|
===Resources provisioning time===
One potential problem is that elasticity takes time. A cloud virtual machine (VM) can be acquired at any time by the user, however, it may take up to several minutes for the acquired VM to be ready to use. The VM startup time is dependent on factors, such as image size, VM type, data center ___location, number of VMs, etc.<ref name="vmstartuptime2012">{{cite journalbook|last=Mao|first=Ming|author2=M. Humphrey|title=A Performance Study on the VM Startup Time in the Cloud|journal=Proceedings of 2012 IEEE 5th International Conference on Cloud Computing (Cloud2012)|year=2012|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6253534&isnumber=6253471|doi=10.1109/CLOUD.2012.103|isbn=978-1-4673-2892-0|page=423}}</ref> Cloud providers have different VM startup performance. This implies any control mechanism designed for elastic applications must consider in its decision process the time needed for the elasticity actions to take effect,<ref>{{cite journalbook|last=Gambi|first=Alessio |author2=Daniel Moldovan |author3=Georgiana Copil |author4=Hong-Linh Truong |author5=Schahram Dustdar|title=On estimating actuation delays in elastic computing systems|journal=Proceedings of ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)|pages=33–42 |year=2013|url=http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6595490|doi=10.1109/SEAMS.2013.6595490|isbn=978-1-4673-4401-2 |citeseerx=10.1.1.353.691 }}</ref> such as provisioning another VM for a specific application component.
===Monitoring elastic applications===
Elastic applications can allocate and deallocate resources (such as VMs) on demand for specific application components. This makes cloud resources volatile, and traditional monitoring tools which associate monitoring data with a particular resource (i.e. VM), such as [[Ganglia (software)|Ganglia]] or [[Nagios]], are no longer suitable for monitoring the ''behavior'' of elastic applications. For example, during its lifetime, a data storage tier of an elastic application might add and remove data storage VMs due to cost and performance requirements, varying the number of used VMs. Thus, additional information is needed in monitoring elastic applications, such as associating the logical application structure over the underlying virtual infrastructure.<ref>{{cite journalbook|last=Moldovan|first=Daniel |author2=Georgiana Copil |author3=Hong-Linh Truong |author4=Schahram Dustdar|title=MELA: Monitoring and Analyzing Elasticity of Cloud Services|journal=Proceedings of IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom 2013)|volume=1 |pages=80–87 |year=2013|url=http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6753781|doi=10.1109/CloudCom.2013.18|isbn=978-0-7695-5095-4 }}</ref> This in turn generates other problems, such as how to aggregate data from multiple VMs towards extracting the behavior of the application component running on top of those VMs, as different metrics might need to be aggregated differently (e.g., cpu usage could be averaged, network transfer might be summed up).
===Elasticity requirements===
===Multiple levels of control===
Cloud applications can be of varying types and complexities, with multiple levels of artifacts deployed in layers. Controlling such structures must take into consideration a variety of issues, an approach in this sense being [http://www.infosys.tuwien.ac.at/research/viecom/SYBL rSYBL].<ref>Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar, [https://link.springer.com/chapter/10.1007%2F978-3-642-45005-1_31# "Specifying, Monitoring, and Controlling Elasticity of Cloud Services"], ''Proceedings of the 11th International Conference on Service Oriented Computing''. Berlin, Germany, 2–5 December 2013. doi=10.1007/978-3-642-45005-1_31
</ref> For multi-level control, control systems need to consider the impact lower level control has upon higher level ones and vice versa (e.g., controlling virtual machines, web containers, or web services in the same time), as well as conflicts which may appear between various control strategies from various levels.<ref>{{cite journalbook | first = Pavlos | last = Kranas | title = ElaaS: An Innovative Elasticity as a Service Framework for Dynamic Management across the Cloud Stack Layers | publisher = IEEE | year = 2012 | journal = Proceedings of Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS) 4–6 July 2012 | urlpages = https://dx.doi.org/10.1109/CISIS.2012.1171042–1049 | doi = 10.1109/CISIS.2012.117| isbn = 978-1-4673-1233-2 }}</ref> Elastic strategies on Clouds can take advantage of control-theoretic methods (e.g., predictive control has been experimented in Cloud scenarios by showing considerable advantages with respect to reactive methods).<ref>{{cite journal|last1=Mencagli|first1=Gabriele|last2=Vanneschi|first2=Marco|title=Towards a systematic approach to the dynamic adaptation of structured parallel computations using model predictive control|journal=Cluster Computing|date=6 February 2014|volume=17|issue=4|pages=1443–1463|doi=10.1007/s10586-014-0346-3}}</ref>
==See also==
|
