Optimal cloud resource provisioning for auto-scaling enterprise applications

被引：0

作者：

Srirama S.N. ^{[1
]}

Ostovar A. ^{[2
]}

机构：

[1] Mobile and Cloud Lab, Institute of Computer Science, University of Tartu, Ulikooli 17-324, Tartu

[2] Science and Engineering Faculty, Information Systems School, Queensland University of Technology, 2 George St, Brisbane, QLD

来源：

Srirama, Satish Narayana (srirama@ut.ee) | 2018年 / Inderscience Publishers卷 / 07期

关键词：

Auto-scaling; Cloud computing; Control flows; Enterprise applications; Optimisation; Resource provisioning;

D O I：

10.1504/IJCC.2018.093769

中图分类号：

学科分类号：

摘要：

Auto-scaling enterprise/workflow systems on cloud needs to deal with both the scaling policy, which determines 'when to scale' and the resource provisioning policy, which determines 'how to scale'. This paper presents a novel resource provisioning policy that can find the most cost optimal setup of variety of instances of cloud that can fulfill incoming workload. All major factors involved in resource amount estimation such as processing power, periodic cost and configuration cost of each instance type, lifetime of each running instance and capacity of clouds are considered in the model. Benchmark experiments were conducted on Amazon cloud and were matched with Amazon AutoScale, using a real load trace and through two main control flow components of enterprise applications, AND and XOR. The experiments showed that the model is plausible for auto-scaling any web/services based enterprise workflow/application on the cloud, along with the effect of individual parameters on the optimal policy. Copyright © 2018 Inderscience Enterprises Ltd.

引用

页码：129 / 162

页数：33

共 42 条

[1]

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

[2]

Amazon AutoScaling (N.d.)

[3]

Amazon EC2 Instances (N.d.)

[4]

Arlitt M.F., Williamson C.L., Web server workload characterization: The search for invariants, ACM SIGMETRICS Performance Evaluation Review, 24, 1, pp. 126-137, (1996)

[5]

Armbrust M., Fox A., Griffith R., Joseph A.D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., A view of cloud computing, Communications of the ACM, 53, 4, pp. 50-58, (2010)

[6]

Buyya R., Yeo C.S., Venugopal S., Broberg J., Brandic I., Cloud computing and emerging it platforms: Vision, hype and reality for delivering computing as the 5th utility, Future Generation Computer Systems, 25, 6, pp. 599-616, (2009)

[7]

Cardellini V., Colajanni M., Philip S.Y., Dynamic load balancing on web-server systems, IEEE Internet Computing, 3, 3, pp. 28-39, (1999)

[8]

ClarkNet-HTTP

[9]

CPLEX Optimizer (N.d.)

[10]

Dutreilh X., Kirgizov S., Melekhova O., Malenfant J., Rivierre N., Truck I., Using reinforcement learning for autonomic resource allocation in clouds: Towards a fully automated workflow, 7th Intl Conf. On Autonomic and Autonomous Systems (ICAS 2011, pp. 67-74, (2011)

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