�ݺ�ߣshows by User: aiosup

�ݺ�ߣshows by User: aiosup / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: aiosup / Fri, 16 May 2014 10:16:36 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: aiosup Big Data in the Cloud: Enabling the Fourth Paradigm by Matching SMEs with Datacenters /slideshow/2014-0514-aiosupbigdatacloud14isobdsgams/34771250 2014-05-14aiosupbigdatacloud14iso-bdsg-ams-140516101636-phpapp02
Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data [4], evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5]. [1] A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf [2] de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl [3] Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf [4] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf [5] B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf]]>
Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data [4], evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5]. [1] A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf [2] de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl [3] Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf [4] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf [5] B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf]]> Fri, 16 May 2014 10:16:36 GMT /slideshow/2014-0514-aiosupbigdatacloud14isobdsgams/34771250 aiosup@slideshare.net(aiosup) Big Data in the Cloud: Enabling the Fourth Paradigm by Matching SMEs with Datacenters aiosup Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data [4], evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5]. [1] A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf [2] de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl [3] Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf [4] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf [5] B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014-05-14aiosupbigdatacloud14iso-bdsg-ams-140516101636-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data [4], evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5]. [1] A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf [2] de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl [3] Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf [4] Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf [5] B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf

Big Data in the Cloud: Enabling the Fourth Paradigm by Matching SMEs with Datacenters from Alexandru Iosup

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0 Gamification: Playful Teaching for Generation-X/-Y/-Z/... /slideshow/2013-a-iosupgamificationprimertudodag/18116337 2013aiosupgamification-primer-tud-odag-130403100612-phpapp01
A primer on gamification in higher education, that is, the use of elements commonly found in gaming to create and deliver higher-education units (courses).]]>
A primer on gamification in higher education, that is, the use of elements commonly found in gaming to create and deliver higher-education units (courses).]]> Wed, 03 Apr 2013 10:06:12 GMT /slideshow/2013-a-iosupgamificationprimertudodag/18116337 aiosup@slideshare.net(aiosup) Gamification: Playful Teaching for Generation-X/-Y/-Z/... aiosup A primer on gamification in higher education, that is, the use of elements commonly found in gaming to create and deliver higher-education units (courses). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2013aiosupgamification-primer-tud-odag-130403100612-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A primer on gamification in higher education, that is, the use of elements commonly found in gaming to create and deliver higher-education units (courses).

Gamification: Playful Teaching for Generation-X/-Y/-Z/... from Alexandru Iosup

]]> 1130 5 https://cdn.slidesharecdn.com/ss_thumbnails/2013aiosupgamification-primer-tud-odag-130403100612-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Cloud Programming Models: eScience, Big Data, etc. /slideshow/cloud-programming-models-escience-big-data-etc/16664128 2012in4392lecture-5cloudprogrammingmodels-130221010438-phpapp01
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]]> Thu, 21 Feb 2013 01:04:38 GMT /slideshow/cloud-programming-models-escience-big-data-etc/16664128 aiosup@slideshare.net(aiosup) Cloud Programming Models: eScience, Big Data, etc. aiosup <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012in4392lecture-5cloudprogrammingmodels-130221010438-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Cloud Programming Models: eScience, Big Data, etc. from Alexandru Iosup

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0 Multi-Tenancy and Virtualization in Cloud Computing /slideshow/multitenancy-and-virtualization-in-cloud-computing/16664062 2012in4392lecture-4multi-tenancyvirtualization-130221010241-phpapp01
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]]> Thu, 21 Feb 2013 01:02:41 GMT /slideshow/multitenancy-and-virtualization-in-cloud-computing/16664062 aiosup@slideshare.net(aiosup) Multi-Tenancy and Virtualization in Cloud Computing aiosup <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012in4392lecture-4multi-tenancyvirtualization-130221010241-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Multi-Tenancy and Virtualization in Cloud Computing from Alexandru Iosup

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0 Introduction to Cloud Computing /slideshow/2012-in4392-lecture1/16663907 2012in4392lecture-1-130221005702-phpapp02
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]]> Thu, 21 Feb 2013 00:57:02 GMT /slideshow/2012-in4392-lecture1/16663907 aiosup@slideshare.net(aiosup) Introduction to Cloud Computing aiosup <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012in4392lecture-1-130221005702-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Introduction to Cloud Computing from Alexandru Iosup

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0 IaaS Cloud Benchmarking: Approaches, Challenges, and Experience /slideshow/iaas-cloud-benchmarking-approaches-challenges-and-experience/15136517 2012-11-11aiosupcloud-benchmarking12mtags-121112064610-phpapp02
IaaS Cloud Benchmarking: Approaches, Challenges, and Experience Impact Award lecture at MTAGS/ACM SC'12. URL: http://datasys.cs.iit.edu/events/MTAGS12/biggest-impact-award.html]]>
IaaS Cloud Benchmarking: Approaches, Challenges, and Experience Impact Award lecture at MTAGS/ACM SC'12. URL: http://datasys.cs.iit.edu/events/MTAGS12/biggest-impact-award.html]]> Mon, 12 Nov 2012 06:46:08 GMT /slideshow/iaas-cloud-benchmarking-approaches-challenges-and-experience/15136517 aiosup@slideshare.net(aiosup) IaaS Cloud Benchmarking: Approaches, Challenges, and Experience aiosup IaaS Cloud Benchmarking: Approaches, Challenges, and Experience Impact Award lecture at MTAGS/ACM SC'12. URL: http://datasys.cs.iit.edu/events/MTAGS12/biggest-impact-award.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012-11-11aiosupcloud-benchmarking12mtags-121112064610-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> IaaS Cloud Benchmarking: Approaches, Challenges, and Experience Impact Award lecture at MTAGS/ACM SC'12. URL: http://datasys.cs.iit.edu/events/MTAGS12/biggest-impact-award.html

IaaS Cloud Benchmarking: Approaches, Challenges, and Experience from Alexandru Iosup

]]> 1866 4 https://cdn.slidesharecdn.com/ss_thumbnails/2012-11-11aiosupcloud-benchmarking12mtags-121112064610-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 GrenchMark at CCGrid, May 2006. /slideshow/grenchmark-at-ccgrid-may-2006/12281985 gmark06talk-120404105612-phpapp01
Under the grid computing paradigm, large sets of heterogeneous resources can be aggregated and shared. Grid development and acceptance hinge on proving that grids reliably support real applications, and on creating adequate benchmarks to quantify this support. However, applications of grids (and clouds) are just beginning to emerge, and traditional benchmarks have yet to prove representative in grid environments. To address this chicken-and-egg problem, we propose a middle-way approach: create and run synthetic grid workloads comprised of applications representative for today's grids (and clouds). For this purpose, we have designed and implemented GrenchMark, a framework for synthetic workload generation and submission. The framework greatly facilitates synthetic workload modeling, comes with over 35 synthetic and real applications, and is extensible and flexible. We show how the framework can be used for grid system analysis, functionality testing in grid environments, and for comparing different grid settings, and present the results obtained with GrenchMark in our multi-cluster grid, the DAS.]]>
Under the grid computing paradigm, large sets of heterogeneous resources can be aggregated and shared. Grid development and acceptance hinge on proving that grids reliably support real applications, and on creating adequate benchmarks to quantify this support. However, applications of grids (and clouds) are just beginning to emerge, and traditional benchmarks have yet to prove representative in grid environments. To address this chicken-and-egg problem, we propose a middle-way approach: create and run synthetic grid workloads comprised of applications representative for today's grids (and clouds). For this purpose, we have designed and implemented GrenchMark, a framework for synthetic workload generation and submission. The framework greatly facilitates synthetic workload modeling, comes with over 35 synthetic and real applications, and is extensible and flexible. We show how the framework can be used for grid system analysis, functionality testing in grid environments, and for comparing different grid settings, and present the results obtained with GrenchMark in our multi-cluster grid, the DAS.]]> Wed, 04 Apr 2012 10:56:10 GMT /slideshow/grenchmark-at-ccgrid-may-2006/12281985 aiosup@slideshare.net(aiosup) GrenchMark at CCGrid, May 2006. aiosup Under the grid computing paradigm, large sets of heterogeneous resources can be aggregated and shared. Grid development and acceptance hinge on proving that grids reliably support real applications, and on creating adequate benchmarks to quantify this support. However, applications of grids (and clouds) are just beginning to emerge, and traditional benchmarks have yet to prove representative in grid environments. To address this chicken-and-egg problem, we propose a middle-way approach: create and run synthetic grid workloads comprised of applications representative for today's grids (and clouds). For this purpose, we have designed and implemented GrenchMark, a framework for synthetic workload generation and submission. The framework greatly facilitates synthetic workload modeling, comes with over 35 synthetic and real applications, and is extensible and flexible. We show how the framework can be used for grid system analysis, functionality testing in grid environments, and for comparing different grid settings, and present the results obtained with GrenchMark in our multi-cluster grid, the DAS. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gmark06talk-120404105612-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Under the grid computing paradigm, large sets of heterogeneous resources can be aggregated and shared. Grid development and acceptance hinge on proving that grids reliably support real applications, and on creating adequate benchmarks to quantify this support. However, applications of grids (and clouds) are just beginning to emerge, and traditional benchmarks have yet to prove representative in grid environments. To address this chicken-and-egg problem, we propose a middle-way approach: create and run synthetic grid workloads comprised of applications representative for today's grids (and clouds). For this purpose, we have designed and implemented GrenchMark, a framework for synthetic workload generation and submission. The framework greatly facilitates synthetic workload modeling, comes with over 35 synthetic and real applications, and is extensible and flexible. We show how the framework can be used for grid system analysis, functionality testing in grid environments, and for comparing different grid settings, and present the results obtained with GrenchMark in our multi-cluster grid, the DAS.

GrenchMark at CCGrid, May 2006. from Alexandru Iosup

]]> 416 4 https://cdn.slidesharecdn.com/ss_thumbnails/gmark06talk-120404105612-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-aiosup-48x48.jpg?cb=1606292661 Prof.dr.ir. Alexandru Iosup is full tenured professor and University Research Chair at Vrije Universiteit Amsterdam, and member of the Young Royal Academy of Arts and Sciences of the Netherlands. He received his PhD in computer science from TU Delft, the Netherlands. He is the chair of the Massivizing Computer Systems research group at the VU and of the SPEC-RG Cloud group. His work in distributed systems and ecosystems has received prestigious recognition, including the 2016 Netherlands ICT Researcher of the Year, the 2015 Netherlands Higher-Education Teacher of the Year, and several SPEC community awards SPECtacular (last in 2017). In 2020, he was knighted by the Romanian President. atlarge-research.com/aiosup/ https://cdn.slidesharecdn.com/ss_thumbnails/2014-05-14aiosupbigdatacloud14iso-bdsg-ams-140516101636-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2014-0514-aiosupbigdatacloud14isobdsgams/34771250 Big Data in the Cloud:... https://cdn.slidesharecdn.com/ss_thumbnails/2013aiosupgamification-primer-tud-odag-130403100612-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2013-a-iosupgamificationprimertudodag/18116337 Gamification: Playful ... https://cdn.slidesharecdn.com/ss_thumbnails/2012in4392lecture-5cloudprogrammingmodels-130221010438-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cloud-programming-models-escience-big-data-etc/16664128 Cloud Programming Mode...