7th International Conference on Cloud and Big Data (CLBD 2026)

August 15 ~ 16, 2026, Melbourne, Australia

Scope

The 7th International Conference on Cloud and Big Data (CLBD 2026) serves as a premier global forum for presenting innovative ideas, advanced research results, practical developments, and emerging trends in the rapidly evolving fields of Cloud Computing and Big Data Systems. As cloud platforms, large scale data infrastructures, and AI driven analytics continue to reshape modern computing, CLBD 2026 provides a vital venue for researchers, practitioners, engineers, and industry experts to exchange knowledge, discuss challenges, and explore breakthroughs that define the next generation of intelligent, scalable, and resilient data centric systems.

 
 
The conference aims to foster collaboration between academia and industry by showcasing cutting edge research, real world deployments, and visionary perspectives that address the latest issues in cloud architectures, distributed systems, big data analytics, AI infrastructure, and emerging intelligent cloud services. With the rapid rise of LLMs, RAG systems, cloud native AI pipelines, multi cloud platforms, edge intelligence, and carbon aware cloud operations, CLBD 2026 embraces a broadened and modernized scope aligned with the top conferences in the field.

Authors are invited to contribute original research papers, survey articles, technical reports, and industrial case studies that demonstrate significant advances in Cloud Computing, Big Data, Distributed Systems, AI/ML Infrastructure, and Data Engineering. Topics of interest include, but are not limited to, the following:

 Topics of Interest

Cloud Computing Systems and Architectures
  • Cloud native architectures and microservices
  • Serverless computing and Function as a Service (FaaS)
  • Containerization, Kubernetes, WASM and microVMs
  • Disaggregated storage and compute (CXL, NVMe oF, remote memory)
  • Cloud networking, SDN, NFV and high performance interconnects
  • Multi cloud, hybrid cloud and cross cloud federation
  • Cloud reliability engineering (SRE), fault tolerance and chaos engineering
  • Cloud observability, telemetry and distributed tracing
  • Cloud performance engineering, benchmarking and SLA enforcement
  • Cloud cost optimization and FinOps
  • Energy efficient and carbon aware cloud computing
Cloud Platforms, Infrastructure and Data Services
  • Cloud storage systems, distributed file systems and object stores
  • Cloud databases (serverless DBs, NewSQL, NoSQL, HTAP)
  • Cloud data lakes, lakehouses and unified analytics platforms
  • Cloud native ETL/ELT pipelines and dataflow systems
  • Virtual compute clusters, cluster scheduling and resource orchestration
  • Confidential computing and secure enclaves (TEE)
  • Cloud governance, compliance and policy automation
  • Cloud security architectures and zero trust cloud systems
Edge, Mobile and IoT Cloud Integration
  • Edge cloud continuum and distributed intelligence
  • Mobile cloud offloading and edge acceleration
  • IoT data pipelines and real time ingestion
  • Geo distributed cloud systems and global data consistency
  • 5G/6G enabled cloud applications
  • Cloud robotics and edge autonomy
AI, Machine Learning and Cloud AI Infrastructure
  • Distributed ML training on cloud platforms
  • GPU/TPU/DPU scheduling and acceleration for AI workloads
  • Cloud systems for foundation model training
  • Mixture of Experts (MoE) training and serving
  • Memory efficient AI (quantization, pruning, distillation)
  • Cloud native MLOps and AIOps
  • Feature stores, online ML and real time inference
  • Serverless AI systems and stateless ML pipelines
  • AI driven autoscaling and resource prediction
LLM, RAG and Intelligent Cloud Retrieval Systems
  • Retrieval augmented generation (RAG) on cloud platforms
  • Vector databases and embedding management at cloud scale
  • Hybrid retrieval (vector + graph + keyword)
  • High throughput retrieval for LLM inference
  • Cloud hosted LLM serving, optimization and scaling
  • RAG orchestrated workflows and intelligent automation
  • Privacy preserving RAG and secure retrieval pipelines
Big Data Systems, Analytics and Processing
  • Big Data platforms, engines and distributed analytics
  • Scalable algorithms, models and dataflow computation
  • Large scale graph analytics and knowledge extraction
  • Big Data security, privacy and trust
  • Big Data applications (bioinformatics, finance, multimedia, healthcare)
  • Big Data tools, frameworks and ecosystems
  • Social media analytics and large scale graph mining
  • Big Data for scientific computing and simulation
  • RAG optimized Big Data systems
  • Foundation model data engineering
Data Engineering, Data Management and Observability
  • Data integration, cleaning and transformation at scale
  • Metadata management, data lineage and data observability
  • Data quality monitoring and validation
  • Data governance, compliance and policy automation
  • Data contracts for cloud and ML pipelines
  • Schema evolution and cloud native data modeling
  • Data centric AI and automated labeling pipelines
Streaming, Real Time and Event Driven Cloud Systems
  • Real time analytics and event processing
  • High velocity ingestion and low latency pipelines
  • Stream processing engines (Flink, Kafka, Pulsar)
  • Real time ML inference and online feature computation
  • Event driven architectures and cloud orchestration
  • Real time digital twin analytics
Security, Privacy and Trust in Cloud and Big Data
  • Privacy preserving computation (DP, MPC, FHE)
  • Secure enclaves (TEE) and confidential computing
  • Federated analytics and privacy preserving data lakes
  • Secure data sharing, auditing and compliance automation
  • AI safety, alignment and cloud level guardrail systems
  • Red teaming automation and adversarial testing for cloud AI
Distributed, Federated and Decentralized Systems
  • Federated cloud systems and multi tenant architectures
  • Blockchain and decentralized data systems
  • Distributed ledger technologies for cloud applications
  • Secure data exchange and data marketplaces
  • Geo distributed data systems and global transaction processing Cloud Applications and Industry Use Cases
  • Cloud applications in finance, healthcare, manufacturing and smart cities
  • Cloud enabled digital twins and simulation systems
  • Cloud robotics and autonomous systems
  • Cloud based scientific computing and HPC
  • Generative media pipelines (video, audio, multimodal)
Emerging Topics in Cloud and Big Data
  • Multi agent cloud systems and agentic workflows
  • Cloud systems for generative AI beyond text (video, multimodal)
  • Cloud native RAG orchestration and intelligent automation
  • CXL based disaggregated memory and compute
  • Tiered storage optimization and erasure coding at scale
  • Global inference systems for LLMs
  • Renewable integrated cloud operations
Paper Submission

Authors are invited to submit papers through the Submission System by May 10, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS&IT) series (Confirmed).

Selected papers from CLBD 2026, after further revisions, will be published in the special issue of the following journals
Important Dates

·         Submission Deadline: May 10, 2026
·         Authors Notification: June 25, 2026
·         Registration & Camera-Ready Paper Due: July 02, 2026

 

Contact Us: clbd@dms2026.org