STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms

2026-05-25 · Ahmed Abdulaal, Maruf Aytekin, Thilaga kumaran Srinivasan, Tomer Lancewicki arxiv

Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions. Current solutions are limited to the univariate space, typically focusing on CPU usage alone to drive scaling decisions. Moreover, they address the problem as a purely forecasting task, focusing on prediction precision while neglecting the greater risks of underestimation and delays in system responsiveness. Alternative solutions are computationally complex, making them impractical for large-scale, real-time deployments. To address these challenges, we present STARIXNet, a lightweight neural network that guides resource allocation decisions in the multivariate space by capturing spatio-temporal relationships among multiple system metrics. STARIXNet models multiple quasi-dependent attributes, in particular the (S)easonal, (T)emporal, (A)uto-(R)egressive (I)ntegrated, and e(X)ogenous patterns, then implements an aggregation policy to finalize scaling decisions, prioritizing service stability, followed by cost-efficiency, over raw forecast accuracy. We empirically demonstrate the performance of STARIXNet by benchmarking against existing solutions in real-world settings. STARIXNet is deployed for critical production microservices at Walmart achieving tangible savings ranging from 10\% to 50\%, in addition to intangible benefits through improved service stability and customer experience.

📄 PDF Abstract BibTeX arXiv:2606.07565

Code (0)

등록된 구현이 없습니다.

A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction

2020-08-02 · Takanori Fujiwara, Shilpika, Naohisa Sakamoto, Jorji Nonaka 외

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and fea…

Contrastive Learning Dimensionality Reduction Time Series Time Series Analysis

GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection

2022-05-23 · Xu Chen, Qiu Qiu, Changshan Li, Kunqing Xie

In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challenges for…

Anomaly Detection Graph Neural Network Management Time Series+2

Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series

2019-03-05 · Babak Hosseini, Barbara Hammer

There exist many approaches for description and recognition of unseen classes in datasets. Nevertheless, it becomes a challenging problem when we deal with multivariate time-series (MTS) (e.g., motion data), where we can…

Clustering Dictionary Learning Online Clustering Time Series+1

Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation

2022-04-29 · Jitendra K Tugnait

We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary multivariate Gaussian time series. In a time series graph, each component of the vector series is represented…

Attribute Graph Learning Time Series Time Series Analysis

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction

2023-03-16 · Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang, Yang-chih Fu 외

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analyti…

Dimensionality Reduction Representation Learning