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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM.RACCES058

ONLINE RESOURCES SCHEDULING UNDER CONCAVE PRICING FOR CLOUD COMPUTING-CLOUD COST OPTIMIZER

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.RACCES058

Volume:

10 |

4 |

April 2026

Pages:

1--

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.RACCES058

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

JK

Jayavarthini K

Computer Science & Engineering

Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India

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AM

Aarthi M

Computer Science & Engineering

Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India

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AB

Agasthiya B

Computer Science & Engineering

Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India

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AFM

Afrin Fathima M

Computer Science & Engineering

Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India

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Mentors

DMP

Dr Madhubala P

Computer Science & Engineering

Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India

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Abstract

With booming growth of cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (iaas) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different iaas service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers' resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker may help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under a special case cost function.

KEYWORDS

Keywords- Cloud Broker Resource Scheduling Cost Optimization Iaas Online Scheduling Algorithm

Cite This Article

Jayavarthini K, Aarthi M, Agasthiya B, Afrin Fathima M, Dr Madhubala P, "ONLINE RESOURCES SCHEDULING UNDER CONCAVE PRICING FOR CLOUD COMPUTING-CLOUD COST OPTIMIZER," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1--, April 2026. https://doi.org/10.55041/ijsrem.RACCES058

Full Article/Conclusion

         Cloud computing is an evolving paradigm, where new service providers are frequently coming into existence, offering services of similar functionality. Major challenge for a cloud customer is to select an appropriate service provider from the cloud marketplace to support its business needs. How-ever, service guarantees provided by vendors through SLAs contain ambiguous clauses which makes the job of selecting an ideal provider even more difficult. As customers use cloud services to process and store their individual client’s data, guarantees related to service quality level is of utmost importance. For this purpose, it is imperative from a customer’s perspective to establish trust relationship with a provider. Moreover, as customers are outsourcing their businesses onto a third-party cloud, capability or competence of CSP determines if former’s objectives are going to be accomplished.

References

[1] S. Tuli, G. Casale and N. R. Jennings, “MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Computing,” IEEE Transactions on Cloud Computing, 2022 [2] A. Q. Khan, M. Matskin and R. Prodan, “Cost Modelling and Optimization for Cloud Computing Environments,” Journal of Cloud Computing, 2024. [3] V. R. S. P. Alla, N. R. Medikondu and K. R. Rao, “Optimizing Task Scheduling in Cloud Computing using Hybrid Artificial Intelligence Techniques,” Cogent Engineering, 2024. [4] D. Bodra and S. Khairnar, “Machine Learning Based Cloud Resource Allocation Algorithms: A Review,” Frontiers in Computer Science, 2025. [5] D. Komarasamy, R. Rajavel and K. Harimoorthy, “Scalable Scheduling and Resource Management Framework for Cloud Native Applications,” Scientific Reports, 2025. [6] Q. Chen, C. Wang and Z. Li, “Online Resource Scheduling under Concave Pricing for Cloud Computing,” IEEE Transactions on Parallel and Distributed Systems, 2020. [7] H. Xu and B. Li, “Dynamic Cloud Resource Scheduling for Cost Optimization,” IEEE Cloud Computing Magazine, 2023. [8] J. Li, Y. Zhang and X. Chen, “Efficient Resource Allocation and Scheduling in Cloud Computing,” International Journal of Cloud Applications and Computing, 2026. [9] M. Zaharia, A. Konwinski and A. D. at Joseph, “Improving Resource Scheduling in Large Scale Cloud Systems,” IEEE Transactions on Cloud Computing, 2020 [10] X. Zhang, L. T. Yang and C. Liu, “Cost Efficient Resource Allocation in Cloud Computing Environments,” Future Generation Computer Systems, 2020.

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Publication Date

5/1/2026

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