379 research outputs found
Exploiting the Path Propagation Time Differences in Multipath Transmission with FEC
We consider a transmission of a delay-sensitive data stream from a single
source to a single destination. The reliability of this transmission may suffer
from bursty packet losses - the predominant type of failures in today's
Internet. An effective and well studied solution to this problem is to protect
the data by a Forward Error Correction (FEC) code and send the FEC packets over
multiple paths.
In this paper we show that the performance of such a multipath FEC scheme can
often be further improved. Our key observation is that the propagation times on
the available paths often significantly differ, typically by 10-100ms.
We propose to exploit these differences by appropriate packet scheduling that
we call `Spread'. We evaluate our solution with a precise, analytical
formulation and trace-driven simulations. Our studies show that Spread
substantially outperforms the state-of-the-art solutions. It typically achieves
two- to five-fold improvement (reduction) in the effective loss rate. Or
conversely, keeping the same level of effective loss rate, Spread significantly
decreases the observed delays and helps fighting the delay jitter.Comment: 12 page
Towards Unbiased BFS Sampling
Breadth First Search (BFS) is a widely used approach for sampling large
unknown Internet topologies. Its main advantage over random walks and other
exploration techniques is that a BFS sample is a plausible graph on its own,
and therefore we can study its topological characteristics. However, it has
been empirically observed that incomplete BFS is biased toward high-degree
nodes, which may strongly affect the measurements. In this paper, we first
analytically quantify the degree bias of BFS sampling. In particular, we
calculate the node degree distribution expected to be observed by BFS as a
function of the fraction f of covered nodes, in a random graph RG(pk) with an
arbitrary degree distribution pk. We also show that, for RG(pk), all commonly
used graph traversal techniques (BFS, DFS, Forest Fire, Snowball Sampling, RDS)
suffer from exactly the same bias. Next, based on our theoretical analysis, we
propose a practical BFS-bias correction procedure. It takes as input a
collected BFS sample together with its fraction f. Even though RG(pk) does not
capture many graph properties common in real-life graphs (such as
assortativity), our RG(pk)-based correction technique performs well on a broad
range of Internet topologies and on two large BFS samples of Facebook and Orkut
networks. Finally, we consider and evaluate a family of alternative correction
procedures, and demonstrate that, although they are unbiased for an arbitrary
topology, their large variance makes them far less effective than the
RG(pk)-based technique.Comment: BFS, RDS, graph traversal, sampling bias correctio
2.5K-Graphs: from Sampling to Generation
Understanding network structure and having access to realistic graphs plays a
central role in computer and social networks research. In this paper, we
propose a complete, and practical methodology for generating graphs that
resemble a real graph of interest. The metrics of the original topology we
target to match are the joint degree distribution (JDD) and the
degree-dependent average clustering coefficient (). We start by
developing efficient estimators for these two metrics based on a node sample
collected via either independence sampling or random walks. Then, we process
the output of the estimators to ensure that the target properties are
realizable. Finally, we propose an efficient algorithm for generating
topologies that have the exact target JDD and a close to the
target. Extensive simulations using real-life graphs show that the graphs
generated by our methodology are similar to the original graph with respect to,
not only the two target metrics, but also a wide range of other topological
metrics; furthermore, our generator is order of magnitudes faster than
state-of-the-art techniques
Becoming America: An Exploration of American Literature from Precolonial to Post-Revolution
The University of North Georgia Press and Affordable Learning Georgia bring you Becoming America: An Exploration of American Literature from Precolonial to Post-Revolution. Featuring sixty-nine authors and full texts of their works, the selections in this open anthology represent the diverse voices in early American literature. This completely-open anthology will connect students to the conversation of literature that is embedded in American history and has helped shaped its culture.
Features: Contextualizing introductions from Pre- and Early Colonial Literature to Early American Romanticism Over 70 historical images In-depth biographies of each author Instructional Design, including Reading and Review Questions
This textbook is an open Educational Resource. It can be reused, remixed, and reedited freely without seeking permission.
About the editor:
Wendy Kurant, Ph.D., teaches Early American Literature, American Romanticism, Realism, Naturalism, and Southern Literature at the University of North Georgia (UNG). Her research interests center on new Historicism and depictions of the South and the Civil War in Literature. She has taught at UNG since 2005.https://oer.galileo.usg.edu/english-textbooks/1019/thumbnail.jp
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Phantom Capital, Hybrid Authorship, and Collective Intelligence
Good morning. Thank you Jane for this introduction. Thank you very much for inviting me to be a part of this Symposium. I won’t have much time to talk about my work, but I will try to say a couple words about several projects. Let me start by explaining the main focus of my practice, which relates in particular to this conference. I have to say that the questions of copyright and authorship and hybrid value of objects, among them artworks, are actually at the core of my artistic practice
Active Learning of Multiple Source Multiple Destination Topologies
We consider the problem of inferring the topology of a network with
sources and receivers (hereafter referred to as an -by- network), by
sending probes between the sources and receivers. Prior work has shown that
this problem can be decomposed into two parts: first, infer smaller subnetwork
components (i.e., -by-'s or -by-'s) and then merge these components
to identify the -by- topology. In this paper, we focus on the second
part, which had previously received less attention in the literature. In
particular, we assume that a -by- topology is given and that all
-by- components can be queried and learned using end-to-end probes. The
problem is which -by-'s to query and how to merge them with the given
-by-, so as to exactly identify the -by- topology, and optimize a
number of performance metrics, including the number of queries (which directly
translates into measurement bandwidth), time complexity, and memory usage. We
provide a lower bound, , on the number of
-by-'s required by any active learning algorithm and propose two greedy
algorithms. The first algorithm follows the framework of multiple hypothesis
testing, in particular Generalized Binary Search (GBS), since our problem is
one of active learning, from -by- queries. The second algorithm is called
the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at
every step, it selects two receivers, queries the corresponding -by-, and
merges it with the given -by-; it requires exactly steps, which is
much less than all possible -by-'s. Simulation results
over synthetic and realistic topologies demonstrate that both algorithms
correctly identify the -by- topology and are near-optimal, but REA is
more efficient in practice
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