{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:11:48Z","timestamp":1772759508110,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Cloud computing offers scalable and adaptable resources on demand, and has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud resources because of dynamic changes in load requirement. Existing forecasting approaches are unable to handle the intricate temporal symmetries and nonlinear patterns in cloud workload data, leading to degradation of prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems (MASNN-WL-RTSP-CS) is proposed. Here, the input data from the Google cluster trace dataset were preprocessed using Multi Window Savitzky\u2013Golay Filter (MWSGF) to remove noise while preserving important data patterns and maintaining structural symmetry in time series trends. Then, the Multi-Dimensional Attention Spiking Neural Network (MASNN) effectively models symmetric patterns in workload fluctuations to predict workload and resource time series. To enhance accuracy, the Secretary Bird Optimization Algorithm (SBOA) was utilized to optimize the MASNN parameters, ensuring accurate workload and resource time series predictions. Experimental results show that the MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, and 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, and 28.93% lower Mean Square Error (MSE), and 24.54%, 23.65%, and 23.62% lower Mean Absolute Error (MAE) compared with other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, and DCRNN-RUP-RP-CCE, respectively. These advances emphasize the utility of MASNN-WL-RTSP-CS in achieving more accurate workload and resource forecasts, thereby facilitating effective cloud resource management.<\/jats:p>","DOI":"10.3390\/sym17030383","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:04:49Z","timestamp":1740992689000},"page":"383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7937-4934","authenticated-orcid":false,"given":"Thulasi","family":"Karpagam","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, R.M.K College of Engineering and Technology, Chennai 601206, India"}]},{"given":"Jayashree","family":"Kanniappan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai 600123, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s12667-019-00368-6","article-title":"Adaptive cloud resource management through workload prediction","volume":"13","author":"Gadhavi","year":"2022","journal-title":"Energy Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.future.2020.12.005","article-title":"Online cloud resource prediction via scalable window waveform sampling on classified workloads","volume":"117","author":"Wang","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.ins.2020.07.012","article-title":"Self directed learning based workload forecasting model for cloud resource management","volume":"543","author":"Kumar","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105303","DOI":"10.1016\/j.engappai.2022.105303","article-title":"STOWP: A light-weight deep residual network integrated windowing strategy for storage workload prediction in cloud systems","volume":"115","author":"Bedi","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13677-020-00223-5","article-title":"Generic SDE and GA-based workload modeling for cloud systems","volume":"10","author":"Benmakrelouf","year":"2021","journal-title":"J. Cloud Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11227-021-04234-0","article-title":"A hybrid CNN-LSTM model for predicting server load in cloud computing","volume":"78","author":"Patel","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e5919","DOI":"10.1002\/cpe.5919","article-title":"Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems","volume":"33","author":"Rjoub","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1109\/TCC.2021.3059096","article-title":"OP-MLB: An online VM prediction-based multi-objective load balancing framework for resource management at cloud data center","volume":"10","author":"Saxena","year":"2021","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49808","DOI":"10.1109\/ACCESS.2022.3174061","article-title":"CANFIS: A chaos adaptive neural fuzzy inference system for workload prediction in the cloud","volume":"10","author":"Amekraz","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Malik, S., Tahir, M., Sardaraz, M., and Alourani, A. (2022). A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques. Appl. Sci., 12.","DOI":"10.3390\/app12042160"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109653","DOI":"10.1016\/j.comnet.2023.109653","article-title":"Intelligent time-series forecasting framework for non-linear dynamic workload and resource prediction in cloud","volume":"225","author":"Ullah","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.comcom.2022.11.018","article-title":"Host load prediction in cloud computing with discrete wavelet transformation (dwt) and bidirectional gated recurrent unit (bigru) network","volume":"198","author":"Dogani","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s11277-020-07773-6","article-title":"Performance assessment of time series forecasting models for cloud datacenter networks\u2019 workload prediction","volume":"116","author":"Kumar","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s10723-021-09561-3","article-title":"Cloud resource demand prediction using machine learning in the context of qos parameters","volume":"19","author":"Nawrocki","year":"2021","journal-title":"J. Grid Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Seshadri, K., Pavana, C., Sindhu, K., and Kollengode, C. (2022). Unsupervised Modeling of Workloads as an Enabler for Supervised Ensemble-based Prediction of Resource Demands on a Cloud. Advances in Data Computing, Communication and Security: Proceedings of I3CS2021, Springer.","DOI":"10.1007\/978-981-16-8403-6_10"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1109\/TPDS.2021.3079341","article-title":"A quantum approach towards the adaptive prediction of cloud workloads","volume":"32","author":"Singh","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s11227-022-04647-5","article-title":"On accurate prediction of cloud workloads with adaptive pattern mining","volume":"79","author":"Bao","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"234","DOI":"10.36548\/jscp.2021.3.008","article-title":"Cloud load estimation with deep logarithmic network for workload and time series optimization","volume":"3","author":"Bhalaji","year":"2021","journal-title":"J. Soft Comput. Paradig."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e4652","DOI":"10.1002\/ett.4652","article-title":"COSCO2: AI-augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers","volume":"34","author":"Karthikeyan","year":"2023","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_20","first-page":"1","article-title":"esDNN: Deep neural network based multivariate workload prediction in cloud computing environments","volume":"22","author":"Xu","year":"2022","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2020.11.011","article-title":"Integrated deep learning method for workload and resource prediction in cloud systems","volume":"424","author":"Bi","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TPDS.2023.3240567","article-title":"Performance analysis of machine learning centered workload prediction models for cloud","volume":"34","author":"Saxena","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10211","DOI":"10.1007\/s00521-021-06665-5","article-title":"A deep learning-based resource usage prediction model for resource provisioning in an autonomic cloud computing environment","volume":"34","author":"Bencherif","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s10723-022-09607-0","article-title":"Workload time series cumulative prediction mechanism for cloud resources using neural machine translation technique","volume":"20","year":"2022","journal-title":"J. Grid Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10586-020-03214-y","article-title":"Workload time series prediction in storage systems: A deep learning based approach","volume":"26","author":"Ruan","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1007\/s11227-022-04782-z","article-title":"Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism","volume":"79","author":"Dogani","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00607-022-01129-7","article-title":"Time series-based workload prediction using the statistical hybrid model for the cloud environment","volume":"105","author":"Devi","year":"2023","journal-title":"Computing"},{"key":"ref_28","unstructured":"(2025, January 06). Available online: https:\/\/www.kaggle.com\/datasets\/derrickmwiti\/google-2019-cluster-sample."},{"key":"ref_29","first-page":"4505214","article-title":"Smooth Deep Learning Magnetotelluric Inversion based on Physics-informed Swin Transformer and Multi-Window Savitzky-Golay Filter","volume":"61","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/TPAMI.2023.3241201","article-title":"Attention spiking neural networks","volume":"45","author":"Yao","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-024-10729-y","article-title":"Secretary bird optimization algorithm: A new metaheuristic for solving global optimization problems","volume":"57","author":"Fu","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Simic, V., Zivkovic, M., Alrasheedi, M., and Petrovic, A. (2023). Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm. Ann. Oper. Res., 1\u201334.","DOI":"10.1007\/s10479-023-05745-0"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"49760","DOI":"10.1109\/ACCESS.2021.3065170","article-title":"Migration-based load balance of virtual machine servers in cloud computing by load prediction using genetic-based methods","volume":"9","author":"Hung","year":"2021","journal-title":"IEEE Access"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:46:08Z","timestamp":1760028368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030383"],"URL":"https:\/\/doi.org\/10.3390\/sym17030383","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]}}}