research-article

Towards an autonomic auto-scaling prediction system for cloud resource provisioning

Authors:

Ali Yadavar Nikravesh,

Samuel A. Ajila,

Chung-Horng LungAuthors Info & Claims

SEAMS '15: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Pages 35 - 45

Published: 16 May 2015 Publication History

Abstract

This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted to compare the accuracy of time-series prediction algorithms for different performance patterns. In the experiment, workload was considered as the performance metric, and Support Vector Machine (SVM) and Neural Networks (NN) were utilized as time-series prediction techniques. In addition, we used Amazon EC2 as the experimental infrastructure and TPC-W as the benchmark to generate different workload patterns. The results of the experiment show that prediction accuracy of SVM and NN depends on the incoming workload pattern of the system under study. Specifically, the results show that SVM has better prediction accuracy in the environments with periodic and growing workload patterns, while NN outperforms SVM in forecasting unpredicted workload pattern. Based on these experimental results, this paper proposes an architecture for a self-adaptive prediction suite using an autonomic system approach. This suite can choose the most suitable prediction technique based on the performance pattern, which leads to more accurate prediction results.

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Cited By

Baresi LMendonça DGarriga MGuinea SQuattrocchi G(2019)A Unified Model for the Mobile-Edge-Cloud ContinuumACM Transactions on Internet Technology10.1145/322664419:2(1-21)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1145/3226644
Nikravesh AAjila SLung C(2018)Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision makerJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-018-0122-77:1(1-21)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1186/s13677-018-0122-7
Qu CCalheiros RBuyya R(2018)Auto-Scaling Web Applications in CloudsACM Computing Surveys10.1145/314814951:4(1-33)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.1145/3148149
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Towards an autonomic auto-scaling prediction system for cloud resource provisioning

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cover image ACM Conferences

SEAMS '15: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

May 2015

186 pages

Conference Chair:
Paola Inverardi
University of L'Aquila, Italy
,
Program Chair:
Bradley Schmerl
Carnegie Mellon University

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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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IEEE Press

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Published: 16 May 2015

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ICSE '15

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ICSE '15: 37th International Conference on Software Engineering

May 16 - 24, 2015

Florence, Italy

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Overall Acceptance Rate 17 of 31 submissions, 55%

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Cited By

Baresi LMendonça DGarriga MGuinea SQuattrocchi G(2019)A Unified Model for the Mobile-Edge-Cloud ContinuumACM Transactions on Internet Technology10.1145/322664419:2(1-21)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1145/3226644
Nikravesh AAjila SLung C(2018)Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision makerJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-018-0122-77:1(1-21)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1186/s13677-018-0122-7
Qu CCalheiros RBuyya R(2018)Auto-Scaling Web Applications in CloudsACM Computing Surveys10.1145/314814951:4(1-33)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.1145/3148149
da Rosa Righi RRodrigues VRostirolla GAndr da Costa CRoloff ENavaux P(2018)A lightweight plug-and-play elasticity service for self-organizing resource provisioning on parallel applicationsFuture Generation Computer Systems10.1016/j.future.2017.02.02378:P1(176-190)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.02.023
Nikravesh AAjila SLung C(2017)An autonomic prediction suite for cloud resource provisioningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-017-0073-46:1(1-20)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1186/s13677-017-0073-4
Rodrigues VDa Rosa Righi RRostirolla GVictória Barbosa JAndré Da Costa CAlberti AChang V(2017)Towards Enabling Live Thresholding as Utility to Manage Elastic Master-Slave Applications in the CloudJournal of Grid Computing10.1007/s10723-017-9405-315:4(535-556)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10723-017-9405-3
Baresi LGuinea SLeva AQuattrocchi GZimmermann TCleland-Huang JSu Z(2016)A discrete-time feedback controller for containerized cloud applicationsProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950328(217-228)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1145/2950290.2950328
Incerto ETribastone MTrubiani C(2016)Symbolic performance adaptationProceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/2897053.2897060(140-150)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2897053.2897060
Lung CZhang XRajeswaran P(2016)Improving software performance and reliability in a distributed and concurrent environment with an architecture-based self-adaptive frameworkJournal of Systems and Software10.1016/j.jss.2016.06.102121:C(311-328)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.jss.2016.06.102

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