Elasticity (computing): Difference between revisions

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{{short description|Degree to which a computer system can adapt to workload changes}}

{{redirect|Elastic computing|the physical property|Elasticity (physics)|the economics measurement|Elasticity (economics)}}

In [[computing]], '''elasticity''' is defined as "the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning [[System resource|resources]] in an [[Autonomic computing|autonomic]] manner, such that at each point in time the available resources match the current demand as closely as possible".<ref>{{cite journalbook|last=Herbst|first=Nikolas|author2=Samuel Kounev |author3=Ralf Reussner |titlechapter=Elasticity in Cloud Computing: What It Is, and What It Is Not|journaltitle=Proceedings of the 12th10th International Conference on Autonomic Computing (ICAC 2013), San Jose, CA, June 24–28|year=2013|chapter-url=http://sdqweb.ipd.kit.edu/publications/pdfs/HeKoRe2013-ICAC-Elasticity.pdf|archive-date=2018-01-07|access-date=2013-07-10|archive-url=https://web.archive.org/web/20180107174907/https://sdqweb.ipd.kit.edu/publications/pdfs/HeKoRe2013-ICAC-Elasticity.pdf|url-status=dead}}</ref><ref>Nikolas Herbst, Rouven Krebs, Giorgos Oikonomou, George Kousiouris, Athanasia Evangelinou, Alexandru Iosup, and Samuel Kounev. Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics. Technical Report SPEC-RG-2016-01, SPEC Research Group - Cloud Working Group, Standard Performance Evaluation Corporation (SPEC), 2016. [https://research.spec.org/fileadmin/user_upload/documents/rg_cloud/endorsed_publications/SPEC-RG-2016-01_CloudMetrics.pdf]</ref> Elasticity is a defining characteristic that differentiates [[cloud computing]] from previously proposed [[distributed computing]] paradigms, such as [[grid computing]]. The dynamic adaptation of capacity, e.g., by altering the use of [[system resource|computing resources]], to meet a varying workload is called "elastic computing".<ref>{{citation |title=Cloud Computing Principles and Paradigms |publisher=John Wiley and Sons |year=2011 |isbn=978-0-470-88799-8}}</ref><ref>{{citation |author=Perez |title=Responsive Elastic Computing |date=15 June 2009 |publisher=Association for Computing Machinery |isbn=978-1-60558-578-9|display-authors=etal}}</ref>

In the world of [[distributed system|distributed systems]], there are several definitions according to the authors, some considering the concepts of [[scalability]] a sub-part of elasticity, others as being distinct.

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===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 book|last=Mao|first=Ming|author2=M. Humphrey|title=2012 IEEE Fifth International Conference on Cloud Computing |chapter=A Performance Study on the VM Startup Time in the Cloud |year=2012|doi=10.1109/CLOUD.2012.103|isbn=978-1-4673-2892-0|page=423|s2cid=1285357 }}</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 book|last=Gambi|first=Alessio |author2=Daniel Moldovan |author3=Georgiana Copil |author4=Hong-Linh Truong |author5=Schahram Dustdar|date=2013 |title=2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) |chapter=On estimating actuation delays in elastic computing systems |pages=33–42 |year=2013|doi=10.1109/SEAMS.2013.6595490|isbn=978-1-4673-4401-2 |citeseerx=10.1.1.353.691 |s2cid=13269185 }}</ref> such as provisioning another VM for a specific application component.

===Monitoring elastic applications===

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===Elasticity requirements===

When deploying applications in cloud infrastructures (IaaS/PaaS), requirements of the stakeholder need to be considered in order to ensure proper elasticity behavior. Even though traditionally one would try to find the optimal trade-off between cost and quality or performance, for real world cloud users requirements regarding the behavior are more complex and target multiple dimensions of elasticity (e.g., SYBL<ref>Georgiana{{cite Copil,book Daniel| Moldovan, Hongchapter-Linh Truong, Schahram Dustdar, [httpurl=https://ieeexplore.ieeedoi.org/xpl10.1109/articleDetailsCCGrid.jsp?arnumber2013.42 | doi=654606810.1109/CCGrid.2013.42 "| chapter=SYBL: anAn Extensible Language for Controlling Elasticity in Cloud Applications"], ''Proceedings| of thetitle=2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid)'',| Maydate=2013 14–16,| 2013,last1=Copil Delft,| thefirst1=Georgiana | last2=Moldovan | first2=Daniel | last3=Truong | first3=Hong-Linh | last4=Dustdar | first4=Schahram | pages=112–119 | isbn=978-1-4673-6465-2 Netherlands}}</ref>).

===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{{cite Copil,book Daniel| Moldovan, Hongchapter-Linh Truong, Schahram Dustdar, [url=https://link.springer.com/chapter/10.1007%2F978-3-642-45005-1_31# "Specifying,| Monitoring,doi=10.1007/978-3-642-45005-1_31 and| Controllingchapter=Multi-level Elasticity Control of Cloud Services"] | title=Service-Oriented Computing | series=Lecture Notes in Computer Science | date=2013 | last1=Copil | first1=Georgiana | last2=Moldovan | first2=Daniel | last3=Truong | first3=Hong-Linh | last4=Dustdar | first4=Schahram | volume=6470 | pages=429–436 | isbn=978-3-642-17357-8 }}</ref> For multi-level control, ''Proceedingscontrol ofsystems need to consider the 11thimpact 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 book | first = Pavlos | last = Kranas | title = 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems | chapter = ElaaS: An Innovative Elasticity as a Service OrientedFramework Computing''for Dynamic Management across the Cloud Stack Layers | year = 2012 | pages = 1042–1049 | doi = 10.1109/CISIS.2012.117| Berlin,isbn Germany= 978-1-4673-1233-2 | s2cid = 18233634 }}</ref> Elastic strategies on Clouds can take advantage of control-theoretic methods (e.g., 2–5predictive Decembercontrol 2013has 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=10.1007/978s10586-3014-6420346-45005-1_313|s2cid=254374635 }}</ref>

</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 book | first = Pavlos | last = Kranas | title = 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems | chapter = ElaaS: An Innovative Elasticity as a Service Framework for Dynamic Management across the Cloud Stack Layers | year = 2012 | pages = 1042–1049 | doi = 10.1109/CISIS.2012.117| isbn = 978-1-4673-1233-2 | s2cid = 18233634 }}</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|s2cid=254374635 }}</ref>

==See also==