Application-driven network management with ProtoRINA

Traditional network management is tied to the TCP/IP architecture, thus it inherits its many limitations, e.g., static management and one-size-fits-all structure. Additionally there is no unified framework for application management, and service (application) providers have to rely on their own ad-hoc mechanisms to manage their application services. The Recursive InterNetwork Architecture (RINA) is our solution to achieve better network management. RINA provides a unified framework for application-driven network management along with built-in mechanisms (including registration, authentication, enrollment, addressing, etc.), and it allows the dynamic formation of secure communication containers for service providers in support of various requirements. In this paper, we focus on how application-driven network management can be achieved over the GENI testbed using ProtoRINA, a user-space prototype of RINA. We demonstrate how video can be efficiently multicast to many clients on demand by dynamically creating a delivery tree. Under RINA, multicast can be enabled through a secure communication container that is dynamically formed to support video transport either through application proxies or via relay IPC processes. Experimental results over the GENI testbed show that application-driven network management enabled by ProtoRINA can achieve better network and application performance.National Science Foundation (NSF grant CNS-0963974)

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table