CCIOT 2016 consists of several tracks, including, but not limited to, the following specific topics:

I. Cloud computing

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

•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

•GraphAlgorithms                                                                                                           

•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.)