1,109 research outputs found
Lying Your Way to Better Traffic Engineering
To optimize the flow of traffic in IP networks, operators do traffic
engineering (TE), i.e., tune routing-protocol parameters in response to traffic
demands. TE in IP networks typically involves configuring static link weights
and splitting traffic between the resulting shortest-paths via the
Equal-Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously
cumbersome and indirect means for optimizing traffic flow, often leading to
poor network performance. Also, obtaining accurate knowledge of traffic demands
as the input to TE is elusive, and traffic conditions can be highly variable,
further complicating TE. We leverage recently proposed schemes for increasing
ECMP's expressiveness via carefully disseminated bogus information ("lies") to
design COYOTE, a readily deployable TE scheme for robust and efficient network
utilization. COYOTE leverages new algorithmic ideas to configure (static)
traffic splitting ratios that are optimized with respect to all (even
adversarially chosen) traffic scenarios within the operator's "uncertainty
bounds". Our experimental analyses show that COYOTE significantly outperforms
today's prevalent TE schemes in a manner that is robust to traffic uncertainty
and variation. We discuss experiments with a prototype implementation of
COYOTE
A Performance Analysis of Point-to-Point Routing along a Directed Acyclic Graph in Low Power and Lossy Networks
International audienceThe ROLL working group at IETF is currently de- signing RPL, the Routing Protocol for Low power and lossy networks. RPL is based on organizing the nodes in a network in one or more directed acyclic graphs (DAGs) rooted at the popular/default destinations. The DAG structure naturally supports multipoint-to-point routing towards the DAG root and point-to-multipoint routing from the root towards the nodes. The point- to-point (P2P) routing mechanism specified in RPL requires a packet to travel upwards along the DAG until it is turned downwards by a node that knows the downward route along the DAG to the packet's destination. This up and down routing along the DAG may result in significantly suboptimal P2P routes. In this paper, we quantify the difference in terms of routing cost between DAG-based P2P routes and the shortest (or minimum cost) P2P routes available in a sample network topology and make the case for a better P2P routing solution in RPL
Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography
We study maximal identifiability, a measure recently introduced in Boolean
Network Tomography to characterize networks' capability to localize failure
nodes in end-to-end path measurements. We prove tight upper and lower bounds on
the maximal identifiability of failure nodes for specific classes of network
topologies, such as trees and -dimensional grids, in both directed and
undirected cases. We prove that directed -dimensional grids with support
have maximal identifiability using monitors; and in the
undirected case we show that monitors suffice to get identifiability of
. We then study identifiability under embeddings: we establish relations
between maximal identifiability, embeddability and graph dimension when network
topologies are model as DAGs. Our results suggest the design of networks over
nodes with maximal identifiability using
monitors and a heuristic to boost maximal identifiability on a given network by
simulating -dimensional grids. We provide positive evidence of this
heuristic through data extracted by exact computation of maximal
identifiability on examples of small real networks
Active Topology Inference using Network Coding
Our goal is to infer the topology of a network when (i) we can send probes
between sources and receivers at the edge of the network and (ii) intermediate
nodes can perform simple network coding operations, i.e., additions. Our key
intuition is that network coding introduces topology-dependent correlation in
the observations at the receivers, which can be exploited to infer the
topology. For undirected tree topologies, we design hierarchical clustering
algorithms, building on our prior work. For directed acyclic graphs (DAGs),
first we decompose the topology into a number of two-source, two-receiver
(2-by-2) subnetwork components and then we merge these components to
reconstruct the topology. Our approach for DAGs builds on prior work on
tomography, and improves upon it by employing network coding to accurately
distinguish among all different 2-by-2 components. We evaluate our algorithms
through simulation of a number of realistic topologies and compare them to
active tomographic techniques without network coding. We also make connections
between our approach and alternatives, including passive inference, traceroute,
and packet marking
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
Evaluation of RPL’s Single Metric Objective Functions
In this paper, we evaluate the performance of RPL
(IPv6 Routing Protocol for Low Power and Lossy Networks)
based on the Objective Function being used to construct the
Destination Oriented Directed Acyclic Graph (DODAG). Using
the Cooja simulator, we compared Objective Function Zero (OF0)
with the Minimum Rank with Hysteresis Objective Function
(MRHOF) in terms of average power consumption, packet loss
ratio, and average end-to-end latency. Our study shows that RPL
performs better in terms of packet loss ratio and average endto-end
latency when MRHOF is used as an objective function.
However, the average power consumption is noticeably higher
compared to OF0
The Complexity of Routing with Few Collisions
We study the computational complexity of routing multiple objects through a
network in such a way that only few collisions occur: Given a graph with
two distinct terminal vertices and two positive integers and , the
question is whether one can connect the terminals by at least routes (e.g.
paths) such that at most edges are time-wise shared among them. We study
three types of routes: traverse each vertex at most once (paths), each edge at
most once (trails), or no such restrictions (walks). We prove that for paths
and trails the problem is NP-complete on undirected and directed graphs even if
is constant or the maximum vertex degree in the input graph is constant.
For walks, however, it is solvable in polynomial time on undirected graphs for
arbitrary and on directed graphs if is constant. We additionally study
for all route types a variant of the problem where the maximum length of a
route is restricted by some given upper bound. We prove that this
length-restricted variant has the same complexity classification with respect
to paths and trails, but for walks it becomes NP-complete on undirected graphs
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