26,829 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
Counter-intuitive throughput behaviors in networks under end-to-end control
It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if alpha is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology
Towards a Queueing-Based Framework for In-Network Function Computation
We seek to develop network algorithms for function computation in sensor
networks. Specifically, we want dynamic joint aggregation, routing, and
scheduling algorithms that have analytically provable performance benefits due
to in-network computation as compared to simple data forwarding. To this end,
we define a class of functions, the Fully-Multiplexible functions, which
includes several functions such as parity, MAX, and k th -order statistics. For
such functions we exactly characterize the maximum achievable refresh rate of
the network in terms of an underlying graph primitive, the min-mincut. In
acyclic wireline networks, we show that the maximum refresh rate is achievable
by a simple algorithm that is dynamic, distributed, and only dependent on local
information. In the case of wireless networks, we provide a MaxWeight-like
algorithm with dynamic flow splitting, which is shown to be throughput-optimal
Design of an Efficient Interconnection Network of Temperature Sensors
Temperature has become a first class design constraint because high temperatures adversely affect circuit reliability, static power and degrade the performance. In this scenario, thermal characterization of ICs and on-chip temperature monitoring represent fundamental tasks in electronic design. In this work, we analyze the features that an interconnection network of temperature sensors must fulfill. Departing from the network topology, we continue with the proposal of a very light-weight network architecture based on digitalization resource sharing. Our proposal supposes a 16% improvement in area and power consumption compared to traditional approache
Jointly Optimal Routing and Caching for Arbitrary Network Topologies
We study a problem of fundamental importance to ICNs, namely, minimizing
routing costs by jointly optimizing caching and routing decisions over an
arbitrary network topology. We consider both source routing and hop-by-hop
routing settings. The respective offline problems are NP-hard. Nevertheless, we
show that there exist polynomial time approximation algorithms producing
solutions within a constant approximation from the optimal. We also produce
distributed, adaptive algorithms with the same approximation guarantees. We
simulate our adaptive algorithms over a broad array of different topologies.
Our algorithms reduce routing costs by several orders of magnitude compared to
prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing
and Caching for Arbitrary Network Topologies", appearing in the 4th ACM
Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28,
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