DCPERF 2017 - Workshop on BIG DATA AND CLOUD PERFORMANCE

Cloud data centers are the backbone infrastructure for tomorrow's information technology. Their advantages are efficient resource provisioning and low operational costs for supporting a wide range of computing needs, be it in business, scientific or mobile/pervasive environments. Because of the rapid growth in user-defined and user-generated applications and content, the range of services provided at data centers will expand tremendously and unpredictably. Particularly, big data applications and services, e.g., social, environmental sensing and IoT monitoring, present a unique class of challenges in Cloud. The high volume of mixed workloads and the diversity of services offered render the performance optimization of data centers ever more challenging. Moreover, important optimization criteria, such as scalability, reliability, manageability, power efficiency, area density, and operating costs, are often conflicting to some extent and require trade-off. In addition, the increasing mobility of users across geographically distributed areas adds another dimension to optimizing big data and cloud performance.
The goal of this workshop is to promote a community-wide discussion to find and identify suitable strategies to enable effective and scalable performance optimizations. We are looking for papers that present new techniques, introduce new theory and methodologies, propose new research directions, or discuss strategies for resolving open performance problems on big data in clouds.
Topics of interest
Topics of interest include (but are not limited to):
Big Data applications and services
Emerging IoT applications
Data flow management
Processing platforms
Empirical studies
Cloud systems
Novel architectures
Resource allocation
Content distribution
Evaluation/modeling methodology
Big Data and Cloud performance
Cost/pricing design
Energy management
Reliability
Performance evaluation/modelingy
Big Data in Cloud
Intra/inter communication
Network protocols
Security
Real time analytics