What is MAX GENI Aggregate?

The Mid-Atlantic Network GENI Facility for Research, Experimentation, and Development is an experimental fiber-based regional network in the Washington DC metro area. MAX GENI is led by the Mid-Atlantic Crossroads (MAX) at the University of Maryland. It is funded by the NSF GENI (Global Environment for Network Innovations) program as part of Cluster B in Spiral 3.

The MAX Aggregate goal is to provide the GENI community with access to a regional optical network consisting of wavelength-selectable switches, 10Gbps Ethernet switches, and virtual machines. To accomplish this, MAX GENI has adapted and extended the NSF-funded DRAGON network for use by the GENI community. The DRAGON network provides end-to-end dynamic circuit provisioning via a standardized Web Services interface through the use of a distributed GMPLS control plane — ensuring deterministic, high-speed performance over dedicated network resources. MAX GENI has leveraged the DRAGON network infrastructure (and related technologies) by adding server virtualization capabilities at the edges of the network and programmable network hardware at two core switching nodes.

MAX GENI also has the capability to connect researchers in the Mid-Atlantic region to the rest of the GENI community via its connection to the private, high-speed Layer 2 backbone provided by Internet2 and the ProtoGENI project.

For additional details about the capabilities that MANFRED has to offer, please see the Substrate page.

If you are involved with the GENI community as a researcher, and would like to gain access to this facility, please refer to the Request Access page for detailed instructions on how to get started.

If you are located in the Washington DC metro area, and are interested in physically connecting to the network in order to access other GENI resources, please visit the Connecting page.

Acknowledgments

We are extremely grateful to BBN Technologies and the National Science Foundation (NSF) for supporting the MANFRED project. This material is based upon work supported by the National Science Foundation under Grants No. 0714770. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of BBN Technologies or the National Science Foundation.