4 research outputs found
SNAP: Stateful Network-Wide Abstractions for Packet Processing
Early programming languages for software-defined networking (SDN) were built
on top of the simple match-action paradigm offered by OpenFlow 1.0. However,
emerging hardware and software switches offer much more sophisticated support
for persistent state in the data plane, without involving a central controller.
Nevertheless, managing stateful, distributed systems efficiently and correctly
is known to be one of the most challenging programming problems. To simplify
this new SDN problem, we introduce SNAP.
SNAP offers a simpler "centralized" stateful programming model, by allowing
programmers to develop programs on top of one big switch rather than many.
These programs may contain reads and writes to global, persistent arrays, and
as a result, programmers can implement a broad range of applications, from
stateful firewalls to fine-grained traffic monitoring. The SNAP compiler
relieves programmers of having to worry about how to distribute, place, and
optimize access to these stateful arrays by doing it all for them. More
specifically, the compiler discovers read/write dependencies between arrays and
translates one-big-switch programs into an efficient internal representation
based on a novel variant of binary decision diagrams. This internal
representation is used to construct a mixed-integer linear program, which
jointly optimizes the placement of state and the routing of traffic across the
underlying physical topology. We have implemented a prototype compiler and
applied it to about 20 SNAP programs over various topologies to demonstrate our
techniques' scalability
Characterizing and Modeling Control-Plane Traffic for Mobile Core Network
In this paper, we first carry out to our knowledge the first in-depth
characterization of control-plane traffic, using a real-world control-plane
trace for 37,325 UEs sampled at a real-world LTE Mobile Core Network (MCN). Our
analysis shows that control events exhibit significant diversity in device
types and time-of-day among UEs. Second, we study whether traditional
probability distributions that have been widely adopted for modeling Internet
traffic can model the control-plane traffic originated from individual UEs. Our
analysis shows that the inter-arrival time of the control events as well as the
sojourn time in the UE states of EMM and ECM for the cellular network cannot be
modeled as Poisson processes or other traditional probability distributions. We
further show that the reasons that these models fail to capture the
control-plane traffic are due to its higher burstiness and longer tails in the
cumulative distribution than the traditional models. Third, we propose a
two-level hierarchical state-machine-based traffic model for UE clusters
derived from our adaptive clustering scheme based on the Semi-Markov Model to
capture key characteristics of mobile network control-plane traffic -- in
particular, the dependence among events generated by each UE, and the diversity
in device types and time-of-day among UEs. Finally, we show how our model can
be easily adjusted from LTE to 5G to support modeling 5G control-plane traffic,
when the sizable control-plane trace for 5G UEs becomes available to train the
adjusted model. The developed control-plane traffic generator for LTE/5G
networks is open-sourced to the research community to support high-performance
MCN architecture design R&D
Efficient Processing of Multi-connection Compressed Web Traffic
Part 2: Content ManagementInternational audienceCompressing web traffic using standard GZIP is becoming both popular and challenging due to the huge increase in wireless web devices, where bandwidth is limited. Security and other content based networking devices are required to decompress the traffic of tens of thousands concurrent connections in order to inspect the content for different signatures. The major limiting factor in this process is the high memory requirements of 32KB per connection that leads to hundreds of megabytes to gigabytes of main memory consumption. This requirement inhibits most devices from handling compressed traffic, which in turn either limits traffic compression or introduces security holes and other dysfunctionalities. In this paper we introduce new algorithms and techniques that drastically reduce this space requirement by over 80%, with only a slight increase in the time overhead, thus making real-time compressed traffic inspection a viable option for network devices