Cloud Computing

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Cloud computing examines how to build and operate shared infrastructure, from virtual machines and Kubernetes clusters to serverless platforms and geo-distributed data centers. Common approaches include predictive autoscaling, reinforcement-learning schedulers, workload forecasting, and graph-based monitoring and root-cause analysis, evaluated on production traces, cloud benchmarks, and fault-injection tests. A central goal is balancing latency, cost, energy use, reliability, and carbon emissions as workloads and hardware change. Current work also explores LLM-based agents for cloud operations, while safe execution, reliable evaluation, and generalization under workload and infrastructure shifts remain important challenges.

Latest papers

48 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design
Oct 1, 2026Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-PerezAutomated Heuristic Design LLM Agent Orchestration
NEC Laboratories Europe · i2CAT Foundation and ICREA
About the Influence of Workflow Topology on Task Intensity Prediction through Graph Learning
Sep 30, 2026Max Otto, Haci Ismail Aslan, Joel Witzke +2Graph Neural Networks Graph Representation Learning
Dept. Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany
Argus: A Real-EKS Study of When Predicting Spot Interruptions Beats Simple Checkpointing
Sep 30, 2026Angshuman Chakravertty, MD RayyanFault-Tolerant Distributed Training Cloud Computing
SVKM’s NMIMS (Deemed to be University)
FDE-Bench: Evaluating LLM Agents for Deployment Environment Configuration
Sep 23, 2026Weihang Ding, Junfei Zhan, Yueting Li +1Software Engineering LLM Agent Evaluation
University of California, Berkeley · Imperial College London · The Hong Kong University of Science and Technology (Guangzhou)
TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
Sep 20, 2026Rohit Patel, Susil Kumar Mohanty, Jeenal ChaudharyLLM Inference Parallel LLM Inference
Department of Computer Science and Engineering Indian Institute of Technology Jodhpur, Jodhpur, India
MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling
Sep 11, 2026Tiangang Li, Shi Ying, Xiangbo Tian +2Cross-Attention Cloud Computing
School of Computer Science, Wuhan University, Wuhan 430072, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds
Sep 3, 2026Ashir Javeed, Anton Borg, Håkan Grahn +3Time Series Forecasting Cloud Computing
Department of Computer Science, Blekinge Institute of Technology, 371 79, Sweden · Ericsson AB, 371 33 Karlskrona, Sweden
Automated Synthesis of Cloud Emulators
Aug 24, 2026Archit Bhatnagar, Zhenning Yang, Sarah McClure +3Cloud Computing Program Synthesis
University of Michigan · University of California, Berkeley · The University of Hong Kong
InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
Aug 13, 2026Nicoletta Tsiopani, Moysis Symeonides, George Pallis +1Energy-Efficient ML LLM Inference Serving
Department of Computer Science University of Cyprus
The Abstention Protocol: RCA for Clos Fabrics
Aug 11, 2026Madhava Gaikwad, Deepak PandeyRoot Cause Analysis Cloud Computing
Independent · Microsoft
AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
Aug 3, 2026Qiushi Lin, Chaojie Zhang, Íñigo Goiri +3Policy Optimization Cloud Computing
The University of Texas at Austin · Microsoft Azure
Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More
Aug 1, 2026Jake Watson, Sadaf R Alam, Christopher Woods +6High-Performance Computing Cloud Computing
Bristol Centre for Supercomputing University of Bristol Bristol, United Kingdom · HPE HPC/AI EMEA Research Lab Hewlett Packard Enterprise Bristol, United Kingdom · HPE HPC/AI EMEA Research Lab Hewlett Packard Enterprise Zürich, Switzerland
Secure AI Watermarking Framework for IP Protection in Multi-Tenant Cloud Platforms
Aug 1, 2026M Anjan Kumar, Kishor Kumar Gajula, Ch PrathimaDataset Watermarking Cloud Computing
Department of Computer Science and Engineering , Viswam Engineering College, Madanapalle, Andhra Pradesh , India · Department of Computer Science and Engineering , Mother Theresa Institute Of Engineering and Technology, Peddapalli, Telangana, India · Department of Computer Science and Engineering , Mohan Babu University, Tirupathi, Andhra Pradesh ; India
LayoutBench: Performance Benchmarking of Cloud Storage Layouts for Multimedia Data
Jul 30, 2026Debopam Sanyal, Hongjie Chen, Alexey Tumanov +1Benchmark Design Cloud Computing
Georgia Tech · Dolby Labs
Towards a Systems Foundation for Agentic Cloud Management
Jul 28, 2026Minghao Li, Ziqian Liu, Ziyu Mao +5Multi-Agent Orchestration AI Agent Safety
The University of Hong Kong · Microsoft · University of Illinois Urbana-Champaign
SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Clouds
Jul 24, 2026Anmol Chaudhary, Rahul MishraCloud Computing
Department of Electronics and Computer Engineering, NIAMT Ranchi · National Institute of Advanced Manufacturing Technology Ranchi, India
ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring
Jul 20, 2026Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy +3Cloud Computing Time-Series Anomaly Detection
Department of Computer Science, Gran Sasso Science Institute, L’Aquila, Italy · Department of Computer Science, Toronto Metropolitan University, Toronto, Canada · Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway +1
MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
Jul 9, 2026Xu Zhou, Haoyang Chen, Xinyu LeiCloud Computing Privacy-Preserving ML
Department of Computer Science, Michigan Technological University, Houghton, MI, USA
Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
Jul 2, 2026Junyan Tan, Haoran Lin, Siyuan Guo +4LLM-Based Program Synthesis LLM-Guided RL
Department of Computer Science Zhejiang University
CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift
Jun 30, 2026Jack Bell, Giacomo Carfi, Gerlando Gramaglia +3AI-Assisted Decision Making Cloud Computing
Department of Computer Science University of Pisa Pisa, Italy · Independent Researcher Sintra Consulting Limited Arezzo, Italy · Department of AI, Data and Decision Science Luiss University Rome, Italy
A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis
Jun 22, 2026Ye Yuan, Song Wang, Hongxun Zhou +2Time Series Forecasting Cloud Computing
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers
Jun 19, 2026Zihan Yu, Xianling Zeng, Zhiming Xue +2Time Series Forecasting Cloud Computing
College of Professional Studies Northeastern University Boston, USA · College of Engineering Northeastern University Boston, USA · Khoury College of Computer Sciences Northeastern University Boston, USA
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
Jun 11, 2026Fabien Chraim, Dominik Janzing, John EvansTime-Series Causal Discovery Cloud Computing
Amazon Web Services
AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning
Jun 8, 2026Bojie Rong, Zheyu Shen, Qiaoping Wang +7Cloud Computing LLM Agent Training
Alibaba Cloud China
STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms
May 25, 2026Ahmed Abdulaal, Maruf Aytekin, Thilaga kumaran Srinivasan +1Multivariate Time Series Forecasting Cloud Computing
Walmart Global Tech Sunnyvale, California, USA
Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
May 24, 2026Hangxuan Li, Renjun Jia, Xuezhang Wu +3LLM-Based Program Synthesis Cloud Computing
Alibaba Cloud Computing Co. Ltd, Hangzhou, China · School of Computer Science, Fudan University, Shanghai, China · School of Computer Science and Technology, Tongji University, Shanghai, China +1
A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
May 18, 2026Seamus L. Anderson, Hadrien A. R. Devillepoix, Lewis Lakerink +30Cloud Computing
NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States. · Space Science and Technology Centre, Curtin University, GPO Box U1987, Perth WA 6102, Australia. · International Centre for Radio Astronomy Research, Curtin University, GPO Box U1987, Perth WA 6102, Australia. +12
Indirect Computing Model with Indirect Formal Method
May 13, 2026Xiaohui ZouCloud Computing
Institute for Higher Education, China University of Geosciences (Beijing), Beijing, China · Institute of Synergistic Cultural Gene Engineering, Tsinghua Science Park, Zhuhai, China · Sino-US Project: UC Berkeley Searle Research Bilingual Information Processing Group
Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
May 5, 2026Yixuan Mei, Zikun Li, Zixuan Chen +5LLM Serving Cost-Aware Inference
Carnegie Mellon University · Peking University
Intelligent Autonomous Orchestration for Distributed Cloud Resources using Complex-Stability Analysis
May 2, 2026Gopal Krishna Shyam, Priyanka BhartiCloud Computing
Department of Computer Science & Engineering Presidency University, Bengaluru, India · Department of Computer Science & Engineering REVA University, Bengaluru, India

Oct 1, 2026J/K move · Enter open · S save

TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design

Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez

NEC Laboratories Europe · i2CAT Foundation and ICREA

Automated Heuristic Design LLM Agent Orchestration Cloud Computing Online Resource Allocation

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Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in research, industrial deployments still rely on hand-written rules that operators can read, audit, and execute within tight latency budgets. LLM-based Automatic Heuristic Design (AHD) promises to automate writing such rules. However, existing AHD frameworks were developed for combinatorial problems fully specified to the LLM, and they learn only from a scalar fitness score. In real systems, the behaviour that determines a good heuristic, such as processor speeds or power consumption, is unknown a priori: the score reveals which heuristic performs better, but not why. This missing information is recorded in the system logs that every evaluation produces. Exploiting it is non-trivial: logs are massive and noisy, the relevant signals depend on the objective, and their content and format vary across hardware and software stacks, so they can neither be fed to an LLM as is nor processed by a fixed parser. We propose TRACE, which couples an evolutionary AHD loop with an agentic knowledge-extraction workflow. A Reasoner agent analyzes the log schema in light of the objective and formulates hypotheses about the system dynamics; a Coder agent writes and executes schema-specific code to test them, producing insights or executable tools for the evolved heuristics. We evaluate TRACE on a synthetic cloud benchmark and a 5G vRAN scenario built from industrial testbed measurements and operational traffic traces. TRACE consistently outperforms state-of-the-art AHD methods in resource assignment problems and yields more auditable heuristics at under 2% overhead.