Tracks
1. Architecture
*Cloud Infrastructure as a Service*Cloud Platform as a Service
*Cloud federation and hybrid cloud infrastructure
*Programming models and systems/tools
*Green data center
*Networking technologies for data center
*Cloud system design with FPGA, GPU, APU
*Monitoring, management and maintenance
*Economic and business models
*Dynamic resource provisioning
2. MapReduce
*Performance characterization and optimization*MapReduce on multi-core, GPU
*MapReduce on hybrid distributed environments
*MapReduce on opportunistic / heterogeneous computing systems
*Extension of the MapReduce programming model
*Debugging and simulation of MapReduce systems
*Data-intensive applications using MapReduce
*Optimized storage for MapReduce applications
*Fault-tolerance & Self-* capabilities
3. Security and Privacy
*Accountability*Audit in clouds
*Authentication and authorization
*Cryptographic primitives
*Reliability and availability
*Trust and credential management
*Usability and security
*Security and privacy in clouds
*Legacy systems migration
*Cloud Integrity and Binding Issues
4. Services and Applications
*Cloud Service Composition*Query and discovery models for cloud services
*Trust and Security in cloud services
*Change management in cloud services
*Organization models of cloud services
*Innovative cloud applications and experiences
*Business process and workflow management
*Service-Oriented Architecture in clouds
5. Virtualization
*Server, storage, network virtualization*Resource monitoring
*Virtual desktop
*Resilience, fault tolerance
*Modeling and performance evaluation
*Security aspects
*Enabling disaster recovery, job migration
*Energy efficient issues
6. HPC on Cloud
*Load balancing for HPC clouds*Middleware framework for HPC clouds
*Scalable scheduling for HPC clouds
*HPC as a Service
*Performance Modeling and Management
*Programming models for HPC clouds
*HPC cloud applications
*Optimal cloud deployment for HPC
7. Big Data:
*Machine learning*Data mining
*Approximate and scalable statistical methods
*Graph algorithms
*Querying and search
*Data Lifecycle Management for Big Data (sources, cleansing, federation, preservation, privacy, etc.)
*Frameworks, tools and their composition
*Storage and analytic architectures
*Performance and debugging
*Hardware optimizations for Big Data (multi-core, GPU, networking, etc.)
*Data Flow management and scheduling