[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83666-en":3,"doc-seo-83666-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83666,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces","Accurate cloud workload forecasting is essential for efficient resource management but is hindered by highly volatile traces with sudden bursts and intertwined multi-scale periodicities. SWIFT addresses these issues with a pure convolutional forecasting framework that replaces fixed wavelet bases via a Learnable Cascaded Wavelet Path for adaptive, data-driven feature peeling. It further uses a Multivariate Interaction Module to model inter-variable spatial and intra-variable interactions, stabilizing noisy states. Experiments show SOTA accuracy with linear O(L) complexity, cutting prediction error by up to 31.04% and reducing latency by 79.74%.","SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces  \nZeyuan Ding 1 Lingfeng Zheng 2 Dian Ding 1 Guangtao Xue 1  \narXiv :2607 .02524v1 [ cs .DC] 7 May 2026  \nAbstract  \nAccurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to sudden bursts. Although wavelets preserve temporal locality, rigid fixed bases struggle with complex patterns and isolated processing neglects critical spatial dependencies. To address this, we propose SWIFT, a pure convolutional framework designed for high-efficiency workload forecasting. We introduce a Learnable Cascaded Wavelet Path that reformulates the traditional fixed wavelet bases into adaptive convolutional operators, enabling precise, data-driven feature peeling. Complementing this, our Multivariate Interaction Module sequentially models inter-variable spatial and intra-variable feature interactions to stabilize andrefine noisy workload states. Extensive experiments demonstrate that SWIFT achieves SOTA accuracy with linear O (L) complexity, reducing prediction error by up to 31.04% while cutting latency by 79.74% .  \n1. Introduction  \nModern cloud environments leverage the synergy between containerized microservices to provide applications (Dinget al., 2022b ; Dua et al., 2014) . To reconcile Quality of Service (QoS) with optimal resource utilization, cloud providers use workload forecasting to drive proactive resource management strategies, such as scaling (Suleiman et al., 2023 ; Golshani & Ashtiani, 2021) and scheduling (Alahmad et al., 2021 ; Lamas & Demeulemeester, 2016) .  \nHowever, in large-scale cloud environments, achieving highperformance workload prediction faces two primary hurdles:  \n(1) Complex Workload Patterns: Temporally, real-world  \n1 Shanghai Jiao Tong University, Shanghai, China 2 Shaanxi Normal University, Xi’an, China. Correspondence to: Dian Ding \u003C[dingdian94@sjtu.edu.cn](dingdian94@sjtu.edu.cn) >, Guangtao Xue \u003Cgt [xue@sjtu.edu.cn](xue@sjtu.edu.cn) >.  \nPreprint. July 7, 2026.  \nTrace1 Burst  \nb) FFT yields identical spectra.  \nc) WT preserves temporal locality.  \nFigure 1. Motivation Analysis. (a) Workload Characteristics: Real-world cloud traces exhibit sudden bursts at distinct timestamps interlaced with multi-scale periodicities. (b) FFT Inferiority: Due to global integration, FFT blindly aggregates these distinct signals into indistinguishable spectra, effectively causing a loss of temporal locality. (c) WT Superiority: Unlike FFT, Wavelet Transform preserves temporal locality, enabling the precise distinction of burst timings and restoring their unique signatures  \ncloud traces are highly volatile and prone to sudden bursts. As illustrated in Figure 1(a), distinct workloads often exhibit sharp, localized fluctuations occurring at completely different timestamps. Furthermore, these traces contain interlaced multi-scale periodicities (e.g., daily and hourly patterns) compounded with intense non-stationarity (Zhao et al., 2024) . Spatially, microservice dependencies induce strong coupling and synchronized fluctuations across workload traces (Ding et al., 2022a) . Therefore, cloud workloads exhibit complex patterns that are difficult to capture.  \nPrior works (Wu et al., 2022 ; Yi et al., 2023 ; Piao et al., 2024) primarily utilize the Fast Fourier Transform (FFT) to decouple these features. However, FFT’s global basis functions cause a loss of temporal locality (Mallat, 1999) . As evident in Figure 1(b), distinct workload bursts yield indistinguishable spectra. This limitation prevents the model from distinguishing when a burst occurs. Consequently, the predictor reacts untimely to sudden changes, predicting a spike only after it has already occurred, which defeats the purpose of proactive scaling.  \nTo address this, recent studies have explored the Wavelet  \nTransform (WT) (Liao et al., 2020 ; Tamilselvi et al., 2025 ; Wang et al., 2025) . As ","cbCainDHWU9n0UvO","https://ap.wps.com/l/cbCainDHWU9n0UvO","pdf",2224603,4,1,20,"English","en",105,"# Introduction\n## Motivation Analysis\n## Challenges and Requirements","[{\"question\":\"What are the key difficulties SWIFT targets in cloud workload forecasting?\",\"answer\":\"SWIFT focuses on volatile workload traces with sudden bursts, intertwined multi-scale periodicities, and strong spatial coupling caused by microservice dependencies, which make patterns hard to capture accurately and timely.\"},{\"question\":\"How does SWIFT improve upon traditional wavelet and FFT approaches?\",\"answer\":\"Instead of rigid fixed wavelet bases, SWIFT uses a Learnable Cascaded Wavelet Path to convert wavelets into adaptive convolutional operators, preserving time-frequency locality while handling complex patterns better than global FFT.\"},{\"question\":\"What efficiency benefits does SWIFT provide compared with common high-performance models?\",\"answer\":\"SWIFT is designed as a pure convolutional framework with linear O(L) complexity, achieving lower prediction error (up to 31.04% reduction) and significantly reduced latency (up to 79.74% reduction) in 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are the key difficulties SWIFT targets in cloud workload forecasting?","Question",{"text":75,"@type":76},"SWIFT focuses on volatile workload traces with sudden bursts, intertwined multi-scale periodicities, and strong spatial coupling caused by microservice dependencies, which make patterns hard to capture accurately and timely.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SWIFT improve upon traditional wavelet and FFT approaches?",{"text":80,"@type":76},"Instead of rigid fixed wavelet bases, SWIFT uses a Learnable Cascaded Wavelet Path to convert wavelets into adaptive convolutional operators, preserving time-frequency locality while handling complex patterns better than global FFT.",{"name":82,"@type":73,"acceptedAnswer":83},"What efficiency benefits does SWIFT provide compared with common high-performance models?",{"text":84,"@type":76},"SWIFT is designed as a pure convolutional framework with linear O(L) complexity, achieving lower prediction error (up to 31.04% 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