68,304 research outputs found
P4CEP: Towards In-Network Complex Event Processing
In-network computing using programmable networking hardware is a strong trend
in networking that promises to reduce latency and consumption of server
resources through offloading to network elements (programmable switches and
smart NICs). In particular, the data plane programming language P4 together
with powerful P4 networking hardware has spawned projects offloading services
into the network, e.g., consensus services or caching services. In this paper,
we present a novel case for in-network computing, namely, Complex Event
Processing (CEP). CEP processes streams of basic events, e.g., stemming from
networked sensors, into meaningful complex events. Traditionally, CEP
processing has been performed on servers or overlay networks. However, we argue
in this paper that CEP is a good candidate for in-network computing along the
communication path avoiding detouring streams to distant servers to minimize
communication latency while also exploiting processing capabilities of novel
networking hardware. We show that it is feasible to express CEP operations in
P4 and also present a tool to compile CEP operations, formulated in our P4CEP
rule specification language, to P4 code. Moreover, we identify challenges and
problems that we have encountered to show future research directions for
implementing full-fledged in-network CEP systems.Comment: 6 pages. Author's versio
A Self-Organized Method for Computing the Epidemic Threshold in Computer Networks
In many cases, tainted information in a computer network can spread in a way
similar to an epidemics in the human world. On the other had, information
processing paths are often redundant, so a single infection occurrence can be
easily "reabsorbed". Randomly checking the information with a central server is
equivalent to lowering the infection probability but with a certain cost (for
instance processing time), so it is important to quickly evaluate the epidemic
threshold for each node. We present a method for getting such information
without resorting to repeated simulations. As for human epidemics, the local
information about the infection level (risk perception) can be an important
factor, and we show that our method can be applied to this case, too. Finally,
when the process to be monitored is more complex and includes "disruptive
interference", one has to use actual simulations, which however can be carried
out "in parallel" for many possible infection probabilities
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