How to Buy Cloud Resource Better for IaaS User: from the Perspective of Cloud Elasticity Testing

For IaaS users, how to buy cloud resources with the lowest spend is worth careful considering. Most IaaS providers utilize the elasticity feature in cloud computing to better provide resource allocation, however, IaaS users still know little about actual cloud resource allocation, so they have to apply for cloud resource very roughly to run their applications, which obviously costs more. In this paper, we address this issue from the perspective of cloud elasticity testing, that is, a more practical and better cloud resource purchase plan will be obtained in advance through elasticity testing of applications for IaaS users. We optimize the test generation towards existing elasticity testing by introducing genetic algorithm idea. Then evaluation metrics are designed to measure elasticity level of IaaS platform. After iterative and sufficient elasticity test executions, the best elasticity level for cloud applications is calculated from the test results to indicate better resource purchase plan. Test experiment results show that our method could help IaaS users to buy cloud resources more reasonable, which is much closer to the actual requirements of their applications and costs less.

[1]  Deyu Qi,et al.  A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing , 2011 .

[2]  Xiaowei Yang,et al.  CloudCmp: comparing public cloud providers , 2010, IMC '10.

[3]  Karim Baïna,et al.  Elasticity and scalability centric quality model for the cloud , 2014, 2014 Third IEEE International Colloquium in Information Science and Technology (CIST).

[4]  Mika Majakorpi,et al.  Theory and practice of rapid elasticity in cloud applications , 2013 .

[5]  Schahram Dustdar,et al.  Automated testing of cloud-based elastic systems with AUToCLES , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[6]  Kenli Li,et al.  On Elasticity Measurement in Cloud Computing , 2016, Sci. Program..

[7]  Chen Zhigao Research of distribution route optimization based on adaptive ant colony algorithm cloud logistics , 2015 .

[8]  Kevin Lee,et al.  How a consumer can measure elasticity for cloud platforms , 2012, ICPE '12.

[9]  S. Venugopal,et al.  An Optimal Model for Priority based Service Scheduling Policy for Cloud Computing Environment , 2011 .

[10]  Keqin Li Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing , 2018 .

[11]  L. Padma Suresh,et al.  A survey of various scheduling algorithms in cloud environment , 2016, 2016 International Conference on Emerging Technological Trends (ICETT).

[12]  Kenli Li,et al.  A New Cloud Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users , 2017 .

[13]  H. S. Guruprasad,et al.  An Optimal Model for Priority based Service Scheduling Policy for Cloud Computing Environment , 2011 .

[14]  Francesc D. Muñoz-Escoí,et al.  A survey on elasticity management in PaaS systems , 2017, Computing.

[15]  Liam O'Brien,et al.  On a Catalogue of Metrics for Evaluating Commercial Cloud Services , 2012, 2012 ACM/IEEE 13th International Conference on Grid Computing.

[16]  Schahram Dustdar,et al.  Iterative test suites refinement for elastic computing systems , 2013, ESEC/FSE 2013.

[17]  Bo Deng,et al.  Workload prediction for cloud computing elasticity mechanism , 2016, 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[18]  A. K. Singh,et al.  A survey on cloud computing , 2014, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).

[19]  Yong Chen,et al.  Cloud Resource Combinatorial Double Auction Algorithm Based on Genetic Algorithm and Simulated Annealing , 2016, QSHINE.