Massive modern data centers consisting of tens of thousands of servers, such as Microsoft's Azure platform, Google's App engine, and Amazon's EC2 platform, have emerged to form the backbone of a variety of powerful distributed computing frameworks. Meanwhile, many companies are moving their services such as e-commerce, scientific computing and social networking to the cloud, due to its ability to offer scalable and elastic computing and storage services. In such large-scale distributed computing frameworks, efficient communication is often required among huge datasets stored in tens of thousands of servers across a data center. The data center network (DCN) that connects different servers would become the bottleneck of the system, and its performance is essential to the successful operation of a data center. On the other hand, many online applications and back-end infrastructural computations hosted by data centers require one-to-many or multicast communication from a server to a group of servers. This research aims to investigate the fundamental and challenging issues faced in building cost-efficient multicast data center networks with guaranteed performance. As cloud computing is penetrating into all aspects of society, this research will have a profound impact on society and help change the world.
The objective of this research is to design cost-efficient multicast fat-tree data center networks (DCNs) with guaranteed performance through exploring some unique novel features and techniques in data centers. The project combines theoretical analysis, algorithm design, network optimization, simulation, and prototyping techniques to provide a comprehensive working solution that enables high performance multicast in fat-tree DCNs. More specifically, the research focuses on following closely coupled issues: (1) cost-efficient provisioning of fat-tree DCNs to deploy guaranteed-bandwidth multicast by exploring server redundancy and link oversubscription in data centers; (2) leveraging the OpenFlow framework to develop practical multicast scheduling algorithms that ensure traffic load balance and efficient network utilization under volatile data center traffic; (3) employing virtual machine technology to offer multicast with differentiated bandwidth guarantees tailored to application-specific demand; (4) conducting a comprehensive performance evaluation through extensive simulations and implementation of proposed schemes in a network prototype. This research hopes to impact fundamental design principles of high performance multicast fat-tree DCNs. The outcome of this research has the potential to boost the performance of cloud computing applications currently hosted in data centers, and to facilitate cloud adoption for future applications that rely on group communication and demand predictable high bandwidth. A project goal is to train graduate students and promote the participation of female engineering students. The important findings of this project are to be disseminated to the research community by way of conferences, journals and web site access.
