Welcome to the Laboratory of Computer Architecture and Network Systems (CANS) at the University of Massachusetts Lowell!

The research at CANS lab, led by Prof. Yan Luo, focuses on computer architecture innovations for high performance computing and network systems. The research topics range from chip multiprocessor (CMP) architecture, to network content processing engines, and to network virtualization.

Ongoing Research Projects

OpenFlow Switching is a newly proposed standard allowing for testing new network architecture and protocols in a production network. We leverage the programmability and high performance of Network Processors (NPs) to accelerate the data path of PC-based OpenFlow reference design.
We design and implement a prototype of programmable edge node with hybrid multi-core processors for aggregating computing/storage sites within GENI. We are one of the 29 teams contributing to GENI Spiral 1.
Programmability in network equipments enables the adaptability of network protocols and optimization of network performance. We are building a programmable network infrastructure, consisting of programmable routers, software defined radio devices and spectrum analyzers, for the research and education of advanced network technologies.
  • Performance Evaluation and Acceleration of an Open Source VPN System on Tolapai Platform, PI: Yan Luo, Intel, 2008-2009
We address the issue of performance inadequacy of Virtual Private Network (VPN) applications. The computational complexity of VPN applications brings challenges for high performance secure networking. We plan to improve the VPN performance with Tolapai platform, taking advantage of Tolapai’s unique converged IA/IXA architecture and on-chip accelerators.

* Design and Performance Evaluation of High Speed Deep Packet Inspection Systems, PI: Yan Luo, 2007-2008 ->This research aims to exploit inherent parallelism in regular expression matching algorithms, develop compression schemes to minimize state machine storage, implement reg-ex matching on programmable architectures and evaluate their performance. The long-term goal of this research project is to design high-performance regular expression matching systems that can greatly improve the performance of DPI and other content processing applications.

Past Projects

Sponsors

We acknowledge the generous supports from

NSF logo Intel logo Altera logo Xilinx logo BBN logo

Both governmental agencies and industrial partners are welcome to contact the director of the lab, Prof. Yan Luo, for information, collaboration or sponsored research.


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