AI-Optimized Cost-Aware Design Strategies for Resource-Efficient Applications

Authors

  • Raghava Satya SaiKrishna Dittakavi ACM Author

DOI:

https://doi.org/10.55662/JST.2023.4101

Keywords:

Artificial Intelligence, Cloud computing, Cloud-based Services

Abstract

In the context of modern computing landscapes marked by escalating resource demands and cost considerations, this paper introduces a novel framework that integrates artificial intelligence (AI) for the creation of resource-efficient applications while maintaining a keen awareness of costs. The imperative to strike a harmonious equilibrium between application performance and expenses has never been more pressing, especially with the proliferation of cloud-based services. In response, our approach capitalizes on AI methodologies to dynamically analyze real-time application requisites, workload trends, and the availability of resources. Central to our methodology is the elevation of cost to a principal design determinant. We devise strategies that dynamically apportion resources, opt for suitable service tiers, and make necessary adjustments to application configurations. This duality of optimizing performance while curtailing expenditure underscores the essence of our approach. Rigorous simulations and empirical evaluations underscore the efficacy of our strategies across diverse scenarios, underscoring substantial cost reductions without compromising the quality of applications.

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References

R. S. S. Dittakavi, "IAAS CLOUD ARCHITECTURE DISTRIBUTED CLOUD INFRA STRUCTURES AND VIRTUALIZED DATA CENTERS."

G. Erion et al., "A cost-aware framework for the development of AI models for healthcare applications," Nature Biomedical Engineering, vol. 6, no. 12, pp. 1384-1398, 2022.

https://doi.org/10.1038/s41551-022-00872-8

PMid:35393566 PMCid:PMC9537352

X. Bao, N. J. Jorgensen, and B. Namatherdhala, "System and method for matching specialists and potential clients," ed: Google Patents, 2023.

A. Verma, P. Ahuja, and A. Neogi, "pMapper: power and migration cost aware application placement in virtualized systems," in ACM/IFIP/USENIX international conference on distributed systems platforms and open distributed processing, 2008: Springer, pp. 243-264.

https://doi.org/10.1007/978-3-540-89856-6_13

T. Ouyang, X. Chen, L. Zeng, and Z. Zhou, "Cost-aware edge resource probing for infrastructure-free edge computing: From optimal stopping to layered learning," in 2019 IEEE Real-Time Systems Symposium (RTSS), 2019: IEEE, pp. 380-391.

https://doi.org/10.1109/RTSS46320.2019.00041

PMid:30248458

G. Erion et al., "CoAI: Cost-aware artificial intelligence for health care," Nature biomedical engineering, vol. 6, no. 12, p. 1384, 2022.

https://doi.org/10.1101/2021.01.19.21249356

G.-H. Lee, U. E. Akpudo, and J.-W. Hur, "FMECA and MFCC-based early wear detection in gear pumps in cost-aware monitoring systems," Electronics, vol. 10, no. 23, p. 2939, 2021.

https://doi.org/10.3390/electronics10232939

A. B. Kareem, U. Ejike Akpudo, and J.-W. Hur, "An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis," Electronics, vol. 10, no. 20, p. 2487, 2021.

https://doi.org/10.3390/electronics10202487

B. Boroumand, E. Yaghoubi, and B. Barekatain, "An enhanced cost-aware mapping algorithm based on improved shuffled frog leaping in network on chips," The Journal of Supercomputing, vol. 77, pp. 498-522, 2021.

https://doi.org/10.1007/s11227-020-03271-5

J. Chen, C.-H. Chang, J. Ding, R. Qiao, and M. Faust, "Tap delay-and-accumulate cost aware coefficient synthesis algorithm for the design of area-power efficient FIR filters," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 2, pp. 712-722, 2017.

https://doi.org/10.1109/TCSI.2017.2725916

J.-w. Park, M.-W. Lee, J. Kim, S.-w. Hwang, and S. Kim, "Costriage: A cost-aware triage algorithm for bug reporting systems," in Proceedings of the AAAI Conference on Artificial Intelligence, 2011, vol. 25, no. 1, pp. 139-144.

https://doi.org/10.1609/aaai.v25i1.7839

R. Han, M. M. Ghanem, L. Guo, Y. Guo, and M. Osmond, "Enabling cost-aware and adaptive elasticity of multi-tier cloud applications," Future Generation Computer Systems, vol. 32, pp. 82-98, 2014.

https://doi.org/10.1016/j.future.2012.05.018

V. Eramo, F. G. Lavacca, T. Catena, and F. Di Giorgio, "Reconfiguration of optical-NFV network architectures based on cloud resource allocation and QoS degradation cost-aware prediction techniques," IEEE Access, vol. 8, pp. 200834-200850, 2020.

https://doi.org/10.1109/ACCESS.2020.3035749

G. Fursin, A. Memon, C. Guillon, and A. Lokhmotov, "Collective Mind, Part II: Towards performance-and cost-aware software engineering as a natural science," arXiv preprint arXiv:1506.06256, 2015.

P. Luong, D. Nguyen, S. Gupta, S. Rana, and S. Venkatesh, "Adaptive cost-aware Bayesian optimization," Knowledge-Based Systems, vol. 232, p. 107481, 2021.

https://doi.org/10.1016/j.knosys.2021.107481

Y. Zhou, D. Sokolov, and A. Yakovlev, "Cost-aware synthesis of asynchronous circuits based on partial acknowledgement," in Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design, 2006, pp. 158-163.

https://doi.org/10.1109/ICCAD.2006.320080

S. Long, W. Long, Z. Li, K. Li, Y. Xia, and Z. Tang, "A game-based approach for cost-aware task assignment with QoS constraint in collaborative edge and cloud environments," IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, pp. 1629-1640, 2020.

https://doi.org/10.1109/TPDS.2020.3041029

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Published

28-02-2023

How to Cite

[1]
R. S. S. Dittakavi, “AI-Optimized Cost-Aware Design Strategies for Resource-Efficient Applications”, J. Sci. Tech., vol. 4, no. 1, pp. 1–10, Feb. 2023, doi: 10.55662/JST.2023.4101.