross Ref
- E. F. Coutinho, F. R. de Carvalho Sousa, P. A. L. Rego, D. G. Gomes, and J. N. de Souza, "Elasticity in cloud computing: a survey," annals of telecommunications-annales des télécommunications, vol. 70, no. 7-8, pp. 289--309, 2015.Google Scholar
- S. Singh and I. Chana, "A survey on resource scheduling in cloud computing: Issues and challenges," Journal of Grid Computing, vol. 14, no. 2, pp. 217--264, 2016. Google ScholarDigital Library
- H. L. Truong and S. Dustdar, "Programming elasticity in the cloud." IEEE Computer, vol. 48, no. 3, pp. 87--90, 2015.Google ScholarCross Ref
- B. Abrahao, V. Almeida, J. Almeida, A. Zhang, D. Beyer, and F. Safai, "Self-adaptive sla-driven capacity management for internet services," in 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006. IEEE, 2006, pp. 557--568.Google Scholar
- D. L. Eager, J. Zahorjan, and D. Lazowska, "Speedup versus efficiency in parallel systems," Computers, IEEE Transactions on, vol. 38, no. 3, pp. 408--423, 1989. Google ScholarDigital Library
- S. Brin and L. Page, "Reprint of: The anatomy of a large-scale hypertextual web search engine," Computer networks, vol. 56, no. 18, pp. 3825--3833, 2012. Google ScholarDigital Library
- C. J. Watkins and P. Dayan, "Q-learning," Machine learning, vol. 8, no. 3-4, pp. 279--292, 1992. Google ScholarDigital Library
- R. Bellman, "A markovian decision process," DTIC Document, Tech. Rep., 1957.Google Scholar
- K. Krauter, R. Buyya, and M. Maheswaran, "A taxonomy and survey of grid resource management systems for distributed computing," Software: Practice and Experience, vol. 32, no. 2, pp. 135--164, 2002. Google ScholarDigital Library
Recommendations
Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics
QoSA '15: Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software ArchitecturesContext: In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading ...
Physics and microeconomics-based metrics for evaluating cloud computing elasticity
Currently, many customers and broadband providers are using cloud resources, such as processing and storage, for their applications and services. With the increase of computational resources usage, elasticity has become quite attractive and a key ...
A Survey on Cloud Computing Elasticity
UCC '12: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud ComputingElasticity is a key feature in the cloud computing context, and perhaps what distinguishes this computing paradigm of the other ones, such as cluster and grid computing. Considering the importance of elasticity in cloud computing context, the objective ...
Comments