Systematic Mapping Study: Research Opportunities on Capacity Planning

  • Yuggo Afrianto Universitas Ibn Khaldun
  • Rendy Munadi Telkom University
  • Setyorini Telkom University
  • Arief Goeritno Universitas Ibn Khaldun
Keywords: Capacity planning, LDA, Review, Systematic Mapping Study


The central idea of the research is to improve the efficiency and sustainability of data centers by implementing accurate capacity planning, which will also improve their performance and availability. Various literature reviews have been conducted to understand the current status of capacity planning implementation in different domains and perspectives. However, a more organized and systematic approach is required to map research and implementation results in the relevant areas of capacity planning that have the potential for further development. The present study aims to fill this gap by conducting a systematic mapping study that combines both quantitative and qualitative methodologies. The quantitative approach involved the collection of literature and the classification of topics using the Latent Dirichlet Allocation (LDA) method. On the contrary, the qualitative approach used content analysis to identify future research directions based on keyword trends and topics. The PRISMA framework was followed to guide the search for relevant studies in electronic research literature databases. The mapping results revealed 15 topics, with topics 8, 10, 11, and 15 showing significant potential for further research and exhibiting increasing trends. The identified topics encompass capacity planning, energy and resource management, computing and technology, data analysis and statistics, engineering, and industry, all crucial for businesses and industries to operate efficiently and sustainably. This study provides a comprehensive overview of the state of capacity planning implementation and highlights areas that require further investigation.



Download data is not yet available.


S. Ramli and D. I. Jambari, “Capacity planning for green data center sustainability,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 4, pp. 1372–1380, Aug. 2018, doi: 10.18517/ijaseit.8.4.6417.

T. Wu, M. Pan, and Y. Yu, “A Long-term Cloud Workload Prediction Framework for Reserved Resource Allocation,” in Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022, 2022, pp. 134–139, doi: 10.1109/SCC55611.2022.00030.

C. Ruf, J. F. Bard, and R. Kolisch, “Workforce capacity planning with hierarchical skills, long-term training, and random resignations,” Int. J. Prod. Res., vol. 60, no. 2, pp. 783–807, 2022, doi: 10.1080/00207543.2021.2017058.

Y. R. Sagaert, N. Kourentzes, S. De Vuyst, E. H. Aghezzaf, and B. Desmet, “Incorporating macroeconomic leading indicators in tactical capacity planning,” Int. J. Prod. Econ., vol. 209, pp. 12–19, 2019, doi: 10.1016/j.ijpe.2018.06.016.

X. Xu, E. Du, Y. Gao, N. Zhang, and J. Li, “Overview on Research and Application of Power System Operation Simulation and Capacity Planning Tools,” Dianli Xitong Zidonghua/Automation Electr. Power Syst., vol. 46, no. 2, pp. 188–199, 2022, doi: 10.7500/AEPS20201104004.

K. S. Shehadeh and R. Padman, “Stochastic optimization approaches for elective surgery scheduling with downstream capacity constraints: Models, challenges, and opportunities,” Comput. Oper. Res., vol. 137, p. 105523, Jan. 2022, doi: 10.1016/j.cor.2021.105523.

N. Banerjee, A. Morton, and K. Akartunalı, “Passenger demand forecasting in scheduled transportation,” Eur. J. Oper. Res., vol. 286, no. 3, pp. 797–810, Nov. 2020, doi: 10.1016/j.ejor.2019.10.032.

G. A. García-Mireles, M. Á. Moraga, F. García, C. Calero, and M. Piattini, “Interactions between environmental sustainability goals and software product quality: A mapping study,” Inf. Softw. Technol., vol. 95, pp. 108–129, Mar. 2018, doi: 10.1016/j.infsof.2017.10.002.

L. Huang, Z. Hou, Y. Fang, J. Liu, and T. Shi, “Evolution of CCUS Technologies Using LDA Topic Model and Derwent Patent Data,” Energies, vol. 16, no. 6, p. 2556, Mar. 2023, doi: 10.3390/en16062556.

A. García-Holgado, S. Marcos-Pablos, and F. García-Peñalvo, “Guidelines for performing Systematic Research Projects Reviews,” Int. J. Interact. Multimed. Artif. Intell., vol. 6, no. 2, p. 9, 2020, doi: 10.9781/ijimai.2020.05.005.

U. Chauhan and A. Shah, “Topic Modeling Using Latent Dirichlet allocation: A Survey,” ACM Comput. Surv., vol. 54, no. 7, pp. 1–35, Sep. 2022, doi: 10.1145/3462478.

E. Izadpanahi, A. Downward, T. Arthanari, and Y. Liu, “[1] Robust optimization for energy transition planning in manufacturing firms: An integrated model addressing economic and environmental issues,” J. Clean. Prod., vol. 334, 2022, doi: 10.1016/j.jclepro.2021.130237.

L. C. Wang, A. Wang, and C. Y. Chueh, “[3] Development of a capacity analysis and planning simulation model for semiconductor fabrication,” Int. J. Adv. Manuf. Technol., vol. 99, no. 1–4, pp. 37–52, 2018, doi: 10.1007/s00170-016-9089-z.

R. L. Burdett, E. Kozan, M. Sinnott, D. Cook, and Y. C. Tian, “[4] A mixed integer linear programing approach to perform hospital capacity assessments,” Expert Syst. Appl., vol. 77, pp. 170–188, 2017, doi: 10.1016/j.eswa.2017.01.050.

A. L. Bukar, C. W. Tan, L. K. Yiew, R. Ayop, and W. S. Tan, “A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm,” Energy Convers. Manag., vol. 221, 2020, doi: 10.1016/j.enconman.2020.113161.

A. L. Bukar, C. W. Tan, L. K. Yiew, R. Ayop, and W. S. Tan, “A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm,” Energy Convers. Manag., vol. 221, 2020, doi: 10.1016/j.enconman.2020.113161.

J. Wang et al., “Multi-objective capacity programming and operation optimization of an integrated energy system considering hydrogen energy storage for collective energy communities,” Energy Convers. Manag., vol. 268, 2022, doi: 10.1016/j.enconman.2022.116057.

A. Leivadeas, M. Falkner, I. Lambadaris, and G. Kesidis, “Optimal virtualized network function allocation for an SDN enabled cloud,” Comput. Stand. Interfaces, vol. 54, pp. 266–278, 2017, doi: 10.1016/j.csi.2017.01.001.

P. Pereira, J. Araujo, M. Torquato, J. Dantas, C. Melo, and P. Maciel, “Stochastic performance model for web server capacity planning in fog computing,” J. Supercomput., vol. 76, no. 12, pp. 9533–9557, 2020, doi: 10.1007/s11227-020-03218-w.

S. Kalayci and S. Arslan, “A dynamic programming based optimization approach for appointment scheduling in banking ,” in 2nd International Conference on Computer Science and Engineering, UBMK 2017, 2017, pp. 625–629, doi: 10.1109/UBMK.2017.8093482.

S. Kalayci and S. Arslan, “Bankacilikta Randevulu Siralama için Dinamik Programlama Tabanli Eniyileme Yaklaşimi,” 2nd Int. Conf. Comput. Sci. Eng. UBMK 2017, pp. 625–629, 2017, doi: 10.1109/UBMK.2017.8093482.

D. H. Kwak, Y. I. Cho, S. W. Choe, H. J. Kwon, and J. H. Woo, “Optimization of long-term planning with a constraint satisfaction problem algorithm with a machine learning,” Int. J. Nav. Archit. Ocean Eng., vol. 14, 2022, doi: 10.1016/j.ijnaoe.2022.100442.

W. Matoussi and T. Hamrouni, “A new temporal locality-based workload prediction approach for SaaS services in a cloud environment,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 3973–3987, 2022, doi: 10.1016/j.jksuci.2021.04.008.

F. Kulmer, M. Wolf, and C. Ramsauer, “Medium-term Capacity Management through Reinforcement Learning - Literature review and concept for an industrial pilot-application,” in Procedia CIRP, 2022, vol. 107, pp. 1065–1070, doi: 10.1016/j.procir.2022.05.109.

G. Garbi, E. Incerto, and M. Tribastone, “Learning queuing networks by recurrent neural networks,” in ICPE 2020 - Proceedings of the ACM/SPEC International Conference on Performance Engineering, 2020, pp. 56–66, doi: 10.1145/3358960.3379134.

P. Buchholz and S. Vastag, “Toward an analytical method for SLA validation,” Softw. Syst. Model., vol. 17, no. 2, pp. 527–545, 2018, doi: 10.1007/s10270-017-0604-y.

X. Xiang, C. Liu, L. H. Lee, and E. P. Chew, “Performance Estimation and Design Optimization of a Congested Automated Container Terminal,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 3, pp. 2437–2449, 2022, doi: 10.1109/TASE.2021.3085329.

D. Tyrovolas, S. A. Tegos, E. C. Dimitriadou-Panidou, P. D. Diamantoulakis, C. K. Liaskos, and G. K. Karagiannidis, “Performance Analysis of Cascaded Reconfigurable Intelligent Surface Networks,” IEEE Wirel. Commun. Lett., p. 1, 2022, doi: 10.1109/LWC.2022.3184635.

Q. Chen, K. Wang, Z. Bian, I. Cremer, G. Xu, and Y. Guo, “Cluster Performance Simulation for Spark Deployment Planning, Evaluation and Optimization,” Advances in Intelligent Systems and Computing, vol. 676. Software and Service Group, Intel Corporation, Shang Hai, China, pp. 34–51, 2018, doi: 10.1007/978-3-319-69832-8_3.

G. P. Gibilisco, M. Li, L. Zhang, and D. Ardagna, “Stage aware performance modeling of DAG based in memory analytic platforms,” in IEEE International Conference on Cloud Computing, CLOUD, 2017, pp. 188–195, doi: 10.1109/CLOUD.2016.32.

G. Raicu and A. Raicu, “Cloud computing environment for engineering and business education,” Adv. Mater. Res., vol. 837, pp. 651–656, 2014, doi: 10.4028/

S. Madireddy et al., “Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity,” 2017, doi: 10.1109/NAS.2017.8026844.

K. Gai, Z. Du, M. Qiu, and H. Zhao, “Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry,” Proc. - 2nd IEEE Int. Conf. Cyber Secur. Cloud Comput. CSCloud 2015 - IEEE Int. Symp. Smart Cloud, IEEE SSC 2015, pp. 1–6, 2016, doi: 10.1109/CSCloud.2015.73.

N. Amsel et al., “Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis,” in Proceedings of INDIS 2020: Innovating the Network for Data-Intensive Science, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis, 2020, pp. 68–78, doi: 10.1109/INDIS51933.2020.00012.

“Document details - Back to the future of IT resource performance modeling and capacity planning.”

S. Ravindra, M. Dayarathna, and S. Jayasena, “Latency aware elastic switching-based stream processing over compressed data streams,” in ICPE 2017 - Proceedings of the 2017 ACM/SPEC International Conference on Performance Engineering, 2017, pp. 91–102, doi: 10.1145/3030207.3030227.

Y. Zhou, X. Wang, M. M. Naim, and J. Gosling, “A system dynamics archetype to mitigate rework effects in engineer-to-order supply chains,” Int. J. Prod. Econ., p. 108620, 2022, doi: 10.1016/j.ijpe.2022.108620.

H. Joe, S. Kim, and B. B. Kang, “Quantitative Server Sizing Model for Performance Satisfaction in Secure U2L Migration,” IEEE Access, vol. 9, pp. 142449–142460, 2021, doi: 10.1109/ACCESS.2021.3119397.

M. A. Cardin, Y. Deng, and C. Sun, “Real options and flexibility analysis in design and management of one-way mobility on-demand systems using decision rules,” Transp. Res. Part C Emerg. Technol., vol. 84, pp. 265–287, 2017, doi: 10.1016/j.trc.2017.08.006.

How to Cite
Yuggo Afrianto, Rendy Munadi, Setyorini, & Arief Goeritno. (2023). Systematic Mapping Study: Research Opportunities on Capacity Planning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 904 - 913.
Information Technology Articles