Workshop Program Big Data and Cloud Performance

7th Workshop on Big Data and Cloud Performance (DCPerf 2017)
Monday, 1 May 2017 ● 08:30 – 12:00 ● Room: Georgia 9
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 Big Data and Cloud Performance (DCPerf) workshop aims 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.
Program Chairs:
Robert Birke (IBM Research Zurich, Switzerland)
George Kesidis (Pennsylvania State University, USA)
08:30 – 08:35
Robert Birke and George Kesidis
08:35 – 10:00
Session I: Cloud Systems and Big Data
A Simple, Cost-effective Addressing and Routing Architecture for Fat-tree Based Datacenter Networks
Aqun Zhao (Beijing Jiaotong University, China and University of Victoria, Canada), Zhiyu Liu (Beijing Jiaotong University, China), Jianping Pan (University of Victoria, Canada) and Mangui Liang (Beijing Jiaotong University, China)
On Power Consumption Profiles for Data Intensive Workloads in Virtualized Hadoop Clusters
Basit Qureshi (Prince Sultan University, Saudi Arabia), Sultan Alwehaibi (Prince Sultan University, Saudi Arabia), Anis Koubâa (Polytechnic Institute of Porto, Portugal and Prince Sultan University, Saudi Arabia)
Efficient and Fair Scheduling of Placement Constrained Threads on Heterogeneous Multi-Processors
Jalal Khamse-Ashari (Carleton University, Canada), George Kesidis (Pennsylvania State University, USA), Ioannis Lambadaris (Carleton University, Canada), Bhuvan Urgaonkar (Pennsylvania State University, USA) and Yiqiang Zhao (Carleton University, Canada)
Simulated Annealing for Edge Partitioning
Hlib Mykhailenko (Université Côte d’Azur Inria, France), Giovanni Neglia (Université Côte d’Azur Inria, France) and Fabrice Huet (Université Côte d’Azur CNRS, France)
10:30 – 11:15
Umakishore Ramachandran (Georgia Institute of Technology, USA)
Large-scale Situational Awareness with Camera Networks and Multimodal Sensing
11:15 – 12:00
Session II: Big Data and Cloud performance
Application Interference Analysis: Towards Energy-efficient Workload Management on Heterogeneous Micro-Server Architectures
Markus Hahnel (Technische Universität Dresden, Germany), Frehiwot Melak Arega (Technische Universität Dresden, Germany), Waltenegus Dargie (Technische Universität Dresden, Germany), Robert Khasanov (Technische Universität Dresden, Germany), Jeronimo Castrillon (Technische Universität Dresden, Germany)
Effect of Optimizing Java Deployment Artifacts on AWS Lambda
Hussachai Puripunpinyo (Workday and Oklahoma State University, USA) and M.H. Samadzadeh (Oklahoma State University, USA)