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

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC digital library.