Abstract
One of the main advantages of cloud computing is elasticity, which allows to rapidly expand or reduce the amount of leased resources in order to adapt to load variations, guaranteeing the desired quality of service. Auto-scaling is an extensively studied topic. Making optimal scaling choices is of paramount importance and can help reduce leasing costs, as well as power consumption. This paper analyzes the current status of auto-scaling in the cloud ecosystem, considering recent literature contributions as well as existing auto-scaling solutions. Then it discusses possible research directions in this field, fostering the development of a methodology that, on the basis of suitably-defined performance parameters, can produce an optimal auto-scaling policy to be implemented using existing auto-scaling services and tools.
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References
Ajila, S.A., Bankole, A.A.: Cloud client prediction models using machine learning techniques. In: 2013 IEEE 37th Annual Computer Software and Applications Conference, pp. 134–142 (2013)
Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82(C), 93–113 (2017)
Assuncao, M., Cardonha, C., Netto, M., Cunha, R.: Impact of user patience onauto-scaling resource capacity for cloud services. Future Gener. Comput. Syst. 55, 41–50 (2015)
Bankole, A.A., Ajila, S.A.: Cloud client prediction models for cloud resource provisioning in a multitier web application environment. In: 2013 IEEE 7th International Symposium on Service-Oriented System Engineering, pp. 156–161 (2013)
Beltran, M.: Automatic provisioning of multi-tier applications in cloud computing environments. J. Supercomput. 71, 2221–2250 (2015). https://doi.org/10.1007/s11227-015-1380-5
Casalicchio, E., Silvestri, L.: Mechanisms for SLA provisioning in cloud-based service providers. Comput. Netw. 57, 795–810 (2013)
Garcia Garcia, A., Blanquer, I., Hernández Garcia, V.: SLA-driven dynamic cloud resource management. Future Gener. Comput. Syst. 31, 1–11 (2014)
Gill, S.S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)
Herbst, N.R., Huber, N., Kounev, S., Amrehn, E.: Self-adaptive workload classification and forecasting for proactive resource provisioning. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, ICPE 2013, pp. 187–198. ACM, New York (2013)
Huang, J., Li, C., Yu, J.: Resource prediction based on double exponential smoothing in cloud computing. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 2056–2060 (2012)
Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener. Comput. Syst. 28(1), 155–162 (2012)
Kaur, P., Chana, I.: A resource elasticity framework for QoS-aware execution ofcloud applications. Future Gener. Comput. Syst. 37, 14–25 (2014)
Maurer, M., Breskovic, I., Emeakaroha, V.C., Brandic, I.: Revealing the MAPE loop for the autonomic management of Cloud infrastructures. In: 2011 IEEE Symposium on Computers and Communications (ISCC), pp. 147–152. IEEE, Corfu (2011)
Mell, P., Grance, T.: The NIST definition of cloud computing. NIST Spec. Publ. 800, 145 (2011)
Moldovan, D., Truong, H.L., Dustdar, S.: Cost-aware scalability of applications in public clouds (2016)
Moltó, G., Caballer, M., de Alfonso, C.: Automatic memory-based vertical elasticity and oversubscription on cloud platforms. Future Gener. Comput. Syst. 56(C), 1–10 (2016)
Ocone, L., Rak, M., Villano, U.: Benchmark-based cost analysis of auto scaling web applications in the cloud. In: 2019 IEEE 28th International WETICE Conference, pp. 98–103 (2019)
Qu, C., Calheiros, R.N., Buyya, R.: Auto-scaling web applications in clouds: a taxonomy and survey. ACM Comput. Surv. 51(4), 1–33 (2018)
Singh, P., Manickam, S., Ul Rehman, S.: A survey of mitigation techniques against economic denial of sustainability (EDoS) attack on cloud computing architecture. In: Proceedings of ICRITO 2014, May 2015
Somasundaram, A.: Economic denial of sustainability attack on cloud - a survey. ICTACT J. Commun. Technol. 07(04), 6 (2016)
Thaper, R., Verma, A.: A survey on economic denial of sustainability attack mitigation techniques. Int. J. Innov. Res. Comput. Commun. Eng. 3(3), 6 (2015)
VivinSandar, S., Shenai, S.: Economic denial of sustainability (EDoS) in cloud services using HTTP and XML based DDoS attacks. Int. J. Comput. Appl. 41(20), 11–16 (2012)
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Catillo, M., Rak, M., Villano, U. (2020). Auto-scaling in the Cloud: Current Status and Perspectives. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_58
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DOI: https://doi.org/10.1007/978-3-030-33509-0_58
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