Cover Image

Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shaikh, Faraz, Reali, Gianluca, Femminella, Mauro
Format:	Preprint
Published:	2026
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Hodge-Based Framework for Service Operational Analysis in Serverless Platforms
by: Reali, Gianluca, et al.
Published: (2026)

Topological Analysis for Identifying Anomalies in Serverless Platforms
by: Reali, Gianluca, et al.
Published: (2026)

Design and Implementation of an Automated Disaster-recovery System for a Kubernetes Cluster Using LSTM
by: Kim, Ji-Beom, et al.
Published: (2024)

An SLO Driven and Cost-Aware Autoscaling Framework for Kubernetes
by: Punniyamoorthy, Vinoth, et al.
Published: (2025)

From Models to Operators: Rethinking Autoscaling Granularity for Large Generative Models
by: Cui, Xingqi, et al.
Published: (2025)

SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
by: Su, Jianchang, et al.
Published: (2026)

MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling
by: Hamzeh, Hamed, et al.
Published: (2026)

AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
by: Li, Siyuan, et al.
Published: (2024)

Taming the Memory Beast: Strategies for Reliable ML Training on Kubernetes
by: Ray, Jaideep
Published: (2024)

Predictive Autoscaling for Node.js on Kubernetes: Lower Latency, Right-Sized Capacity
by: Tymoshenko, Ivan, et al.
Published: (2026)

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
by: Sun, Ao, et al.
Published: (2024)

Federated Learning Under Temporal Drift -- Mitigating Catastrophic Forgetting via Experience Replay
by: Kokkula, Sahasra, et al.
Published: (2026)

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
by: Molina, Romina Soledad, et al.
Published: (2025)

Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
by: Sedlak, Boris, et al.
Published: (2025)

Priority Matters: Optimising Kubernetes Clusters Usage with Constraint-Based Pod Packing
by: Christensen, Henrik Daniel, et al.
Published: (2025)

Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition
by: Zhao, Yuwen, et al.
Published: (2024)

Scaling Deep Learning Research with Kubernetes on the NRP Nautilus HyperCluster
by: Hurt, J. Alex, et al.
Published: (2024)

Self-adaptive, Requirements-driven Autoscaling of Microservices
by: Nunes, João Paulo Karol Santos, et al.
Published: (2024)

Proactive and Reactive Autoscaling Techniques for Edge Computing
by: Gupta, Suhrid, et al.
Published: (2025)

Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data
by: Li, Bangyu, et al.
Published: (2024)

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
by: Khan, Ahmad Faraz, et al.
Published: (2022)

Is Flash Attention Stable?
by: Golden, Alicia, et al.
Published: (2024)

LSRAM: A Lightweight Autoscaling and SLO Resource Allocation Framework for Microservices Based on Gradient Descent
by: Hu, Kan, et al.
Published: (2024)

Convergence Analysis of Decentralized ASGD
by: Tosi, Mauro DL, et al.
Published: (2023)

Federated Temporal Graph Clustering
by: Zhou, Zihao, et al.
Published: (2024)

AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
by: Hamzeh, Hamed
Published: (2026)

Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
by: Xu, Zheng, et al.
Published: (2024)

Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors in the Field
by: Boixaderas, Isaac, et al.
Published: (2024)

STHFL: Spatio-Temporal Heterogeneous Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
by: Yang, Qiantao, et al.
Published: (2026)

Fused3S: Fast Sparse Attention on Tensor Cores
by: Li, Zitong, et al.
Published: (2025)

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025)

Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
by: Zhou, Wenxuan, et al.
Published: (2024)

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
by: Armah, Eugene, et al.
Published: (2025)

Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems
by: Aach, Marcel, et al.
Published: (2024)

Kubernetes in Action: Exploring the Performance of Kubernetes Distributions in the Cloud
by: Aqasizade, Hossein, et al.
Published: (2024)

RollPacker: Mitigating Long-Tail Rollouts for Fast, Synchronous RL Post-Training
by: Gao, Wei, et al.
Published: (2025)

A Dynamic Weighting Strategy to Mitigate Worker Node Failure in Distributed Deep Learning
by: Xu, Yuesheng, et al.
Published: (2024)

Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2025)

FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel
by: Yan, Ran, et al.
Published: (2025)