SlideShare una empresa de Scribd logo
1 de 20
High-throughput eScience mixing Grids and Clouds:,[object Object],an experience with the Nimrod tool family,[object Object],Presenter:,[object Object],Blair Bethwaite,[object Object],MonasheScience and Grid Engineering Lab,[object Object]
MeSsAGE Lab team:,[object Object],David Abramson,[object Object],Colin Enticott,[object Object],SlavisaGaric,[object Object],and others...,[object Object],Acknowledgements,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Agenda,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
The Nimrod tool family,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Parametric computing with the Nimrod tools,[object Object],Vary parameters,[object Object],Execute programs,[object Object],Copy code/data in/out,[object Object],X, Y, Z could be:,[object Object],Basic data types; ints, floats, strings,[object Object],Files,[object Object],Random numbers to drive Monte Carlo modelling,[object Object],X,[object Object],Y,[object Object],Parameter,[object Object],Space,[object Object],Solution,[object Object],Space,[object Object],Z,[object Object],User Job,[object Object],EII Cloud Workshop - AWS Intro		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Nimrod Applications,[object Object],messagelab.monash.edu.au/EScienceApplications,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
From Clusters, to Grids, to Clouds,[object Object],Jobs / Nimrod experiment,[object Object],Nimrod,[object Object],Actuator, e.g., SGE, PBS, LSF, Condor,[object Object],Local Batch System,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
From Clusters, to Grids, to Clouds,[object Object],Jobs / Nimrod experiment,[object Object],Portal,[object Object],Nimrod-O/E/K,[object Object],Nimrod/G,[object Object],Actuator, e.g., Globus,[object Object],Servers,[object Object],Upper middleware,[object Object],Lower middleware,[object Object],Pilot jobs / agents,[object Object],Agents,[object Object],Grid Middleware,[object Object],Grid Middleware,[object Object],Grid Middleware,[object Object],Agents,[object Object],Grid Middleware,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
From Clusters, to Grids, to Clouds,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object],The Grid,[object Object],Global utility computing mk.1-(beta),[object Object],Somewhere in-between Infrastructure and Platform as-a-Service,[object Object],For Nimrod,[object Object],Increased computational scale – massively parallel,[object Object],New scheduling and data challenges,[object Object],Computational economy proposed,[object Object],Problems,[object Object],Interoperability,[object Object],Barriers to entry,[object Object]
From Clusters, to Grids, to Clouds,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
From Clusters, to Grids, to Clouds,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object],Cloud opportunities for HTC,[object Object],Virtualisation helps interoperability and scalability,[object Object],Cloud bursting,[object Object],Scale-out to supplement locally and nationally available resources,[object Object],Test computational economy and scheduling, in anger,[object Object],Deadline driven,[object Object],Budget driven,[object Object],What’s missing?,[object Object],Grids provide services above IaaS,[object Object],E.g., you can build a grid on EC2,[object Object],Grids provide job and data handling services, more like PaaS,[object Object]
From Clusters, to Grids, to Clouds,[object Object], def process_queue(self):,[object Object],        """Prepare allocation of commands/agents to instances.,[object Object],        This might mean requesting new instances from the web service and/or,[object Object],        allocating available slots from existing instances.,[object Object],        ""“,[object Object],        if not self._queued_cmds and not self.proxy:,[object Object],            return False,[object Object],self._update_available_instances(),[object Object],req_slots = len(self._queued_cmds),[object Object],new_slots = req_slots - self.free_slots,[object Object],num_insts = new_slots / self.slots_per_instance,[object Object],        # if we need the proxy we might have to force,[object Object],        # launching an instance to host it,[object Object],        if self.proxy and num_insts < 1 br />               and len(self.instances) < 1:,[object Object],num_insts = 1,[object Object],rsv = None,[object Object],        ...,[object Object],        ...,[object Object],        if num_insts > 0:,[object Object],            try:,[object Object],rsv = self.ec2conn.run_instances(self.ami_id,,[object Object],min_count=1, max_count=num_insts,,[object Object],key_name=self.ws_label,,[object Object],security_groups=[self.secgroup.name],,[object Object],instance_type=self.ec2InstanceType),[object Object],            except EC2ResponseError,e:,[object Object],                if ec2.parse_response_error(e, 'Code') == br />u'InstanceLimitExceeded':,[object Object],self.at_instance_limit = True,[object Object],                    print "[%s] Instance limit exceeded" % self.label,[object Object],                else:,[object Object],                    print "[%s] Error running instances:%s" % br />                        (self.label, t5exc.exception()),[object Object],                    raise,[object Object],        if rsv:,[object Object],self._pending_reservations.append(rsv),[object Object],        ...,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Integrating with IaaS,[object Object],Jobs / Nimrod experiment,[object Object],Portal,[object Object],Nimrod-O/E/K,[object Object],Nimrod/G,[object Object],Actuator: Globus,...,[object Object],Services,[object Object],New actuators: EC2, IBM, Azure, OCCI?,...?,[object Object],RESTfulIaaS API,[object Object],Grid Middleware,[object Object],VM,[object Object],Agents,[object Object],Agents,[object Object],VM,[object Object],VM,[object Object],Agents,[object Object],Agents,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Integrating with IaaS,[object Object],Advantage: Nimrod is already a meta-scheduler,[object Object],Creates an ad-hoc grid dynamically overlaying the available resource pool,[object Object],Don’t need Grid-like job processing services to stand-up resource pool,[object Object],Requires explicit management of infrastructure,[object Object],Extra level of scheduling – when to initialise infrastructure?,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Integrating with IaaS,[object Object],1,[object Object],2,[object Object],3,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Application Examples,[object Object],A lot of existing grid based infrastructure,[object Object],So, mix it together,[object Object],“Mixing Grids and Clouds: High-Throughput Science Using the Nimrod Tool Family,” in Cloud Computing, vol. 0 (Springer London, 2010),[object Object],Markov Chain Monte Carlo methods for recommender systems,[object Object],For better results, insert coins here...,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Application Examples,[object Object],Modelling ash dispersion – NG-TEPHRA,[object Object],IEEE e-Science 2010,[object Object],Supplement local infrastructure for deadline sensitive analysis,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Work-in-progress,[object Object],What’s keeping me awake...,[object Object],Spot-price scheduling,[object Object],Smarter data handling,[object Object],Windows support,[object Object],On EC2,[object Object],And integrating with Azure,[object Object],Rose,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Nimrod utilising NeCTAR RC,[object Object],Host MeSsAGE Lab tools,[object Object],Dev and test environment,[object Object],Excess capacity,[object Object],		supporting HTC,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]
Thank you!,[object Object],Presentation by:,[object Object],Blair Bethwaite,[object Object],Researcher, Developer, SysAdmin,[object Object],Monash eScience and Grid Engineering Lab,[object Object],Feedback/queries:,[object Object],blair.bethwaite@monash.edu,[object Object],david.abramson@monash.edu,[object Object],NeCTAR Research Cloud Workshop		     Blair Bethwaite - MeSsAGE Lab, Monash Uni,[object Object]

Más contenido relacionado

La actualidad más candente

Anomaly Detection at Scale
Anomaly Detection at ScaleAnomaly Detection at Scale
Anomaly Detection at ScaleJeff Henrikson
 
Spark Meetup TensorFrames
Spark Meetup TensorFramesSpark Meetup TensorFrames
Spark Meetup TensorFramesJen Aman
 
Real-time Big Data Processing with Storm
Real-time Big Data Processing with StormReal-time Big Data Processing with Storm
Real-time Big Data Processing with Stormviirya
 
Applying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksApplying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksDatabricks
 
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves MabialaDeep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves MabialaSpark Summit
 
Deep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in SparkDeep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in SparkDataWorks Summit/Hadoop Summit
 
CPAC Connectome Analysis in the Cloud
CPAC Connectome Analysis in the CloudCPAC Connectome Analysis in the Cloud
CPAC Connectome Analysis in the CloudCameron Craddock
 
Time Series Analysis for Network Secruity
Time Series Analysis for Network SecruityTime Series Analysis for Network Secruity
Time Series Analysis for Network Secruitymrphilroth
 
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...CERTyou Formation
 
CloudClustering: Toward an Iterative Data Processing Pattern on the Cloud
CloudClustering: Toward an Iterative Data Processing Pattern on the CloudCloudClustering: Toward an Iterative Data Processing Pattern on the Cloud
CloudClustering: Toward an Iterative Data Processing Pattern on the CloudAnkur Dave
 
Image Classification Done Simply using Keras and TensorFlow
Image Classification Done Simply using Keras and TensorFlow Image Classification Done Simply using Keras and TensorFlow
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
 
Computational decision making
Computational decision makingComputational decision making
Computational decision makingBoris Adryan
 
Master's Thesis - climateprediction.net: A Cloudy Approach
Master's Thesis - climateprediction.net: A Cloudy ApproachMaster's Thesis - climateprediction.net: A Cloudy Approach
Master's Thesis - climateprediction.net: A Cloudy Approachkabute
 
1 r029g formation-ibm-cognos-icm-building-reports-v-8-1
1 r029g formation-ibm-cognos-icm-building-reports-v-8-11 r029g formation-ibm-cognos-icm-building-reports-v-8-1
1 r029g formation-ibm-cognos-icm-building-reports-v-8-1CERTyou Formation
 
Weather Station Data Publication at Irstea: an implementation Report.
Weather Station Data Publication at Irstea: an implementation Report.  Weather Station Data Publication at Irstea: an implementation Report.
Weather Station Data Publication at Irstea: an implementation Report. catherine roussey
 
Intro to the Distributed Version of TensorFlow
Intro to the Distributed Version of TensorFlowIntro to the Distributed Version of TensorFlow
Intro to the Distributed Version of TensorFlowAltoros
 

La actualidad más candente (18)

Anomaly Detection at Scale
Anomaly Detection at ScaleAnomaly Detection at Scale
Anomaly Detection at Scale
 
Spark Meetup TensorFrames
Spark Meetup TensorFramesSpark Meetup TensorFrames
Spark Meetup TensorFrames
 
Real-time Big Data Processing with Storm
Real-time Big Data Processing with StormReal-time Big Data Processing with Storm
Real-time Big Data Processing with Storm
 
Applying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksApplying your Convolutional Neural Networks
Applying your Convolutional Neural Networks
 
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves MabialaDeep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala
Deep Recurrent Neural Networks for Sequence Learning in Spark by Yves Mabiala
 
Deep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in SparkDeep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in Spark
 
53
5353
53
 
CPAC Connectome Analysis in the Cloud
CPAC Connectome Analysis in the CloudCPAC Connectome Analysis in the Cloud
CPAC Connectome Analysis in the Cloud
 
Time Series Analysis for Network Secruity
Time Series Analysis for Network SecruityTime Series Analysis for Network Secruity
Time Series Analysis for Network Secruity
 
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...
1 sx5g formation-solutions-cloud-avancees-avec-smartcloud-orchestrator-sur-la...
 
CloudClustering: Toward an Iterative Data Processing Pattern on the Cloud
CloudClustering: Toward an Iterative Data Processing Pattern on the CloudCloudClustering: Toward an Iterative Data Processing Pattern on the Cloud
CloudClustering: Toward an Iterative Data Processing Pattern on the Cloud
 
DIET_BLAST
DIET_BLASTDIET_BLAST
DIET_BLAST
 
Image Classification Done Simply using Keras and TensorFlow
Image Classification Done Simply using Keras and TensorFlow Image Classification Done Simply using Keras and TensorFlow
Image Classification Done Simply using Keras and TensorFlow
 
Computational decision making
Computational decision makingComputational decision making
Computational decision making
 
Master's Thesis - climateprediction.net: A Cloudy Approach
Master's Thesis - climateprediction.net: A Cloudy ApproachMaster's Thesis - climateprediction.net: A Cloudy Approach
Master's Thesis - climateprediction.net: A Cloudy Approach
 
1 r029g formation-ibm-cognos-icm-building-reports-v-8-1
1 r029g formation-ibm-cognos-icm-building-reports-v-8-11 r029g formation-ibm-cognos-icm-building-reports-v-8-1
1 r029g formation-ibm-cognos-icm-building-reports-v-8-1
 
Weather Station Data Publication at Irstea: an implementation Report.
Weather Station Data Publication at Irstea: an implementation Report.  Weather Station Data Publication at Irstea: an implementation Report.
Weather Station Data Publication at Irstea: an implementation Report.
 
Intro to the Distributed Version of TensorFlow
Intro to the Distributed Version of TensorFlowIntro to the Distributed Version of TensorFlow
Intro to the Distributed Version of TensorFlow
 

Similar a Nimrod cloud

Research in Cloud Computing
Research in Cloud ComputingResearch in Cloud Computing
Research in Cloud ComputingRajshri Mohan
 
StackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackStackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackChiradeep Vittal
 
MBrace: Large-scale cloud computation with F# (CUFP 2014)
MBrace: Large-scale cloud computation with F# (CUFP 2014)MBrace: Large-scale cloud computation with F# (CUFP 2014)
MBrace: Large-scale cloud computation with F# (CUFP 2014)Eirik George Tsarpalis
 
Azure machine learning service
Azure machine learning serviceAzure machine learning service
Azure machine learning serviceRuth Yakubu
 
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016Jisc
 
Task Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningTask Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningMLAI2
 
Using Grid Technologies in the Cloud for High Scalability
Using Grid Technologies in the Cloud for High ScalabilityUsing Grid Technologies in the Cloud for High Scalability
Using Grid Technologies in the Cloud for High Scalabilitymabuhr
 
Cloudsim_openstack_aws_lastunit_bsccs_cloud computing
Cloudsim_openstack_aws_lastunit_bsccs_cloud computingCloudsim_openstack_aws_lastunit_bsccs_cloud computing
Cloudsim_openstack_aws_lastunit_bsccs_cloud computingMrSameerSTathare
 
Building a Just-in-Time Application Stack for Analysts
Building a Just-in-Time Application Stack for AnalystsBuilding a Just-in-Time Application Stack for Analysts
Building a Just-in-Time Application Stack for AnalystsAvere Systems
 
Openstack Pakistan Workshop (intro)
Openstack Pakistan Workshop (intro)Openstack Pakistan Workshop (intro)
Openstack Pakistan Workshop (intro)Affan Syed
 
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...Alex Maclinovsky
 
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...
Multicloud Deployment of Computing Clusters for Loosely  Coupled Multi Task C...Multicloud Deployment of Computing Clusters for Loosely  Coupled Multi Task C...
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...IOSR Journals
 
Julien Simon "Scaling ML from 0 to millions of users"
Julien Simon "Scaling ML from 0 to millions of users"Julien Simon "Scaling ML from 0 to millions of users"
Julien Simon "Scaling ML from 0 to millions of users"Fwdays
 
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Amazon Web Services
 
Essel cloud-tecnical
Essel cloud-tecnicalEssel cloud-tecnical
Essel cloud-tecnicalTapas Shome
 
Experiments with Complex Scientific Applications on Hybrid Cloud Infrastructures
Experiments with Complex Scientific Applications on Hybrid Cloud InfrastructuresExperiments with Complex Scientific Applications on Hybrid Cloud Infrastructures
Experiments with Complex Scientific Applications on Hybrid Cloud InfrastructuresRafael Ferreira da Silva
 
Time to Science, Time to Results. Accelerating Scientific research in the Cloud
Time to Science, Time to Results. Accelerating Scientific research in the CloudTime to Science, Time to Results. Accelerating Scientific research in the Cloud
Time to Science, Time to Results. Accelerating Scientific research in the CloudAmazon Web Services
 
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning ServiceViktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning ServiceLviv Startup Club
 

Similar a Nimrod cloud (20)

Research in Cloud Computing
Research in Cloud ComputingResearch in Cloud Computing
Research in Cloud Computing
 
StackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackStackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStack
 
MBrace: Large-scale cloud computation with F# (CUFP 2014)
MBrace: Large-scale cloud computation with F# (CUFP 2014)MBrace: Large-scale cloud computation with F# (CUFP 2014)
MBrace: Large-scale cloud computation with F# (CUFP 2014)
 
Azure machine learning service
Azure machine learning serviceAzure machine learning service
Azure machine learning service
 
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016
 
Task Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningTask Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive Learning
 
Using Grid Technologies in the Cloud for High Scalability
Using Grid Technologies in the Cloud for High ScalabilityUsing Grid Technologies in the Cloud for High Scalability
Using Grid Technologies in the Cloud for High Scalability
 
Concurrent and Distributed CloudSim Simulations
Concurrent and Distributed CloudSim SimulationsConcurrent and Distributed CloudSim Simulations
Concurrent and Distributed CloudSim Simulations
 
Cloudsim_openstack_aws_lastunit_bsccs_cloud computing
Cloudsim_openstack_aws_lastunit_bsccs_cloud computingCloudsim_openstack_aws_lastunit_bsccs_cloud computing
Cloudsim_openstack_aws_lastunit_bsccs_cloud computing
 
FULLTEXT02
FULLTEXT02FULLTEXT02
FULLTEXT02
 
Building a Just-in-Time Application Stack for Analysts
Building a Just-in-Time Application Stack for AnalystsBuilding a Just-in-Time Application Stack for Analysts
Building a Just-in-Time Application Stack for Analysts
 
Openstack Pakistan Workshop (intro)
Openstack Pakistan Workshop (intro)Openstack Pakistan Workshop (intro)
Openstack Pakistan Workshop (intro)
 
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...
Three Degrees of Mediation: Challenges and Lessons in building Cloud-agnostic...
 
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...
Multicloud Deployment of Computing Clusters for Loosely  Coupled Multi Task C...Multicloud Deployment of Computing Clusters for Loosely  Coupled Multi Task C...
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...
 
Julien Simon "Scaling ML from 0 to millions of users"
Julien Simon "Scaling ML from 0 to millions of users"Julien Simon "Scaling ML from 0 to millions of users"
Julien Simon "Scaling ML from 0 to millions of users"
 
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
 
Essel cloud-tecnical
Essel cloud-tecnicalEssel cloud-tecnical
Essel cloud-tecnical
 
Experiments with Complex Scientific Applications on Hybrid Cloud Infrastructures
Experiments with Complex Scientific Applications on Hybrid Cloud InfrastructuresExperiments with Complex Scientific Applications on Hybrid Cloud Infrastructures
Experiments with Complex Scientific Applications on Hybrid Cloud Infrastructures
 
Time to Science, Time to Results. Accelerating Scientific research in the Cloud
Time to Science, Time to Results. Accelerating Scientific research in the CloudTime to Science, Time to Results. Accelerating Scientific research in the Cloud
Time to Science, Time to Results. Accelerating Scientific research in the Cloud
 
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning ServiceViktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
 

Último

Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfinfogdgmi
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Websitedgelyza
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaborationbruanjhuli
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 

Último (20)

20230104 - machine vision
20230104 - machine vision20230104 - machine vision
20230104 - machine vision
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdf
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Website
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
201610817 - edge part1
201610817 - edge part1201610817 - edge part1
201610817 - edge part1
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 

Nimrod cloud

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.

Notas del editor

  1. Please ask questions during the talk if you have them.
  2. Simple pleasingly-parallel computing for “legacy” (misnomer: just need existing app, Nimrod is the distributed glue that launches and contextualises each job). Onclusters, compute grids, and now clouds.Also support computational economy via economic scheduling.
  3. Molecular docking in drug designEngineering antennae for maximum gainAirfoil optimising LD ratio
  4. Original Nimrod also acted as the cluster management system, commercial spin-off to Enfuzion.
  5. Nimrod/G – “G” originally stood for Globus but now more general supporting other lower level middleware, such as Condor.
  6. Then AWS came along... suddenly public utility computing became a realityOn demand: start and stop machines any time, lead time of minutes.Self service: no lengthy email trail with your data centre admin, just make a web service call.PAYG: pay for what you use, tear it down when not needed.Think of it as a computational vending machine.
  7. Code snippet from Nimrod EC2 actuator – bringing up your first few machines like this is cool! And incredibly easy with these APIs, and great tools like Boto.
  8. Actuator model makes this integration relatively painless compared to an app highly dependent on higher Grid middleware functions.
  9. Clouds provide an interesting infrastructure to supplement the usual resources available for academic computing.You can pay to get your results faster, or make them higher quality.
  10. Probabilistic spatial and density distribution mapping of volcanic tephra, potentially useful in time sensitive scenarios, i.e., immediately preceding or following an eruption event.