WNCG - Wireless Networking and Communications Group - networks
http://wncg.org/tags/networks
enNetworks-of-Systems Simulation
http://wncg.org/research/briefs/networks-systems-simulation
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Wednesday, April 8, 2015</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>In future computing systems, such as the Internet-of-Things (IoT), functionality is increasingly defined by the networked connectivity of spatially distributed devices. This, however, poses fundamentally new design challenges and tradeoffs. Computation and communication need to be tightly coupled and jointly explored, e.g. to determine whether a functionality should be performed locally or remotely over the network in order to achieve the best performance and energy consumption. For this, designers and application developers will first and foremost need fast yet accurate simulations tools that will allow them to rapidly explore this design space. With tens to hundreds of devices in a network, traditional solutions that combine detailed system and network simulations will likely be too slow. In this work, we investigate novel networks-of-systems (NoS) simulators to close this gap. Building on our previous work on host-compiled system simulation, we develop high-level abstractions and unified models of network/system interactions. To achieve fast simulation speed, it is crucial to carefully abstract away unnecessary low-level details and describe behavior at a coarse granularity as much as possible. For improved accuracy, we employ advanced prediction and adaptivity approaches that exploit inherent knowledge about future system behavior encoded in a model. To further improve simulation speeds, we parallelize simulations on multiple host CPUs and GPUs. Results show that such simulators can scale to large numbers of nodes while providing accurate feedback about NoS performance and power consumption.</p>
<p> This research is funded by the Nation Science Foundation.</p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/andreas-gerstlauer">Andreas Gerstlauer</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/networks">networks</a>, <a href="/tags/systems">Systems</a>, <a href="/tags/wncg">WNCG</a>, <a href="/tags/andreas-gerstlauer">Andreas Gerstlauer</a></div></div></div>Wed, 08 Apr 2015 16:00:54 +0000lab27993706 at http://wncg.orghttp://wncg.org/research/briefs/networks-systems-simulation#commentsDetecting Epidemics in Networks
http://wncg.org/research/briefs/detecting-epidemics-networks
<div class="field field-name-field-publish-date field-type-datetime field-label-hidden"><div class="field-items"><div class="field-item even"><span class="date-display-single">Saturday, March 22, 2014</span></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"> <p>WNCG Ph.D. student Chris Milling, along with WNCG Professors Constantine Caramanis and Sanjay Shakkottai, and Technion Professor Shie Mannor, have developed efficient algorithms for quickly and efficiently determining if an epidemic is spreading through a social network.</p>
<p>The history of infections and epidemics holds famous examples where understanding, containing and ultimately treating an outbreak began with understanding its mode of spread. Influenza, HIV and most computer viruses, spread person to person, device to device, through contact networks; Cholera, Cancer, and seasonal allergies, on the other hand, do not. In this paper we study two fundamental questions of detection: first, given a snapshot view of a (perhaps vanishingly small) fraction of those infected, under what conditions is an epidemic spreading via contact (e.g., Influenza), distinguishable from a "random illness" operating independently of any contact network (e.g., seasonal allergies); second, if we do have an epidemic, under what conditions is it possible to determine which network of interactions is the main cause of the spread -- the <em> causative network</em> -- without any knowledge of the epidemic, other than the identity of a minuscule subsample of infected nodes? The core, therefore, of this paper, is to obtain an understanding of the <em>diagnostic power of network information</em>. We derive sufficient conditions networks must satisfy for these problems to be identifiable, and produce efficient, highly scalable algorithms that solve these problems. We show that the identifiability condition we give is fairly mild, and in particular, is satisfied by two common graph topologies: the grid, and the Erdos-Renyi graphs. For the details, see:</p>
<ul><li><a href="http://arxiv.org/pdf/1309.6545v1.pdf">Epidemic Detection on Networks</a></li>
</ul><p>This work was partially supported by the National Science Foundation (NSF) and the Defense Threat Reduction Agency (DTRA).</p>
</div></div></div><div class="field field-name-field-related-faculty field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Faculty: </div><div class="field-items"><div class="field-item even"><a href="/people/faculty/constantine-caramanis">Constantine Caramanis</a></div><div class="field-item odd"><a href="/people/faculty/sanjay-shakkottai">Sanjay Shakkottai</a></div></div></div><div class="field field-name-field-related-students field-type-node-reference field-label-inline clearfix"><div class="field-label">Related Researchers: </div><div class="field-items"><div class="field-item even"><a href="/people/students/p-chris-milling">P. Chris Milling</a></div></div></div><div class="field field-name-field-tags field-type-taxonomy-term-reference field-label-inline clearfix"><div class="field-label">Keywords: </div><div class="field-items"><div class="field-item even"><a href="/tags/networks">networks</a>, <a href="/tags/social-networks">social networks</a>, <a href="/tags/machine-learning">Machine Learning</a>, <a href="/tags/statistics">Statistics</a></div></div></div>Sat, 22 Mar 2014 20:22:33 +0000cc333383418 at http://wncg.orghttp://wncg.org/research/briefs/detecting-epidemics-networks#comments