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DTSTAMP:20181221T160906Z
LOCATION:D163
DTSTART;TZID=America/Chicago:20181111T090000
DTEND;TZID=America/Chicago:20181111T173000
UID:submissions.supercomputing.org_SC18_sess159@linklings.com
SUMMARY:Innovating the Network for Data Intensive Science (INDIS)
DESCRIPTION:Workshop\nArchitectures, Networks, Security, Workshop Reg Pass
 \n\nAnalysis of CPU Pinning and Storage Configuration in 100 Gbps Network 
 Data Transfer\n\nYu, Chen, Mambretti, Yeh\n\nA common bottleneck for high-
 speed network data transfers is lack of CPU resources. A number of techniq
 ues and solutions have been proposed to reduce CPU load for data transfer.
  One can optimize the core affinity settings in their Non-Uniform Memory A
 ccess (NUMA) system and use NVMe over Fabrics to ...\n\n------------------
 ---\nBandwidth Scheduling for Big Data Transfer with Deadline Constraint b
 etween Data Centers\n\nHou, Wu, Fang, Zuo, Zhu...\n\nAn increasing number 
 of applications in scientific and other domains have moved or are in activ
 e transition to clouds, and the demand for the movement of big data betwee
 n geographically distributed cloud-based data centers is rapidly growing. 
 Many modern backbone networks leverage logically centrali...\n\n----------
 -----------\nFlowzilla: A Methodology for Detecting Data Transfer Anomalie
 s in Research Networks\n\nGiannakou, Gunter, Peisert\n\nResearch networks 
 are designed to support high volume scientific data transfers that span mu
 ltiple network links. Like any other network, research networks experience
  anomalies. Anomalies are deviations from profiles of normality in a scien
 ce network’s traffic levels. Diagnosing anomalies is critica...\n\n-------
 --------------\nIntroduction - Innovating the Network for Data Intensive S
 cience (INDIS)\n\nGrosso, Hester, Baldin, Zhu\n\nWide area networks are no
 w an integral and essential part of this data-driven supercomputing ecosys
 tem connecting information sources, data stores, processing, simulation, v
 isualization and user communities together.  Networks for data-intensive s
 cience have more extreme requirements than general-pu...\n\n--------------
 -------\nFast Detection of Elephant Flows with Dirichlet-Categorical Infer
 ence\n\nGudibanda, Ros-Giralt, Commike, Lethin\n\nThe problem of elephant 
 flow detection is a longstanding research area with the goal of quickly id
 entifying flows in a network that are large enough to affect the quality o
 f service of smaller flows. Past work in this field has largely been eithe
 r domain-specific, based on thresholds for a specific ...\n\n-------------
 --------\nSDN for End-to-End Networked Science at the Exascale (SENSE)\n\n
 Monga, Guok, MacAuley, Sim, Newman...\n\nThe Software-defined network for 
 End-to-end Networked Science at Exascale (SENSE) research project is build
 ing smart network services to accelerate scientific discovery in the era o
 f ‘big data’ driven by Exascale, cloud computing, machine learning and AI.
  The project’s architecture, models, and demo...\n\n---------------------\
 nSocial Computational Trust Model (SCTM): A Framework to Facilitate Select
 ion of Partners\n\nDeljoo, van Engers, Gommans, de Laat\n\nCreating a cybe
 r security alliance among network domain owners, as a means to minimize se
 curity incidents, has gained the interest of practitioners and academics i
 n the last few years. A cyber security alliance, like any membership organ
 ization, requires the creation and maintenance of trust among i...\n\n----
 -----------------\nTracking Network Flows with P4\n\nHill, Aloserij, Gross
 o\n\nTracking flows within a single device, as well as tracking the full p
 ath a flow takes in a network, are core components in securing networks. M
 alicious traffic can be easily identified and its source blocked. Traditio
 nal methods have performance and precision shortcomings, while new program
 mable dev...\n\n---------------------\nBigData Express: Toward Schedulable
 , Predictable, and High-Performance Data Transfer\n\nLu, Zhang, Sasidharan
 , Wu, Demar...\n\nBig Data has emerged as a driving force for scientific d
 iscoveries. Large scientific instruments (e.g., colliders, and telescopes)
  generate exponentially increasing volumes of data. To enable scientific d
 iscovery, science data must be collected, indexed, archived, shared, and a
 nalyzed, typically in ...\n\n---------------------\nINDIS Afternoon Keynot
 e\n\nKeahey\n\nKate Keahey will give the second keynote on "Chameleon: New
  Capabilities for Experimental Computer Science". Chameleon is a large-sca
 le, deeply reconfigurable testbed built specifically to explore a broad ra
 nge of different state-of-the-art hardware options, assess scalability of 
 systems, and provid...\n\n---------------------\nINDIS Morning Keynote\n\n
 Bailey\n\nJosh Bailey will give a keynote on FAUCET. FAUCET is an open sou
 rce, production SDN controller, supporting multiple network vendors, and i
 s deployed as part of SC18's SCinet network among other networks. This tal
 k will be an update on FAUCET's current status and features and an informa
 l Q&A on the s...\n\n---------------------\nWorkshop Morning Break\n\n\n\n
 ---------------------\nWorkshop Lunch (on your own)\n\n\n\n---------------
 ------\nINDIS Showcases Panel: NRE and XNET and Architecture\n\n\n\n------
 ---------------\nINDIS Invited Talk: Introduction to SCinet\n\nZurawski\n\
 n---------------------\nWorkshop Afternoon Break\n\n\n
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