Call For Papers
Overview
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 and environmental sensing, and IoT monitoring, present a unique class of challenges in the Cloud. In addition, the high volume of mixed workloads and the diversity of services offered render the performance optimization of data centers even more challenging. Moreover, important optimization criteria, such as scalability, reliability, manageability, power efficiency, area density, and operating costs, are often conflicting. The increasing mobility of users across geographically distributed areas adds another dimension to optimizing big data and cloud applications.
The goal of this workshop is to promote a community-wide discussion to identify suitable strategies to enable effective and scalable performance optimizations. We are looking for papers that present new techniques, introduce new methodologies, propose new research directions, or discuss strategies for resolving open performance problems for hosting big data analytics in the cloud.
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
- Power/energy management
- Reliability/dependability
- Performance evaluation/modeling
- Big data in the cloud
- Intra/Inter communication
- Network protocols
- Security
- Real-time analytics