skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: MaxReach: Reducing Network Incompleteness through Node Probes

; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Presented at: The IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, San Francisco, CA, United States, Aug 18 - Aug 21, 2016
Country of Publication:
United States

Citation Formats

Soundarajan, S, Eliassi-Rad, T, Gallagher, B, and Pinar, A. MaxReach: Reducing Network Incompleteness through Node Probes. United States: N. p., 2016. Web. doi:10.1109/ASONAM.2016.7752227.
Soundarajan, S, Eliassi-Rad, T, Gallagher, B, & Pinar, A. MaxReach: Reducing Network Incompleteness through Node Probes. United States. doi:10.1109/ASONAM.2016.7752227.
Soundarajan, S, Eliassi-Rad, T, Gallagher, B, and Pinar, A. Wed . "MaxReach: Reducing Network Incompleteness through Node Probes". United States. doi:10.1109/ASONAM.2016.7752227.
title = {MaxReach: Reducing Network Incompleteness through Node Probes},
author = {Soundarajan, S and Eliassi-Rad, T and Gallagher, B and Pinar, A},
abstractNote = {},
doi = {10.1109/ASONAM.2016.7752227},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jun 22 00:00:00 EDT 2016},
month = {Wed Jun 22 00:00:00 EDT 2016}

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Abstract not provided.
  • Consider a network where nodes represent processors and edges represent bidirectional communication links. The processor at a node v can be upgraded at an expense of cost(v). Such an upgrade reduces the delay of each link emanating from v by a fixed factor x, where 0 < x < 1. The goal is to find a minimum cost set of nodes to be upgraded so that the resulting network has a spanning tree in which edge is of delay at most a given value {delta}. The authors provide both hardness and approximation results for the problem. They show that themore » problem is NP-hard and cannot be approximated within any factor {beta} < ln n, unless NP {improper_subset} DTIME(n{sup log log n}), where n is the number of nodes in the network. They then present the first polynomial time approximation algorithms for the problem. For the general case, the approximation algorithm comes within a factor of 2 ln n of the minimum upgrading cost. When the cost of upgrading each node is 1, they present an approximation algorithm with a performance guarantee of 4(2 + ln {Delta}), where {Delta} is the maximum node degree. Finally, they present a polynomial time algorithm for the class of treewidth-bounded graphs.« less
  • Given a Bayesian network, sensitivity analysis is an important activity. This paper begins by describing a network augmentation technique which can simplifY the analysis. Next, we present two techniques which allow the user to determination the probability distribution of a hypothesis node under conditions of uncertain evidence; i.e. the state of an evidence node or nodes is described by a user specified probability distribution. Finally, we conclude with a discussion of three criteria for ranking evidence nodes based on their influence on a hypothesis node. All of these techniques have been used in conjunction with a commercial software package. Amore » Bayesian network based on a directed acyclic graph (DAG) G is a graphical representation of a system of random variables that satisfies the following Markov property: any node (random variable) is independent of its non-descendants given the state of all its parents (Neapolitan, 2004). For simplicities sake, we consider only discrete variables with a finite number of states, though most of the conclusions may be generalized.« less
  • Oak Ridge National Laoratory (ORNL) has engineered an on-line enrichment monitor (OLEM) to continuously measure U-235 emissions from the UF6 gas flowing through a unit header pipe of a gas centrifuge enrichment plant (GCEP) as a component of the International Atomic Energy Agency s (IAEA) new generation of technology to support enrichment plant safeguards1. In contrast to other enrichment monitoring approaches, OLEM calibrates and corrects for the pressure and temperature dependent UF6 gas-density without external radiation sources by using the inherent unit header pipe pressure dynamics and combining U-235 gamma-ray spectrometery using a shielded NaI detector with gas pressure andmore » temperature data near the spectrum measurement point to obtain the enrichment of the gas as a function of time. From a safeguards perspective, OLEM can provide early detection of a GCEP being misused for production of highly enriched uranium, but would not detect directly the isolation and use of a cascade within the production unit to produce HEU. OLEM may also reduce the number of samples collected for destructive assay and, if coupled with load cell monitoring, could support isotope mass balance verification and unattended cylinder verification. The earlier paper presented OLEM as one component along with shared load cells and unattended cylinder verification, in the IAEA emering toolbox for unattended instruments at GCEPs1 and described the OLEM concept and how previous modeling studies and field measurements helped confirm the viability of a passive on-line enrichment monitor for meeting IAEA objectives and to support the development of performance targets. Phase I of the United States Support Program (USSP) OLEM project completed a preliminary hardware, software and communications design; phase II will build and test field prototypes in controlled laboratory settings and then at an operational facility. That paper also discussed many of the OLEM collection node commercial off the shelf (COTS) components and summarized the OLEM collection node data security provisions. This paper will discuss a secure and redundant network of OLEM collection nodes, auxiliary detection units and supporting junction boxes distributed throughout a facility for monitoring enrichment on product, feed and tails unit header pipes; the purpose and capability of the built-in Electronic Optical Sealing System (EOSS) network gateway; and a network approach for obtaining reliable and authenticated pressure measurements.« less
  • As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the modelmore » size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.« less