Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Article Sidebar

PDF
Published: Nov 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10.8495
Keywords:
Load balancing, Resource allocation, Cloud computing, Optimization, task scheduling, SLA

Main Article Content

Nidhi Ranjan, Balasaheb Balkhande, Sanjivani Deokar, Torana Kamble, Chaitrali Chaudhari, Shrinivas T. Shirkande

Abstract

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.

Article Details

How to Cite
Nidhi Ranjan, et al. (2023). Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 320–331. https://doi.org/10.17762/ijritcc.v11i10.8495
Issue
Vol. 11 No. 10 (2023)
Section
Articles
Author Biography

Nidhi Ranjan, Balasaheb Balkhande, Sanjivani Deokar, Torana Kamble, Chaitrali Chaudhari, Shrinivas T. Shirkande

Dr. Nidhi Ranjan1, Dr. Balasaheb Balkhande2, Dr. Sanjivani Deokar3, Torana Kamble4, Dr. Chaitrali Chaudhari5, Dr. Shrinivas T. Shirkande6

1Associate Professor, Mumbai University, Mumbai, Maharashtra, India

2Associate Professor, Vasantdada Patil Pratishthan's College of Engineering & Visual Arts, Mumbai

Mumbai University, Maharashtra, India

3Department of Computer Engineering, Lokmanya Tilak College of Engineering, Mumbai University, India

4Assistant Professor, Bharati Vidyapeeth College of Engineering, Navi Mumbai, Mumbai University, India

5Department of Computer Engineering, Lokmanya Tilak College of Engineering, University of Mumbai, Maharashtra, India

6Principal, S.B.Patil College of Engineering Indapur, Pune

nidhipranjan@gmail.com1, balkhandeakshay@gmail.com2, sanjivanideokar@gmail.com3, torana.kamble@gmail.com4, chaitralichaudhari13@gmail.com5, shri.shirkande8@gmail.com6

References

H. Shukur, S. Zeebaree, R. Zebari, D. Zeebaree, O. Ahmed, and A. Salih, ‘‘Cloud computing virtualization of resources allocation for distributed systems,’’ J. Appl. Sci. Technol. Trends, vol. 1, no. 3, pp. 98–105, Jun. 2020, doi: 10.38094/jastt1331.

M. Agarwal and G. M. Saran Srivastava, ‘‘Cloud computing: A paradigm shift in the way of computing,’’ Int. J. Mod. Educ. Comput. Sci., vol. 9, no. 12, pp. 38–48, Dec. 2017, doi: 10.5815/ijmecs.2017.12.05.

N. Zanoon, ‘‘Toward cloud computing: Security and performance,’’ Int. J. Cloud Comput.: Services Archit., vol. 5, no. vol. 5, nos. 5–6, pp. 17–26, Dec. 2015, doi: 10.5121/ijccsa.2015.5602.

C. T. S. Xue and F. T. W. Xin, ‘‘Benefits and challenges of the adoption of cloud computing in business,’’ Int. J. Cloud Comput.: Services Archit., vol. 6, no. 6, pp. 1–15, Dec. 2016, doi: 10.5121/ijccsa.2016.6601.

D. A. Shafiq, N. Jhanjhi, and A. Abdullah, ‘‘Proposing a load balancing algorithm for the optimization of cloud computing applications,’’ in Proc. 13th Int. Conf. Math., Actuarial Sci., Comput. Sci. Statist. (MACS), Dec. 2019, pp. 1–6, doi: 10.1109/MACS48846.2019.9024785.

S. K. Mishra, B. Sahoo, and P. P. Parida, ‘‘Load balancing in cloud computing: A big picture,’’ J. King Saud Univ.–Comput. Inf. Sci., vol. 32, no. 2, pp. 149–158, 2020, doi: 10.1016/j.jksuci.2018.01.003.

I. Odun-Ayo, M. Ananya, F. Agono, and R. Goddy-Worlu, ‘‘Cloud computing architecture: A critical analysis,’’ in Proc. 18th Int. Conf. Comput. Sci. Appl. (ICCSA), Jul. 2018, pp. 1–7, doi: 10.1109/ICCSA.2018.8439638.

A. Jyoti, M. Shrimali, and R. Mishra, ‘‘Cloud computing and load balancing in cloud computing -survey,’’ in Proc. 9th Int. Conf. Cloud Comput., Data Sci. Eng. (Confluence), Jan. 2019, pp. 51–55, doi: 10.1109/confluence.2019.8776948.

S. H. H. Madni, M. S. Abd Latiff, M. Abdullahi, S. M. Abdulhamid, and M. J. Usman, ‘‘Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment,’’ PLoS ONE, vol. 12, no. 5, May 2017, Art. no. e0176321, doi: 10.1371/journal.pone.0176321.

M. Adhikari and T. Amgoth, ‘‘Heuristic-based load-balancing algorithm for IaaS cloud,’’ Future Gener. Comput. Syst., vol. 81, pp. 156–165, Apr. 2018, doi: 10.1016/j.future.2017.10.035.

B. Singh and G. Singh, ‘‘A study on virtualization and hypervisor in cloud computing,’’ Int. J. Comput. Sci. Mobile Appl., vol. 6, no. 1, pp. 17–22, 2018.

M. Kumar, S. C. Sharma, A. Goel, and S. P. Singh, ‘‘A comprehensive survey for scheduling techniques in cloud computing,’’ J. Netw. Comput. Appl., vol. 143, pp. 1–33, Oct. 2019, doi: 10.1016/j.jnca.2019.06.006.

F. Zabini, A. Bazzi, B. M. Masini, and R. Verdone, ‘‘Optimal performance versus fairness tradeoff for resource allocation in wireless systems,’’ IEEE Trans. Wireless Commun., vol. 16, no. 4, pp. 2587–2600, Apr. 2017, doi: 10.1109/TWC.2017.2667644.

M. Kumar and S. C. Sharma, ‘‘Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment,’’ Int. J. Comput. Appl., vol. 42, no. 1, pp. 108–117, Jan. 2020, doi: 10.1080/1206212X.2017.1404823.

G. Patel, R. Mehta, and U. Bhoi, ‘‘Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing,’’ Procedia Comput. Sci., vol. 57, pp. 545–553, 2015, doi: 10.1016/j.procs.2015.07.385.

M. A. Alworafi, A. Dhari, A. A. Al-Hashmi, and A. B. Darem, ‘‘An improved SJF scheduling algorithm in cloud computing environment,’’ in Proc. Int. Conf. Electr., Electron., Commun., Comput. Optim. Techn. (ICEECCOT), Dec. 2016, pp. 208–212, doi: 10.1109/ICEECCOT.2016.7955216.

A. V. Lakra and D. K. Yadav, ‘‘Multi-objective tasks scheduling algorithm for cloud computing throughput optimization,’’ Procedia Comput. Sci., vol. 48, pp. 107–113, 2015, doi: 10.1016/j.procs.2015.04.158.

B. J. H. Shanthan and L. Arockiam, ‘‘Resource based load balanced min min algorithm (RBLMM) for static meta task scheduling in cloud,’’ in Proc. IC-ACT, 2018, pp. 1–5.

A. Thomas, G. Krishnalal, and V. P. Jagathy Raj, ‘‘Credit based scheduling algorithm in cloud computing environment,’’ Procedia Comput. Sci., vol. 46, pp. 913–920, 2015, doi: 10.1016/j.procs.2015.02.162.

H. Gamal El Din Hassan Ali, I. A. Saroit, and A. M. Kotb, ‘‘Grouped tasks scheduling algorithm based on QoS in cloud computing network,’’ Egyptian Informat. J., vol. 18, no. 1, pp. 11–19, Mar. 2017, doi: 10.1016/j.eij.2016.07.002.

S. Banerjee, M. Adhikari, S. Kar, and U. Biswas, ‘‘Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud,’’ Arabian J. Sci. Eng., vol. 40, no. 5, pp. 1409–1425, May 2015, doi: 10.1007/s13369-015-1626-9.

R. Kaur and P. Luthra, ‘‘Load balancing in cloud system using max min and min min algorithm,’’ in Proc. Nat. Conf. Emerg. Trends Comput. Technol. NCETCT, vol. 1, 2014, pp. 31–34.

A. Arunarani, D. Manjula, and V. Sugumaran, ‘‘Task scheduling techniques in cloud computing: A literature survey,’’ Future Gener. Comput. Syst., vol. 91, pp. 407–415, Feb. 2019, doi: 10.1016/j.future.2018.09.014.

P. Kathalkar, ‘‘Challenges & issues in load balancing in cloud computing,’’ Int. J. Res. Appl. Sci. Eng. Technol., vol. 6, no. 4, pp. 963–968, Apr. 2018, doi: 10.22214/ijraset.2018.4163.

S. Afzal and K. Ganesh, ‘‘A taxonomic classification of load balancing metrics: A systematic review,’’ in Proc. 33rd Indian Eng. Congr., Jan. 2019, pp. 85–90.

S. Afzal and G. Kavitha, ‘‘Load balancing in cloud computing–A hierarchical taxonomical classification,’’ J. Cloud Comput., vol. 8, no. 1, p. 22, 2019, doi: 10.1186/s13677-019-0146-7.

R. K. Naha and M. Othman, ‘‘Cost-aware service brokering and performance sentient load balancing algorithms in the cloud,’’ J. Netw. Comput. Appl., vol. 75, pp. 47–57, Nov. 2016, doi: 10.1016/j.jnca.2016.08.018.

Samir N Ajani Piyush K. Ingole , Apeksha V. Sakhare “Modality of Multi-Attribute Decision Making for Network Selection in Heterogeneous Wireless Networks”, Ambient Science - National Cave Research and Protection Organization, India,2022, Vol.9, Issue.2, ISSN- 2348 5191. DOI:10.21276/ambi.2022.09.2.ta02

P. Kumar and R. Kumar, ‘‘Issues and challenges of load balancing techniques in cloud computing: A survey,’’ ACM Comput. Surv., vol. 51, no. 6, pp. 1–35, Feb. 2019, doi: 10.1145/3281010.

A. Jindal, ‘‘Optimization of task scheduling algorithm through QoS parameters for cloud computing,’’ in Proc. ICAET, vol. 57, 2016, pp. 1–4, doi: 10.1051/matecconf/20165702009.

A. Semmoud, M. Hakem, B. Benmammar, and J. Charr, ‘‘Load balancing in cloud computing environments based on adaptive starvation threshold,’’ Concurrency Comput., Pract. Exper., vol. 32, no. 11, pp. 1–14, Jun. 2020, doi: 10.1002/cpe.5652.

V. Polepally and K. Shahu Chatrapati, ‘‘Dragonfly optimization and constraint measure-based load balancing in cloud computing,’’ Cluster Comput., vol. 22, no. S1, pp. 1099–1111, Jan. 2019, doi: 10.1007/s10586-017- 1056-4.

S.-C. Wang, K.-Q. Yan, W.-P. Liao, and S.-S. Wang, ‘‘Towards a load balancing in a three-level cloud computing network,’’ in Proc. 3rd Int. Conf. Comput. Sci. Inf. Technol., Jul. 2010, pp. 108–113, doi: 10.1109/ICCSIT.2010.5563889.

A. Thakur and M. S. Goraya, ‘‘A taxonomic survey on load balancing in cloud,’’ J. Netw. Comput. Appl., vol. 98, pp. 43–57, Nov. 2017, doi: 10.1016/j.jnca.2017.08.020.

J. K. Konjaang, F. H. Ayob, and A. Muhammed, ‘‘An optimized max-min scheduling algorithm in cloud computing,’’ J. Theor. Appl. Inf. Technol., vol. 95, no. 9, pp. 1916–1926, 2017.

A. Dhari and K. I. Arif, ‘‘An efficient load balancing scheme for cloud computing,’’ Indian J. Sci. Technol., vol. 10, no. 11, pp. 1–8, Mar. 2017, doi: 10.17485/ijst/2017/v10i11/110107.