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UID:submissions.supercomputing.org_SC18_sess163@linklings.com
SUMMARY:WORKS 2018: 13th Workshop on Workflows in Support of Large-Scale
DESCRIPTION:Workshop\nReproducibility, Scientific Computing, Scientific Wo
 rkflows, Workflows, Workshop Reg Pass, HPC, Data Intensive\n\nWorkshop Aft
 ernoon Break\n\n\n\n---------------------\nOptimizing the Throughput of St
 orm-Based Stream Processing in Clouds\n\nCao, Wu, Bao, Hou\n\nThere is a r
 apidly growing need for processing large volumes of streaming data in real
  time in various big data applications. As one of the most commonly used s
 ystems for streaming data processing, Apache Storm provides a workflow-bas
 ed mechanism to execute directed acyclic graph (DAG)-structured to...\n\n-
 --------------------\nIntroduction - WORKS 2018: 13th Workshop on Workflow
 s in Support of Large-Scale Science\n\nGesing, Ferreira da Silva\n\nData I
 ntensive Workflows (aka scientific workflows) are routinely used in most s
 cientific disciplines today, especially in the context of parallel and dis
 tributed computing. Workflows provide a systematic way of describing the a
 nalysis and rely on workflow management systems to execute the complex a..
 .\n\n---------------------\nWorkshop Lunch (on your own)\n\n\n\n----------
 -----------\nEnergy-Aware Workflow Scheduling and Optimization in Clouds U
 sing Bat Algorithm\n\nGu, Budati\n\nWith the ever-increasing deployment of
  data centers and computer networks around the world, cloud computing has 
 emerged as one of the most important paradigms for large-scale data-intens
 ive applications. However, these cloud environments face many challenges i
 ncluding energy consumption, execution t...\n\n---------------------\nWORK
 S 2018 Panel\n\n\n\n---------------------\nDagOn*: Executing Direct Acycli
 c Graphs as Parallel Jobs on Anything\n\nMontella, Di Luccio, Kosta\n\nThe
  democratization of computational resources, thanks to the advent of publi
 c, private, and hybrid clouds, changed the rules in many science fields. F
 or decades, one of the effort of computer scientists and computer engineer
 s was the development of tools able to simplify access to high-end computa
 t...\n\n---------------------\nKeynote\n\nAltintas\n\n--------------------
 -\nWorkshop Morning Break\n\n\n\n---------------------\nWRENCH: A Framewor
 k for Simulating Workflow Management Systems\n\nCasanova, Pandey, Oeth, Ta
 naka, Suter...\n\nScientific workflows are used routinely in numerous scie
 ntific domains, and Workflow Management Systems (WMSs) have been developed
  to orchestrate and optimize workflow executions on distributed platforms.
   WMSs are complex software systems that interact with complex software in
 frastructures. Most WM...\n\n---------------------\nReduction of Workflow 
 Resource Consumption Using a Density-based Clustering Model\n\nZhang, Krem
 er-Herman, Tovar, Thain\n\nOften times, a researcher running a scientific 
 workflow will ask for orders of magnitude too few or too many resources to
  run their workflow. If the resource requisition is too small, the job may
  fail due to resource exhaustion; if it is too large, resources will be wa
 sted though job may succeed. It...\n\n---------------------\nEnd-to-End On
 line Performance Data Capture and Analysis for Scientific Workflows\n\nPap
 adimitriou, Wang, Vahi, Ferreira da Silva, Mandal...\n\nWith the increased
  prevalence of employing workflows for scientific computing and a push tow
 ard exascale computing, it has become paramount that we are able to analyz
 e characteristics of scientific applications to better understand the impa
 ct on the underlying infrastructure and vice-versa. Such ana...\n\n-------
 --------------\nFlux: Overcoming Scheduling Challenges for Exascale Workfl
 ows\n\nAhn, Bass, Chu, Garlick, Grondona...\n\nMany emerging scientific wo
 rkflows that target high-end HPC systems require complex interplay with th
 e resource and job management software~(RJMS).  However, portable, efficie
 nt and easy-to-use scheduling and execution of these workflows is still an
  unsolved problem.  We present Flux, a novel, hiera...\n\n----------------
 -----\nDynamic Distributed Orchestration of Node-RED IOT Workflows Using a
  Vector Symbolic Architecture\n\nSimpkin, Taylor, Harborne, Bent, Preece..
 .\n\nThere are a large number of workflow systems designed to work in vari
 ous scientific domains, including support for the Internet of Things (IoT)
 .  One such workflow system is Node-RED, which is designed to bring workfl
 ow-based programming to IoT. However, the majority of scientific workflow 
 systems, ...\n\n---------------------\nLOS: Level Order Sampling for Task 
 Graph Scheduling on Heterogeneous Resources\n\nWitt, Wheating, Leser\n\nLi
 st scheduling is an approach to task graph scheduling that has been shown 
 to work well for scheduling tasks with data dependencies on heterogeneous 
 resources. Key to the performance of a list scheduling heuristic is its me
 thod to prioritize the tasks, and various ranking schemes have been propos
 ed...\n\n---------------------\nPlanner: Cost-efficient Execution Plans Pl
 acement for Uniform Stream Analytics on Edge and Cloud\n\nProsperi, Costan
 , Silva, Antoniu\n\nStream processing applications handle unbounded and co
 ntinuous flows of data items which are generated from multiple geographica
 lly distributed sources. Two approaches are commonly used for processing: 
 cloud-based analytics and edge analytics. The first one routes the whole d
 ata set to the Cloud, in...\n\n---------------------\nA Practical Roadmap 
 for Provenance Capture and Data Analysis in Spark-Based Scientific Workflo
 ws\n\nGuedes, Silva, Mattoso, Bedo, Oliveira\n\nWhenever high-performance 
 computing applications meet data-intensive scalable systems, an attractive
  approach is the use of Apache Spark for the management of scientific work
 flows. Spark provides several advantages such as being widely supported an
 d granting efficient in-memory data management for l...\n
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