survey

Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey

Authors:

Rodrigo N. Calheiros,

Rajkumar BuyyaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 4

Article No.: 73, Pages 1 - 33

https://doi.org/10.1145/3148149

Published: 13 July 2018 Publication History

Abstract

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.

References

[1]

F. Al-Haidari, M. Sqalli, and K. Salah. 2013. Impact of CPU utilization thresholds and scaling size on autoscaling cloud resources. In Proceedings of 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, Vol. 2. 256--261.

Digital Library

[2]

A. Ali-Eldin, M. Kihl, J. Tordsson, and E. Elmroth. 2012. Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control. In Proceedings of the 3rd Workshop on Scientific Cloud Computing Date (ScienceCloud’12). ACM, New York, 31--40.

Digital Library

[3]

A. Ali-Eldin, J. Tordsson, and E. Elmroth. 2012. An adaptive hybrid elasticity controller for cloud infrastructures. In Proceedings of 2012 IEEE Network Operations and Management Symposium (NOMS’12). IEEE, 204--212.

[4]

A. Ali-Eldin, J. Tordsson, E. Elmroth, and M. Kihl. 2013. Workload Classification for Efficient Auto-Scaling of Cloud Resources. Technical Report. Retrieved from http://www.cs.umu.se/research/uminf/reports/2013/013/part1.pdf.

[5]

F. J. Almeida Morais, F. Vilar Brasileiro, R. Vigolvino Lopes, R. Araujo Santos, W. Satterfield, and L. Rosa. 2013. Autoflex: Service agnostic auto-scaling framework for IaaS deployment models. In Proceedings of 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’13). 42--49.

[6]

Amazon. 2016. Amazon Auto Scaling Service. Retrieved from http://aws.amazon.com/autoscaling/.

[7]

L. Aniello, S. Bonomi, F. Lombardi, A. Zelli, and R. Baldoni. 2014. An Architecture for Automatic Scaling of Replicated Services. Springer International Publishing, 122--137.

[8]

E. Barrett, E. Howley, and J. Duggan. 2013. Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurrency and Computation: Practice and Experience 25, 12 (2013), 1656--1674.

[9]

J. Bi, Z. Zhu, R. Tian, and Q. Wang. 2010. Dynamic provisioning modeling for virtualized multitier applications in cloud data center. In Proceedings of 2010 IEEE 3rd International Conference on Cloud Computing. 370--377.

Digital Library

[10]

P. Bodik, R. Griffith, C. Sutton, A. Fox, M. Jordan, and D. Patterson. 2009. Statistical machine learning makes automatic control practical for internet datacenters. In Proceedings of the 2009 Conference on Hot Topics in Cloud Computing. 12--16.

Digital Library

[11]

X. Bu, J. Rao, and C. Z. Xu. 2013. Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. IEEE Transactions on Parallel and Distributed Systems 24, 4 (April 2013), 681--690.

Digital Library

[12]

N. M. Calcavecchia, B. A. Caprarescu, E. Di Nitto, D. J. Dubois, and D. Petcu. 2012. DEPAS: A decentralized probabilistic algorithm for auto-scaling. Computing 94, 8 (2012), 701--730.

[13]

E. Caron, F. Desprez, and A. Muresan. 2011. Pattern matching based forecast of non-periodic repetitive behavior for cloud clients. Journal of Grid Computing 9, 1 (2011), 49--64.

Digital Library

[14]

G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. 2008. Energy-aware server provisioning and load dispatching for connection-intensive internet services. In Proceedings of 5th USENIX Symposium on Networked Systems Design and Implementation, Vol. 8. 337--350.

Digital Library

[15]

T. Chen and R. Bahsoon. 2015. Self-adaptive trade-off decision making for autoscaling cloud-based services. IEEE Transactions on Services Computing 10, 4 (2015), 618--632.

[16]

T. Chen and R. Bahsoon. 2016. Self-adaptive and online QoS modeling for cloud-based software services. IEEE Transactions on Software Engineering 43, 5 (2016), 453--475.

Digital Library

[17]

T. C. Chieu, A. Mohindra, and A. A. Karve. 2011. Scalability and performance of web applications in a compute cloud. In Proceedings of 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE’11). 317--323.

Digital Library

[18]

R. L. F. Cunha, M. D. Assuncao, C. Cardonha, and M. A. S. Netto. 2014. Exploiting user patience for scaling resource capacity in cloud services. In Proceedings of 2014 IEEE 7th International Conference on Cloud Computing. 448--455.

Digital Library

[19]

A. da Silva Dias, L. H. V. Nakamura, J. C. Estrella, R. H. C. Santana, and M. J. Santana. 2014. Providing IaaS resources automatically through prediction and monitoring approaches. In Proceedings of 2014 IEEE Symposium on Computers and Communication (ISCC’14). 1--7.

[20]

W. Dawoud, I. Takouna, and C. Meinel. 2012. Elastic Virtual Machine for Fine-Grained Cloud Resource Provisioning. Communications in Computer and Information Science, Vol. 269. Springer, Berlin, 11--25.

[21]

R. P. Doyle, J. S. Chase, O. M. Asad, W. Jin, and A. Vahdat. 2003. Model-based resource provisioning in a web service utility. In Proceedings of the 2003 USENIX Symposium on Internet Technologies and Systems, Vol. 4. 5--5.

Digital Library

[22]

X. Dutreilh, S. Kirgizov, O. Melekhova, J. Malenfant, N. Rivierre, and I. Truck. 2011. Using reinforcement learning for autonomic resource allocation in clouds: Towards a fully automated workflow. In Proceedings of 2011 International Conference on Autonomic and Autonomous Systems. 67--74.

[23]

X. Dutreilh, A. Moreau, J. Malenfant, N. Rivierre, and I. Truck. 2010. From data center resource allocation to control theory and back. In Proceedings of 2010 IEEE 3rd International Conference on Cloud Computing. 410--417.

Digital Library

[24]

S. Dutta, S. Gera, V. Akshat, and B. Viswanathan. 2012. SmartScale: Automatic application scaling in enterprise clouds. In Proceedings of 2012 IEEE 5th International Conference on Cloud Computing (CLOUD’12). 221--228.

Digital Library

[25]

M. Fallah, M. G. Arani, and M. Maeen. 2015. NASLA: Novel auto scaling approach based on learning automata for web application in cloud computing environment. International Journal of Computer Applications 113, 2 (2015).

[26]

W. Fang, Z. Lu, J. Wu, and Z. Cao. 2012. RPPS: A novel resource prediction and provisioning scheme in cloud data center. In Proceedings of 2012 IEEE Ninth International Conference on Services Computing (SCC’12). 609--616.

Digital Library

[27]

H. Fernandez, G. Pierre, and T. Kielmann. 2014. Autoscaling web applications in heterogeneous cloud infrastructures. In Proceedings of 2014 IEEE International Conference on Cloud Engineering (IC2E’14). 195--204.

Digital Library

[28]

S. Frey, C. Luthje, C. Reich, and N. Clarke. 2014. Cloud QoS scaling by fuzzy logic. In Proceedings of 2014 IEEE International Conference on Cloud Engineering (IC2E’14). 343--348.

Digital Library

[29]

A. Gambi, M. Pezze, and G. Toffetti. 2016. Kriging-based self-adaptive cloud controllers. IEEE Transactions on Services Computing 9, 3 (2016), 368--381.

[30]

A. Gambi, G. Toffetti, C. Pautasso, and M. Pezzé. 2013. Kriging controllers for cloud applications. IEEE Internet Computing 17, 4 (July 2013), 40--47.

Digital Library

[31]

A. Gandhi, P. Dube, A. Karve, A. Kochut, and L. Zhang. 2014. Adaptive, model-driven autoscaling for cloud applications. In Proceedings of the 11th International Conference on Autonomic Computing.

[32]

A. Gandhi, P. Dube, A. Karve, A. Kochut, and L. Zhang. 2014. Modeling the impact of workload on cloud resource scaling. In 2014 IEEE 26th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD’14). 310--317.

Digital Library

[33]

A. Gandhi, M. Harchol-Balter, R. Raghunathan, and M. A. Kozuch. 2012. AutoScale: Dynamic, robust capacity management for multitier data centers. ACM Transactions on Computer Systems 30, 4 (Nov. 2012), 14:1--14:26.

Digital Library

[34]

I. Gergin, B. Simmons, and M. Litoiu. 2014. A decentralized autonomic architecture for performance control in the cloud. In Proceedings of 2014 IEEE International Conference on Cloud Engineering (IC2E’14). 574--579.

Digital Library

[35]

H. Ghanbari, M. Litoiu, P. Pawluk, and C. Barna. 2014. Replica placement in cloud through simple stochastic model predictive control. In 2014 IEEE 7th International Conference on Cloud Computing. 80--87.

Digital Library

[36]

H. Ghanbari, B. Simmons, M. Litoiu, C. Barna, and G. Iszlai. 2012. Optimal autoscaling in a IaaS cloud. In Proceedings of the 9th International Conference on Autonomic Computing (ICAC’12). ACM, New York, 173--178.

Digital Library

[37]

M. Ghobaei-Arani, S. Jabbehdari, and M. A. Pourmina. 2018. An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems 78, Part 1 (2018), 191--210.

[38]

B. V. Gnedenko and I. N. Kovalenko. 1989. Introduction to Queueing Theory. Birkhauser Boston.

Digital Library

[39]

Z. Gong, X. Gu, and J. Wilkes. 2010. PRESS: PRedictive elastic resource scaling for cloud systems. In Proceedings of 2010 International Conference on Network and Service Management. 9--16.

[40]

G. Y. Grabarnik, L. Shwartz, and M. Tortonesi. 2014. Business-driven optimization of component placement for complex services in federated clouds. In Proceedings of 2014 IEEE Network Operations and Management Symposium (NOMS’14). 1--9.

[41]

D. Grimaldi, V. Persico, A. Pescape, A. Salvi, and S. Santini. 2015. A feedback-control approach for resource management in public clouds. In 2015 IEEE Global Communications Conference (GLOBECOM’15). 1--7.

[42]

N. Grozev and R. Buyya. 2014. Inter-cloud architectures and application brokering: Taxonomy and survey. Software: Practice and Experience 44, 3 (2014), 369--390.

Digital Library

[43]

N. Grozev and R. Buyya. 2014. Multi-cloud provisioning and load distribution for three-tier applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 9, 3 (2014), 13.

Digital Library

[44]

N. Grozev and R. Buyya. 2016. Dynamic selection of virtual machines for application servers in cloud environments. CoRR abs/1602.02339 (2016). Retrieved from http://arxiv.org/abs/1602.02339.

[45]

T. Guo, P. Shenoy, and H. H. Hacigumus. 2016. GeoScale: Providing geo-elasticity in distributed clouds. In Proceedings of 2016 IEEE International Conference on Cloud Engineering (IC2E’16). 123--126.

[46]

R. Han, M. M. Ghanem, L. Guo, Y. Guo, and M. Osmond. 2014. Enabling cost-aware and adaptive elasticity of multitier cloud applications. Future Generation Computer Systems 32 (2014), 82--98.

Digital Library

[47]

X. He, P. Shenoy, R. Sitaraman, and D. Irwin. 2015. Cutting the cost of hosting online services using cloud spot markets. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing. ACM, 207--218.

Digital Library

[48]

N. R. Herbst, N. Huber, S. Kounev, and E. Amrehn. 2014. Self-adaptive workload classification and forecasting for proactive resource provisioning. Concurrency and Computation: Practice and Experience 26, 12 (2014), 2053--2078.

Digital Library

[49]

N. Huber, F. Brosig, and S. Kounev. 2011. Model-based self-adaptive resource allocation in virtualized environments. In Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’11). ACM, New York, 90--99.

Digital Library

[50]

W. Iqbal, M. Dailey, and D. Carrera. 2009. SLA-Driven Adaptive Resource Management for Web Applications on a Heterogeneous Compute Cloud. Springer, 243--253.

Digital Library

[51]

W. Iqbal, M. N. Dailey, and D. Carrera. 2016. Unsupervised learning of dynamic resource provisioning policies for cloud-hosted multitier web applications. IEEE Systems Journal 10, 4 (2016), 1453--1446.

[52]

W. Iqbal, M. N. Dailey, D. Carrera, and P. Janecek. 2011. Adaptive resource provisioning for read intensive multitier applications in the cloud. Future Generation Computer Systems 27, 6 (2011), 871--879.

Digital Library

[53]

S. Islam, J. Keung, K. Lee, and A. Liu. 2010. An empirical study into adaptive resource provisioning in the cloud. In Proceedings of IEEE International Conference on Utility and Cloud Computing (UCC’10). 8.

[54]

P. Jamshidi, C. Pahl, and N. C. Mendonça. 2016. Managing uncertainty in autonomic cloud elasticity controllers. IEEE Cloud Computing 3, 3 (May 2016), 50--60.

[55]

D. Jiang, G. Pierre, and C.-H. Chi. 2010. Autonomous resource provisioning for multi-service web applications. In Proceedings of the 19th International Conference on World Wide Web. ACM, 471--480.

Digital Library

[56]

D. Jiang, G. Pierre, and C.-H. Chi. 2011. Resource provisioning of web applications in heterogeneous clouds. In Proceedings of the 2nd USENIX Conference on Web Application Development. USENIX Association, 49--60.

Digital Library

[57]

Jing Jiang, Jie Lu, Guangquan Zhang, and Guodong Long. 2013. Optimal cloud resource auto-scaling for web applications. In Proceedings of 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’13). IEEE, 58--65.

Digital Library

[58]

X. Jing, Z. Ming, J. Fortes, R. Carpenter, and M. Yousif. 2007. On the use of fuzzy modeling in virtualized data center management. In Proceedings of 4th International Conference on Autonomic Computing (ICAC’07). 25--25.

Digital Library

[59]

G. Jung, K. R. Joshi, M. A. Hiltunen, R. D. Schlichting, and C. Pu. 2008. Generating adaptation policies for multitier applications in consolidated server environments. In Proceedings of 2008 International Conference on Autonomic Computing (ICAC’08). 23--32.

Digital Library

[60]

E. Kalyvianaki, T. Charalambous, and S. Hand. 2009. Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters. In Proceedings of the 6th International Conference on Autonomic Computing (ICAC’09). ACM, New York, 117--126.

Digital Library

[61]

A. Kamra, V. Misra, and E. M. Nahum. 2004. Yaksha: A self-tuning controller for managing the performance of 3-tiered Web sites. In 12th IEEE International Workshop on Quality of Service, 2004 (IWQOS’04). 47--56.

[62]

P. D. Kaur and I. Chana. 2014. A resource elasticity framework for QoS-aware execution of cloud applications. Future Generation Computer Systems 37 (2014), 14--25.

[63]

J. O. Kephart and D. M. Chess. 2003. The vision of autonomic computing. Computer 36, 1 (Jan. 2003), 41--50.

Digital Library

[64]

P. Lama and X. Zhou. 2009. Efficient server provisioning with end-to-end delay guarantee on multitier clusters. In Proceedings of 17th International Workshop on Quality of Service (IWQoS’09). 1--9.

[65]

P. Lama and X. Zhou. 2010. Autonomic provisioning with self-adaptive neural fuzzy control for end-to-end delay guarantee. In 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. 151--160.

Digital Library

[66]

H. Li and S. Venugopal. 2011. Using reinforcement learning for controlling an elastic web application hosting platform. In Proceedings of the 8th ACM International Conference on Autonomic Computing (ICAC’11). ACM, New York, 205--208.

Digital Library

[67]

H. C. Lim, S. Babu, and J. S. Chase. 2010. Automated control for elastic storage. In Proceedings of the 7th International Conference on Autonomic Computing (ICAC’10). ACM, New York, 1--10.

Digital Library

[68]

H. C. Lim, S. Babu, J. S. Chase, and S. S. Parekh. 2009. Automated control in cloud computing: Challenges and opportunities. In Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds (ACDC’09). ACM, New York, 13--18.

Digital Library

[69]

J. Loff and J. Garcia. 2014. Vadara: Predictive elasticity for cloud applications. In Proceedings of 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom’14). 541--546.

Digital Library

[70]

T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano. 2014. A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing 12, 4 (2014), 559--592.

Digital Library

[71]

S. J. Malkowski, M. Hedwig, J. Li, C. Pu, and D. Neumann. 2011. Automated control for elastic N-tier workloads based on empirical modeling. In Proceedings of the 8th ACM International Conference on Autonomic Computing (ICAC’11). ACM, New York, NY, USA, 131--140.

Digital Library

[72]

M. A. S. Netto, C. Cardonha, R. L. F. Cunha, and M. D. Assuncao. 2014. Evaluating auto-scaling strategies for cloud computing environments. In Proceedings of 2014 IEEE 22nd International Symposium on Modelling, Analysis Simulation of Computer and Telecommunication Systems. 187--196.

Digital Library

[73]

H. Nguyen, Z. Shen, X. Gu, S. Subbiah, and J. Wilkes. 2013. Agile: Elastic distributed resource scaling for infrastructure-as-a-service. In Proceedings of the USENIX International Conference on Automated Computing (ICAC’13).

[74]

A. Y. Nikravesh, S. A. Ajila, and C. H. Lung. 2015. Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In Proceedings of the 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. 35--45.

Digital Library

[75]

A. Nisar, W. Iqbal, F. S. Bokhari, and F. Bukhari. {n.d.}. Hybrid auto-scaling of multi-tier web applications: A case of using Amazon public cloud.

[76]

P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant. 2009. Automated control of multiple virtualized resources. In Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys’09). ACM, New York, 13--26.

Digital Library

[77]

A. V. Papadopoulos, Ahmed Ali-Eldin, Karl-Erik Årzén, Johan Tordsson, and Erik Elmroth. 2016. PEAS: A performance evaluation framework for auto-scaling strategies in cloud applications. ACM Transactions on Modeling and Performance Evaluation of Computing Systems 1, 4, Article 15 (Aug. 2016), 31 pages.

Digital Library

[78]

T. Patikirikorala, A. Colman, J. Han, and L. Wang. 2011. A multi-model framework to implement self-managing control systems for QoS management. In Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. ACM, 218--227.

Digital Library

[79]

R. Prodan and V. Nae. 2009. Prediction-based real-time resource provisioning for massively multiplayer online games. Future Generation Computer Systems 25, 7 (2009), 785--793.

Digital Library

[80]

C. Qu, R. N. Calheiros, and R. Buyya. 2016. A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances. Journal of Network and Computer Applications 65 (2016), 167--180.

Digital Library

[81]

M. Richards. 2015. Microservices vs. service-oriented architecture. O'Reilly Media.

[82]

RightScale. 2016. Understanding the voting process. Retrieved from https://support.rightscale.com/12-Guides/RightScale_101/System_Architecture/RightScale_Alert_System/Alerts_based_on_Voting_Tags/Understanding_the_Voting_Process/.

[83]

Y. Rochman, H. Levy, and E. Brosh. 2014. Efficient resource placement in cloud computing and network applications. SIGMETRICS Performance Evaluation Review 42, 2 (Sept. 2014), 49--51.

Digital Library

[84]

G. Rodolakis, S. Siachalou, and L. Georgiadis. 2006. Replicated server placement with QoS constraints. IEEE Transactions on Parallel and Distributed Systems 17, 10 (Oct. 2006), 1151--1162.

Digital Library

[85]

N. Roy, A. Dubey, and A. Gokhale. 2011. Efficient autoscaling in the cloud using predictive models for workload forecasting. In Proceedings of 2011 IEEE International Conference on Cloud Computing (CLOUD’11). IEEE, 500--507.

Digital Library

[86]

H. Rui, G. Li, M. M. Ghanem, and G. Yike. 2012. Lightweight resource scaling for cloud applications. In Proceedings of 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’12). 644--651.

Digital Library

[87]

K. Salah, K. Elbadawi, and R. Boutaba. 2016. An analytical model for estimating cloud resources of elastic services. Journal of Network and Systems Management 24, 2 (April 2016), 285--308.

Digital Library

[88]

M. Sedaghat, F. Hernandez-Rodriguez, and E. Elmroth. 2013. A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling. In Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference (CAC’13). ACM, New York, Article 6, 10 pages.

Digital Library

[89]

P. Sharma, S. Lee, T. Guo, D. Irwin, and P. Shenoy. 2015. SpotCheck: Designing a derivative IaaS cloud on the spot market. In Proceedings of the 10th European Conference on Computer Systems (EuroSys’15). ACM, New York, Article 16, 15 pages.

Digital Library

[90]

U. Sharma, P. Shenoy, and D. F. Towsley. 2012. Provisioning multitier cloud applications using statistical bounds on sojourn time. In Proceedings of the 9th International Conference on Autonomic Computing (ICAC’12). ACM, New York, 43--52.

Digital Library

[91]

Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. 2011. Cloudscale: Elastic resource scaling for multi-tenant cloud systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 5.

Digital Library

[92]

R. Singh, U. Sharma, E. Cecchet, and P. Shenoy. 2010. Autonomic mix-aware provisioning for non-stationary data center workloads. In Proceedings of the 7th International Conference on Autonomic Computing (ICAC’10). ACM, New York, 21--30.

Digital Library

[93]

S. Spinner, S. Kounev, X. Zhu, L. Lu, M. Uysal, A. Holler, and R. Griffith. 2014. Runtime vertical scaling of virtualized applications via online model estimation. In 2014 IEEE 8th International Conference on Self-Adaptive and Self-Organizing Systems. 157--166.

Digital Library

[94]

S. N. Srirama and A. Ostovar. 2014. Optimal resource provisioning for scaling enterprise applications on the cloud. In 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom’14). 262--271.

Digital Library

[95]

G. Tesauro. 2005. Online resource allocation using decompositional reinforcement learning. In Proceedings of AAAI Conference on Artificial Intelligence. Vol. 5. 886--891.

Digital Library

[96]

G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani. 2007. On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing 10, 3 (2007), 287--299.

Digital Library

[97]

M. Tortonesi and L. Foschini. 2016. Business-driven service placement for highly dynamic and distributed cloud systems. IEEE Transactions on Cloud Computing PP, 99 (2016), 1--1.

[98]

S. Upendra, P. Shenoy, S. Sahu, and A. Shaikh. 2011. A cost-aware elasticity provisioning system for the cloud. In Proceedings of 2011 31st International Conference on Distributed Computing Systems (ICDCS’11). 559--570.

Digital Library

[99]

B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal, and T. Wood. 2008. Agile dynamic provisioning of multitier internet applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 3, 1 (2008), 1.

Digital Library

[100]

N. Vasić, D. Novaković, S. Miučin, D. Kostić, and R. Bianchini. 2012. DejaVu: Accelerating resource allocation in virtualized environments. In Proceedings of the 17th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XVII’12). ACM, New York, 423--436.

Digital Library

[101]

D. Villela, P. Pradhan, and D. Rubenstein. 2007. Provisioning servers in the application tier for e-commerce systems. ACM Transactions on Internet Technology 7, 1 (Feb. 2007), Article No. 7.

Digital Library

[102]

C. Wang, A. Gupta, and B. Urgaonkar. 2016. Fine-grained resource scaling in a public cloud: A tenant’s perspective. In Proceeding of 2016 IEEE 9th International Conference on Cloud Computing (CLOUD’16). 124--131.

[103]

Y. Wu, C. Wu, B. Li, L. Zhang, Z. Li, and F. C. M. Lau. 2012. Scaling social media applications into geo-distributed clouds. In Proceedings of 2012 IEEE INFOCOM. 684--692.

[104]

J. Yang, C. Liu, Y. Shang, B. Cheng, Z. Mao, C. Liu, L. Niu, and J. Chen. 2014. A cost-aware auto-scaling approach using the workload prediction in service clouds. Information Systems Frontiers 16, 1 (2014), 7--18.

Digital Library

[105]

R. Yanggratoke, J. Ahmed, J. Ardelius, C. Flinta, A. Johnsson, D. Gillblad, and R. Stadler. 2015. Predicting service metrics for cluster-based services using real-time analytics. In Proceedings of 2015 11th International Conference on Network and Service Management (CNSM’15). 135--143.

Digital Library

[106]

L. Yazdanov and C. Fetzer. 2012. Vertical scaling for prioritized vms provisioning. In Proceedings of 2012 2nd International Conference on Cloud and Green Computing (CGC’12). IEEE, 118--125.

Digital Library

[107]

L. Yazdanov and C. Fetzer. 2013. VScaler: Autonomic virtual machine scaling. In Proceedings of 2013 IEEE 6th International Conference on Cloud Computing (CLOUD’13). 212--219.

Digital Library

[108]

Y. You, G. Huang, J. Cao, E. Chen, J. He, Y. Zhang, and L. Hu. 2013. GEAM: A general and event-related aspects model for Twitter event detection. In Proceedings of the 14th International Conference on Web Information Systems Engineering, Part II (WISE’13). Springer, Berlin, 319--332.

[109]

Q. Zhang, L. Cherkasova, and E. Smirni. 2007. A regression-based analytic model for dynamic resource provisioning of multitier applications. In Proceedings of the 4th International Conference on Autonomic Computing (ICAC’07). 27--27.

Digital Library

[110]

Q. Zhang, Q. Zhu, M. F. Zhani, R. Boutaba, and J. L. Hellerstein. 2013. Dynamic service placement in geographically distributed clouds. IEEE Journal on Selected Areas in Communications 31, 12 (Dec. 2013), 762--772.

[111]

Q. Zhu and G. Agrawal. 2012. Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Transactions on Services Computing 5, 4 (2012), 497--511.

Digital Library

Cited By

Xu DLiu FWang BTang XZeng DGao HChen RWu Q(2025)GenesisRM: A state-driven approach to resource management for distributed JVM web applicationsFuture Generation Computer Systems10.1016/j.future.2024.107539163(107539)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107539
Alharthi SAlshamsi AAlseiari AAlwarafy A(2024)Auto-Scaling Techniques in Cloud Computing: Issues and Research DirectionsSensors10.3390/s2417555124:17(5551)Online publication date: 28-Aug-2024
https://doi.org/10.3390/s24175551
Swethasri Kavuri (2024)Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud ServicesInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105103810:5(480-502)Online publication date: 6-Oct-2024
https://doi.org/10.32628/CSEIT241051038
Show More Cited By

Index Terms

Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey

Recommendations

Auto-scaling web applications in clouds

The elasticity feature of cloud computing and its pay-per-use pricing entice application providers to use cloud application hosting. One of the most valuable methods, an application provider can use in order to reduce costs is resource auto-scaling. ...
Monitoring-based auto-scalability across hybrid clouds
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Cloud computing is a relatively new type of Internet-based computing that becomes more and more popular. Using methods like virtualization, adopting architectures based on microservices, automation of building and deployment processes, Cloud could ...
A survey on auto-scaling: how to exploit cloud elasticity

Elasticity plays an essential role as far as the wide diffusion of cloud computing is concerned. It enables a cloud application deployment to 'scale' automatically, adapting to workload changes, guaranteeing the performance requirements with minimum ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 4

July 2019

765 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3236632

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2018

Accepted: 01 September 2017

Revised: 01 September 2017

Received: 01 September 2016

Published in CSUR Volume 51, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Research
Refereed

Funding Sources

Future Fellowship grant from the Australian Research Council (ARC)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

178
Total Citations
View Citations
2,893
Total Downloads

Downloads (Last 12 months)287
Downloads (Last 6 weeks)28

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xu DLiu FWang BTang XZeng DGao HChen RWu Q(2025)GenesisRM: A state-driven approach to resource management for distributed JVM web applicationsFuture Generation Computer Systems10.1016/j.future.2024.107539163(107539)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107539
Alharthi SAlshamsi AAlseiari AAlwarafy A(2024)Auto-Scaling Techniques in Cloud Computing: Issues and Research DirectionsSensors10.3390/s2417555124:17(5551)Online publication date: 28-Aug-2024
https://doi.org/10.3390/s24175551
Swethasri Kavuri (2024)Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud ServicesInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105103810:5(480-502)Online publication date: 6-Oct-2024
https://doi.org/10.32628/CSEIT241051038
Souza PFerreto TCalheiros R(2024)Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research ChallengesACM Computing Surveys10.1145/365909756:10(1-38)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659097
Li WZhang JYin YLi YZhu ZZhou WLin LLi FBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDBCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653381(255-268)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653381
Guo YGe JGuo PChai YLi TShi MTu YOuyang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web ApplicationsProceedings of the ACM Web Conference 202410.1145/3589334.3645330(2747-2758)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645330
Lin LPan LLiu S(2024)SpotDAG: An RL-Based Algorithm for DAG Workflow Scheduling in Heterogeneous Cloud EnvironmentsIEEE Transactions on Services Computing10.1109/TSC.2024.342282817:5(2904-2917)Online publication date: Sep-2024
https://doi.org/10.1109/TSC.2024.3422828
Agarwal SRodriguez MBuyya R(2024)A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless FunctionsIEEE Transactions on Services Computing10.1109/TSC.2024.338766117:5(1899-1910)Online publication date: Sep-2024
https://doi.org/10.1109/TSC.2024.3387661
Anselmi J(2024)Asynchronous Load Balancing and Auto-Scaling: Mean-Field Limit and Optimal DesignIEEE/ACM Transactions on Networking10.1109/TNET.2024.336813032:4(2960-2971)Online publication date: Aug-2024
https://doi.org/10.1109/TNET.2024.3368130
Pinheiro TMialaret MPereira PLins LSilva DDantas JMaciel P(2024)Performance Modeling of Microservices with Circuit Breakers using Stochastic Petri Nets2024 IEEE International Systems Conference (SysCon)10.1109/SysCon61195.2024.10553490(1-8)Online publication date: 15-Apr-2024
https://doi.org/10.1109/SysCon61195.2024.10553490
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents