Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
General Research Article

Container Elasticity: Based on Response Time using Docker

Author(s): Mahendra Pratap Yadav*, Harishchandra A. Akarte and Dharmendra Kumar Yadav

Volume 15, Issue 5, 2022

Published on: 12 October, 2020

Article ID: e060422186807 Pages: 13

DOI: 10.2174/2666255813999201012192010

Price: $65

conference banner
Abstract

Objectives: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at the right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for the successful completion of tasks in a dynamic environment efficiently.

Methods: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on the response time of container using mechanism of control theory.

Results: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload.

Conclusion: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.

Keywords: Cloud computing, elasticity, virtualization, container, auto-scaling, time series, control theory

[1]
Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-art and research challenges", J. Internet Serv. Appl., vol. 1, no. 1, pp. 7-18, 2010.
[http://dx.doi.org/10.1007/s13174-010-0007-6]
[2]
R. Moreno-Vozmediano, R.S. Montero, and I.M. Llorente, "Iaas cloud architecture: From virtualized datacenters to federated cloud infrastructures", Computer, vol. 45, no. 12, pp. 65-72, 2012.
[http://dx.doi.org/10.1109/MC.2012.76]
[3]
J. Smith, and R. Nair, Virtual machines: versatile platforms for systems and processes., Elsevier, 2005.
[4]
K.T. Seo, H.S. Hwang, I.Y. Moon, O.Y. Kwon, and B.J. Kim, "Performance comparison analysis of linux container and virtual machine for building cloud", Advanced Science and Technology Letters, vol. 66, pp. 105-111, 2014.
[http://dx.doi.org/10.14257/astl.2014.66.25]
[5]
R. Moreno-Vozmediano, R.S. Montero, E. Huedo, and I.M. Llorente, "Efficient resource provisioning for elastic Cloud services based on machine learning techniques", Journal of Cloud Computing, vol. 8, no. 1, 2019.
[6]
J. Jiang, "Optimal cloud resource auto-scaling for web applications", 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013
[7]
Valter Rog’erio Messias, "Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure", Neural Comput. Appl., pp. 2383-2406, 2016.
[8]
A. Coca˜na-Fern’andez, L. S’anchez, and R. Jos’e, "Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters", Eng. Appl. Artif. Intell., vol. 48, pp. 95-105, 2016.
[http://dx.doi.org/10.1016/j.engappai.2015.10.003]
[9]
Y. Al-Dhuraibi, F. Paraiso, N. Djarallah, and P. Merle, "Elasticity in cloud computing: state of the art and research challenges", IEEE Trans. Serv. Comput., vol. 11, no. 2, pp. 430-447, 2017.
[http://dx.doi.org/10.1109/TSC.2017.2711009]
[10]
M.C. De Abranches, and P. Solis, "An algorithm based on response time and traffic demands to scale containers on a Cloud Computing system", 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), , 2016pp. 343-350 "An algorithm based on response time and traffic demands to scale containers on a Cloud Computing system".
[http://dx.doi.org/10.1109/NCA.2016.7778639]
[11]
Y. Al-Dhuraibi, F. Paraiso, N. Djarallah, and P. Merle, "Autonomic vertical elasticity of docker containers with elasticdocker", In: 2017 IEEE 10th international conference on cloud computing (CLOUD),, 2017, pp. 472-479. (pp. 472-479). IEEE
[http://dx.doi.org/10.1109/CLOUD.2017.67]
[12]
M. Mesbahi, and A.M. Rahmani, "Load balancing in cloud computing: a state of the art survey", International Journal of Modern Education and Computer Science, vol. 8, no. 3, p. 64, 2016.
[http://dx.doi.org/10.5815/ijmecs.2016.03.08]
[13]
"HAPROXY", Haproxydocumentation.", github.io/haproxydconv/2.0/intro.html3.1/
[14]
"LOAD BALANCING", Introduction to haproxy and load balancingconcepts.", https://www.digitalocean. com/community/tutorials/an-introductionto-haproxy-and-load-balancingconcepts/
[15]
E.J. Ghomi, A.M. Rahmani, and N.N. Qader, "Load-balancing algorithms in cloud computing: A survey", J. Netw. Comput. Appl., vol. 88, pp. 50-71, 2017.
[http://dx.doi.org/10.1016/j.jnca.2017.04.007]
[16]
""Introduction to docker",", https://docs.docker.com/config/containers/resource constraints/
[17]
M. ANICAS, Anintroduction to haproxy and load balancingconcepts.www.digitalocean.com/community/tutorials/anintroduction-to-haproxy-andload-balancing-concepts
[18]
"APACHE BENCHMARK", Apache http server benchmarking", tool.httpd.apache.org/docs/2.4/programs/ab.html
[19]
Y. Al-Dhuraibi, F. Zalila, N. Djarallah, and P. Merle, "Coordinating vertical elasticity of both containers and virtual machines", In proceding of the 8th international conference on cloud computing and services science: Volume-1, pages-322-329.
[http://dx.doi.org/10.5220/0006652403220329]
[20]
M.Z. Hasan, E. Magana, A. Clemm, L. Tucker, and S.L.D. Gudreddi, “Integrated and autonomic cloud resource scaling”, 2012 IEEE network operations and management symposium., IEEE, pp. 1327-1334, 2012.
[http://dx.doi.org/10.1109/NOMS.2012.6212070]
[21]
H.C. Lim, S. Babu, J.S. Chase, and S.S. Parekh, "Automated control in cloud computing: challenges and opportunities", In: Proceedings of the 1st workshop on Automated control for datacenters and clouds, 2009, pp. 13-18.
[http://dx.doi.org/10.1145/1555271.1555275]
[22]
P. Padala, K.Y. Hou, K.G. Shin, X. Zhu, M. Uysal, Z. Wang, and A. Merchant, "Automated control of multiple virtualized resources", In: Proceedings of the 4th ACM European conference on Computer systems, 2009, pp. 13-26.
[23]
Z. Gong, X. Gu, and J. Wilkes, "Press: Predictive elastic resource scaling for cloud systems", In: 2010 International Conference on Network and Service Management, 2010, pp. 9-16.
[http://dx.doi.org/10.1109/CNSM.2010.5691343]
[24]
R. Poddar, A. Vishnoi, and V. Mann, "HAVEN:Holistic load balancing and auto scaling in the cloud", In: 2015 7th International Conference on Communication Systems and Networks (COMSNETS), 2015, pp. 1-8.
[25]
""Google Horizontal Pod Autoscaler"", https://github.com/kubernetes/kubernetes/blob/release-1.2/docs/design/
[26]
N. Roy, A. Dubey, and A. Gokhale, "Efficient autoscaling in the cloud using predictive models for workload forecasting", IEEE 4th International Conference on Cloud Computing, , 2011",
[27]
"INSTRUMENTS, E", Pidcontroller", https://www.electricaltechnology.org/2015/10/whatis-pid-controllerhow-it-works.html
[28]
""National Instruments", National Instruments explicando a teoria pid 2015", http://www.ni.com/whitepaper/3782/pt/

Rights & Permissions Print Cite
Article Metrics
12
© 2024 Bentham Science Publishers | Privacy Policy