This document provides an introduction to cloud computing through 5 parts:
1. Defines clouds and key aspects like scale, ease of use, and pricing models.
2. Describes the cloud computing industry ecosystem including various cloud types, economic models, and applications.
3. Explains virtualization and how it increases flexibility and utilization.
4. Compares clouds for data versus supercomputers and databases, noting clouds trade functionality for scalability.
5. Discusses several standards efforts aimed at interoperability between cloud services.
The Open Science Data Cloud (OSDC) is a non-profit consortium that manages cloud computing infrastructure to support scientific research. It operates multiple clouds with thousands of nodes and petabytes of storage across four data centers. The OSDC supports various scientific projects involving astronomical, biological, and networking data as well as image processing. Its goals are to provide open, interoperable infrastructure at scale to enable large-scale scientific experiments and to preserve research data long-term like libraries preserve books. The OSDC is working on various technical challenges around data migration, virtual networking standards, and finding sustainable business models.
Introduction to Big Data and Science Clouds (Chapter 1, SC 11 Tutorial)Robert Grossman
This document provides an introduction to data intensive computing. It discusses how advances in instruments are producing massive amounts of data, creating new paradigms of "data intensive science" and computing. It also discusses how utility clouds like Amazon and data clouds are addressing this challenge by providing on-demand access to vast computing resources and data storage at large scale. The document outlines different models for responsibility between cloud service providers and customers.
This document provides an outline for a talk on cloud computing. It begins with an introduction to cloud concepts and technologies like virtualization and parallel computing models. It then discusses different cloud models including IaaS, PaaS and SaaS. The outline includes demonstrations of cloud capabilities with Amazon AWS and Microsoft Azure, as well as data and computing models using MapReduce. It concludes with a case study of a real business application of the cloud and a question and answer section.
This document discusses cloud computing and CloudStack. It begins with definitions of cloud computing and describes its basic layers and architecture. It then covers the evolution of cloud computing from earlier concepts like grid computing and utility computing. Different cloud solutions are presented, with CloudStack discussed in more depth including its architecture and components. The document concludes with sections on research areas related to cloud computing and references.
The Pandemic Changes Everything, the Need for Speed and ResiliencyAlluxio, Inc.
This document discusses how the COVID-19 pandemic has accelerated the need for cloud computing and digital transformation. Some key points:
- By 2021, over 90% of organizations will rely on a mix of on-premises, private clouds, public clouds, and legacy systems to meet infrastructure needs.
- By 2023, an emerging cloud ecosystem for extending resource control and analytics will underlie all IT and business automation initiatives anywhere.
- Resilient business models and superior customer experience will be critical as organizations shift more operations and services to the cloud.
What we Learned About Application Resiliency When the Data Center Burned DownScyllaDB
Is your data infrastructure architected to withstand anything —including a disaster suddenly taking down an entire data center? When a cloud provider’s data center burned to the ground, 3.6 million websites went dark. But one leading travel service kept running without a hitch, thanks to the design of their environment-aware distributed database. Learn how they architected their data infrastructure for extreme resiliency, how their strategy held up and what lessons they learned.
This document provides an introduction to cloud computing through 5 parts:
1. Defines clouds and key aspects like scale, ease of use, and pricing models.
2. Describes the cloud computing industry ecosystem including various cloud types, economic models, and applications.
3. Explains virtualization and how it increases flexibility and utilization.
4. Compares clouds for data versus supercomputers and databases, noting clouds trade functionality for scalability.
5. Discusses several standards efforts aimed at interoperability between cloud services.
The Open Science Data Cloud (OSDC) is a non-profit consortium that manages cloud computing infrastructure to support scientific research. It operates multiple clouds with thousands of nodes and petabytes of storage across four data centers. The OSDC supports various scientific projects involving astronomical, biological, and networking data as well as image processing. Its goals are to provide open, interoperable infrastructure at scale to enable large-scale scientific experiments and to preserve research data long-term like libraries preserve books. The OSDC is working on various technical challenges around data migration, virtual networking standards, and finding sustainable business models.
Introduction to Big Data and Science Clouds (Chapter 1, SC 11 Tutorial)Robert Grossman
This document provides an introduction to data intensive computing. It discusses how advances in instruments are producing massive amounts of data, creating new paradigms of "data intensive science" and computing. It also discusses how utility clouds like Amazon and data clouds are addressing this challenge by providing on-demand access to vast computing resources and data storage at large scale. The document outlines different models for responsibility between cloud service providers and customers.
This document provides an outline for a talk on cloud computing. It begins with an introduction to cloud concepts and technologies like virtualization and parallel computing models. It then discusses different cloud models including IaaS, PaaS and SaaS. The outline includes demonstrations of cloud capabilities with Amazon AWS and Microsoft Azure, as well as data and computing models using MapReduce. It concludes with a case study of a real business application of the cloud and a question and answer section.
This document discusses cloud computing and CloudStack. It begins with definitions of cloud computing and describes its basic layers and architecture. It then covers the evolution of cloud computing from earlier concepts like grid computing and utility computing. Different cloud solutions are presented, with CloudStack discussed in more depth including its architecture and components. The document concludes with sections on research areas related to cloud computing and references.
The Pandemic Changes Everything, the Need for Speed and ResiliencyAlluxio, Inc.
This document discusses how the COVID-19 pandemic has accelerated the need for cloud computing and digital transformation. Some key points:
- By 2021, over 90% of organizations will rely on a mix of on-premises, private clouds, public clouds, and legacy systems to meet infrastructure needs.
- By 2023, an emerging cloud ecosystem for extending resource control and analytics will underlie all IT and business automation initiatives anywhere.
- Resilient business models and superior customer experience will be critical as organizations shift more operations and services to the cloud.
What we Learned About Application Resiliency When the Data Center Burned DownScyllaDB
Is your data infrastructure architected to withstand anything —including a disaster suddenly taking down an entire data center? When a cloud provider’s data center burned to the ground, 3.6 million websites went dark. But one leading travel service kept running without a hitch, thanks to the design of their environment-aware distributed database. Learn how they architected their data infrastructure for extreme resiliency, how their strategy held up and what lessons they learned.
Modernizing Global Shared Data Analytics Platform and our Alluxio JourneyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Sandipan Chakraborty, Director of Engineering (Rakuten)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Learn about TerraEchos Kairos on IBM PowerLinux servers. A world leader in stream computing harnesses the power of real-time associative analytics for extreme
workflow optimization in the big data arena. For more information on Power Systems, visit http://ibm.co/Lx6hfc.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
OCCIware: extensible and standard-based XaaS platform to manage everything in...OCCIware
This document discusses OCCIware, an open source platform for managing cloud resources using the Open Cloud Computing Interface (OCCI) standard. It introduces OCCIware Studio for designing, simulating, and developing cloud applications and services, and the OCCIware Runtime for deploying and managing those services. It then demonstrates OCCIware's capabilities for linked data analytics as a service using Docker Studio and a MongoDB cluster. Upcoming work on OCCIware includes improvements to Studio, integration with cloud management tools, and further use cases involving data centers, big data, and linked data.
CREODIAS is a leading cloud platform for processing Earth observation data. It offers cloud services, tools, and storage for processing and disseminating satellite data. CREODIAS integrates data from over 20 sources and allows users to process data using virtual machines, containers, and serverless functions. It provides tools for finding, accessing, and visualizing data as well as developing applications.
OCRE Workshop: Shaping the Earth Observation Services Market for Research. Session 3: Presentations from DIAS and eoMALL.
This workshop aims to bring the EO service providers closer to the research community, capture their needs and develop fit for purpose EO services.
The event will be the 4th OCRE Requirements Gathering Workshop. Researchers and Earth Observation Service Providers will be asked to provide inputs to help us shape OCRE's tender.
The OCRE project aims to provide the first end-to-end instance of organised, large-scale market pull for EO services in Europe. These services will be provided for free to EU researchers through the European Open Science Cloud. To ensure that the services meet the actual needs of the research community we invite both the demand and the supply side, to share their views and engage in a productive dialogue. Our aim is to capture the needs of EU researchers and inform the EO service providers so that they make available services that effectively address them. We will also explain how the OCRE process will work, how the different stakeholders should be involved and how to make the most of the foreseen benefits.
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Mundi Presentation - A Space of New Opportunitiesplan4all
This document provides an overview of Mundi, an Atos DIAS (Data and Information Access Service) platform. It discusses how Mundi provides simple access to Copernicus and other satellite data through cloud and big data technologies. The document outlines Mundi's offerings, including Jupyter notebooks, APIs, data formats, historical data access, on-demand processing, and its growing marketplace. Users are invited to create an account, explore the marketplace and data, and try Mundi.
Different data types, operational efficiencies, and variable workloads are driving the convergence of data platforms. A converged data platform combines technologies like Hadoop, Spark, streaming, and databases on a single platform with centralized management. This reduces costs and improves reliability compared to separate data silos. Major vendors like MapR are offering converged data platforms that provide real-time processing, multi-model databases, and integration of streaming and batch workloads. Widespread adoption of converged data platforms is expected to continue as businesses seek improved data management and analytics capabilities.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Future of Computing is Distributed
Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Orchestrate a Data Symphony
Speaker:
Haoyuan Li, Alluxio
For more Alluxio events: https://www.alluxio.io/events/
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Cloud computing represents a new approach to addressing scalability problems by providing reusable infrastructure components that organizations can use to build applications that can rapidly scale to large volumes of data. The amount of data generated is growing exponentially from a variety sources and far exceeds what a single computer can process. Frameworks like Hadoop provide a scalable and reliable way to process vast amounts of data across many computers working in parallel by distributing data and computation automatically. This allows organizations to efficiently gain insights from large datasets.
MapR Technologies Chief Marketing Officer, Jack Norris, talks about the advantages of Hadoop. He elaborates and multiple use cases and explains how MapR Technologies is the best Hadoop distribution.
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016Jisc
In Jisc's future of cloud computing horizon scan report, we identified three strategic areas where Jisc could support universities and colleges in moving to the cloud – cloud as a utility, app as a service, and working to build capability in cloud technologies.
Come along to this session to hear more about this work from Jisc futurist Martin Hamilton, and find out how you can get involved.
TierraCloud's HC2 open-source project aims to enable enterprise-class private cloud storage using standard x86 servers and sophisticated software. This allows 10x lower total cost of ownership compared to traditional storage solutions while providing scalability to billions of objects and petabytes of capacity. The current beta release supports object storage and retrieval with S3 and HTTP APIs, metadata storage and querying, and background data integrity checking across a minimum of 8 or 16 servers. The technology is based on Sun's Project Honeycomb and has received praise from analysts and universities for its potential to revolutionize data management and archival storage.
Big data analytics in the cloud allows companies to extract value from vast amounts of data. By leveraging cloud computing infrastructure, businesses can analyze customer behavior patterns, optimize operations, and gain insights faster at lower costs compared to on-premise data centers. The cloud provides massive scalability, advanced analytics tools, and pay-as-you-go pricing that enables organizations to efficiently process big data and make data-driven decisions.
The VINEYARD project aims to increase the performance and energy efficiency of data centers through the use of heterogeneous hardware accelerators like programmable dataflow engines and FPGA-accelerated servers. The project will develop these novel accelerators and integrate them into the data center infrastructure with an open programming framework and runtime scheduler. This will allow big data applications to leverage the accelerators while hiding the complexity from programmers. The goals are demonstrated through applications in computational neuroscience, finance, data analytics, and IoT.
Webinar: Learn How To Deploy High-Scale, Low-Latency Cost-Efficient Solutions...BTI Systems
In this webinar, Chandra Pandey, VP of Platform Solutions, and Joel Daly, Director of Solutions Marketing, will discuss how BTI™ Intelligent Packet Optical Solutions enable massive scalability with ultra low latency, accelerate service delivery with high availability while reducing capital and operational costs.
HPC Cloud: Clouds on supercomputers for HPCRyousei Takano
- HPC Cloud is a promising platform that can provide high performance, energy efficiency, scalability, and usability for HPC workloads. It utilizes technologies like VMM-bypass I/O, hybrid live migration, and virtual cluster migration to minimize performance overhead.
- The AIST has integrated these technologies into their HPC Cloud OS and Apache CloudStack to provide bare-metal-comparable I/O performance within a cloud environment. This allows HPC workloads and applications to efficiently utilize cloud infrastructures.
- The HPC Cloud federation concept allows VM images to be easily shared between different cloud systems. This achieves large-scale utilization of computing resources by leveraging supercomputers across
MS TechDays 2011 - Cloud Computing with the Windows Azure PlatformSpiffy
This document provides an overview of the Windows Azure cloud computing platform. It discusses the types of cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It then describes several key Windows Azure services like Compute, Storage, Database, Content Delivery Network, Reporting, Virtual Machines, Service Bus, Access Control, Caching, Virtual Network, and Marketplace. The presentation encourages Singapore companies using Windows Azure to contact Microsoft to have their applications featured. It concludes with inviting questions from attendees.
Modernizing Global Shared Data Analytics Platform and our Alluxio JourneyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Sandipan Chakraborty, Director of Engineering (Rakuten)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Learn about TerraEchos Kairos on IBM PowerLinux servers. A world leader in stream computing harnesses the power of real-time associative analytics for extreme
workflow optimization in the big data arena. For more information on Power Systems, visit http://ibm.co/Lx6hfc.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
OCCIware: extensible and standard-based XaaS platform to manage everything in...OCCIware
This document discusses OCCIware, an open source platform for managing cloud resources using the Open Cloud Computing Interface (OCCI) standard. It introduces OCCIware Studio for designing, simulating, and developing cloud applications and services, and the OCCIware Runtime for deploying and managing those services. It then demonstrates OCCIware's capabilities for linked data analytics as a service using Docker Studio and a MongoDB cluster. Upcoming work on OCCIware includes improvements to Studio, integration with cloud management tools, and further use cases involving data centers, big data, and linked data.
CREODIAS is a leading cloud platform for processing Earth observation data. It offers cloud services, tools, and storage for processing and disseminating satellite data. CREODIAS integrates data from over 20 sources and allows users to process data using virtual machines, containers, and serverless functions. It provides tools for finding, accessing, and visualizing data as well as developing applications.
OCRE Workshop: Shaping the Earth Observation Services Market for Research. Session 3: Presentations from DIAS and eoMALL.
This workshop aims to bring the EO service providers closer to the research community, capture their needs and develop fit for purpose EO services.
The event will be the 4th OCRE Requirements Gathering Workshop. Researchers and Earth Observation Service Providers will be asked to provide inputs to help us shape OCRE's tender.
The OCRE project aims to provide the first end-to-end instance of organised, large-scale market pull for EO services in Europe. These services will be provided for free to EU researchers through the European Open Science Cloud. To ensure that the services meet the actual needs of the research community we invite both the demand and the supply side, to share their views and engage in a productive dialogue. Our aim is to capture the needs of EU researchers and inform the EO service providers so that they make available services that effectively address them. We will also explain how the OCRE process will work, how the different stakeholders should be involved and how to make the most of the foreseen benefits.
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Mundi Presentation - A Space of New Opportunitiesplan4all
This document provides an overview of Mundi, an Atos DIAS (Data and Information Access Service) platform. It discusses how Mundi provides simple access to Copernicus and other satellite data through cloud and big data technologies. The document outlines Mundi's offerings, including Jupyter notebooks, APIs, data formats, historical data access, on-demand processing, and its growing marketplace. Users are invited to create an account, explore the marketplace and data, and try Mundi.
Different data types, operational efficiencies, and variable workloads are driving the convergence of data platforms. A converged data platform combines technologies like Hadoop, Spark, streaming, and databases on a single platform with centralized management. This reduces costs and improves reliability compared to separate data silos. Major vendors like MapR are offering converged data platforms that provide real-time processing, multi-model databases, and integration of streaming and batch workloads. Widespread adoption of converged data platforms is expected to continue as businesses seek improved data management and analytics capabilities.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Future of Computing is Distributed
Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Orchestrate a Data Symphony
Speaker:
Haoyuan Li, Alluxio
For more Alluxio events: https://www.alluxio.io/events/
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Cloud computing represents a new approach to addressing scalability problems by providing reusable infrastructure components that organizations can use to build applications that can rapidly scale to large volumes of data. The amount of data generated is growing exponentially from a variety sources and far exceeds what a single computer can process. Frameworks like Hadoop provide a scalable and reliable way to process vast amounts of data across many computers working in parallel by distributing data and computation automatically. This allows organizations to efficiently gain insights from large datasets.
MapR Technologies Chief Marketing Officer, Jack Norris, talks about the advantages of Hadoop. He elaborates and multiple use cases and explains how MapR Technologies is the best Hadoop distribution.
Cloud present, future and trajectory (Amazon Web Services) - JIsc Digifest 2016Jisc
In Jisc's future of cloud computing horizon scan report, we identified three strategic areas where Jisc could support universities and colleges in moving to the cloud – cloud as a utility, app as a service, and working to build capability in cloud technologies.
Come along to this session to hear more about this work from Jisc futurist Martin Hamilton, and find out how you can get involved.
TierraCloud's HC2 open-source project aims to enable enterprise-class private cloud storage using standard x86 servers and sophisticated software. This allows 10x lower total cost of ownership compared to traditional storage solutions while providing scalability to billions of objects and petabytes of capacity. The current beta release supports object storage and retrieval with S3 and HTTP APIs, metadata storage and querying, and background data integrity checking across a minimum of 8 or 16 servers. The technology is based on Sun's Project Honeycomb and has received praise from analysts and universities for its potential to revolutionize data management and archival storage.
Big data analytics in the cloud allows companies to extract value from vast amounts of data. By leveraging cloud computing infrastructure, businesses can analyze customer behavior patterns, optimize operations, and gain insights faster at lower costs compared to on-premise data centers. The cloud provides massive scalability, advanced analytics tools, and pay-as-you-go pricing that enables organizations to efficiently process big data and make data-driven decisions.
The VINEYARD project aims to increase the performance and energy efficiency of data centers through the use of heterogeneous hardware accelerators like programmable dataflow engines and FPGA-accelerated servers. The project will develop these novel accelerators and integrate them into the data center infrastructure with an open programming framework and runtime scheduler. This will allow big data applications to leverage the accelerators while hiding the complexity from programmers. The goals are demonstrated through applications in computational neuroscience, finance, data analytics, and IoT.
Webinar: Learn How To Deploy High-Scale, Low-Latency Cost-Efficient Solutions...BTI Systems
In this webinar, Chandra Pandey, VP of Platform Solutions, and Joel Daly, Director of Solutions Marketing, will discuss how BTI™ Intelligent Packet Optical Solutions enable massive scalability with ultra low latency, accelerate service delivery with high availability while reducing capital and operational costs.
HPC Cloud: Clouds on supercomputers for HPCRyousei Takano
- HPC Cloud is a promising platform that can provide high performance, energy efficiency, scalability, and usability for HPC workloads. It utilizes technologies like VMM-bypass I/O, hybrid live migration, and virtual cluster migration to minimize performance overhead.
- The AIST has integrated these technologies into their HPC Cloud OS and Apache CloudStack to provide bare-metal-comparable I/O performance within a cloud environment. This allows HPC workloads and applications to efficiently utilize cloud infrastructures.
- The HPC Cloud federation concept allows VM images to be easily shared between different cloud systems. This achieves large-scale utilization of computing resources by leveraging supercomputers across
MS TechDays 2011 - Cloud Computing with the Windows Azure PlatformSpiffy
This document provides an overview of the Windows Azure cloud computing platform. It discusses the types of cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It then describes several key Windows Azure services like Compute, Storage, Database, Content Delivery Network, Reporting, Virtual Machines, Service Bus, Access Control, Caching, Virtual Network, and Marketplace. The presentation encourages Singapore companies using Windows Azure to contact Microsoft to have their applications featured. It concludes with inviting questions from attendees.
Windows Azure David Chappell White Paper March 09guest120d945
This document introduces Windows Azure, a platform for building and hosting scalable cloud applications and services. It provides an overview of the main components of Windows Azure, including the Compute service for running applications, the Storage service for persistent data, and the underlying Fabric for management. It then discusses scenarios for using Windows Azure, such as creating scalable web applications, parallel processing applications, and applications with background processing. Finally, it examines the components in more detail regarding development, the compute and storage services, and the fabric.
This document contains a presentation on cloud computing concepts and Microsoft Azure. It discusses what cloud computing is, examples of cloud architectures like processing pipelines and websites, benefits of cloud computing like reduced costs and scalability, and an overview of Microsoft Azure including its features and how to deploy applications to Azure.
The document provides an overview of Microsoft's Azure Services Platform, which includes four main components: Windows Azure, .NET Services, SQL Services, and Live Services. Windows Azure provides a platform for building and hosting applications in the cloud, .NET Services offers distributed infrastructure services, SQL Services provides data storage and services in the cloud, and Live Services allows accessing and synchronizing data from Microsoft's online applications.
2011.05.31 super mondays-servicebus-demodaveingham
Presentation by David Ingham demonstrating the messaging features (queues, topics) of Windows Azure AppFabric Service Bus. Given at SuperMondays, Gateshead, UK on May 31, 2011.
The document introduces warehouse-scale computers (WSCs), which are the computing platforms that power large Internet services like search engines and social networks. WSCs consist of thousands of computing nodes, networking equipment, storage systems, and extensive cooling and power infrastructure housed in large buildings. They differ from traditional datacenters in that they belong to a single organization, use homogeneous hardware and software platforms, and share resource management to run a small number of very large Internet services rather than many smaller applications. The document provides an overview of the key architectural components of WSCs and how their design is optimized for cost efficiency at massive scale.
Optimize your IT network infrastructure environment with the industry's leading business technology analytics platform from RISC Networks. IT HealthCheck is a turn-key, software-as-a-service (SaaS) business technology analytics platform that helps you significantly improve the efficiency, agility and resiliency of your IT network infrastructure.
RISC Networks CloudScape simplifies cloud migration planning through a process of discovery, analysis, and migration. It uses intelligent application grouping to understand complex application dependencies and segment workloads by location and function. CloudScape analyzes applications to identify migration drivers and issues. It optimizes cloud pricing across 15+ vendors and provisions resources while factoring in storage, network I/O, and true costs. Migration plans can then be exported and executed, including full network connectivity requirements.
Power Comparison Power Comparison of Cloud Data of Cloud Data Center Architec...Paolo Giaccone
Power consumption is a primary concern for cloud computing data centers. Being the network one of the non- negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of im- proving network performance and being energy-efficient. In this paper we provide a comparison study of data center architectures, covering both classical two- and three-tier design and state-of- art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies.
An introduction to the Design of Warehouse-Scale ComputersAlessio Villardita
A brief overview of the main factors involved in the design of Warehouse-Scale Computers (WSC), from the hardware, to the cooling system to the overall plant energy efficiency, always keeping in mind the costs of such a big architecture.
Co-Author: Pietro Piscione (https://www.linkedin.com/pub/pietro-piscione/84/b37/926)
A work based on:
"The Datacenter as a Computer, An Introduction to the Design of Warehouse-Scale Machines, Second Edition"
by
Luiz André Barroso
Jimmy Clidaras
Urs Hölzle
This was presented at 2009 Web World Conference.
The presentation analyzes some trends of cloud computing, and prospects the futures of cloud computing.
The document provides an overview of several data center network architectures: Monsoon, VL2, SEATTLE, PortLand, and TRILL. Monsoon proposes a large layer 2 domain with a Clos topology and uses MAC-in-MAC encapsulation and load balancing to improve scalability. VL2 also uses a Clos topology with flat addressing, load balancing, and an end host directory for address resolution. SEATTLE employs flat addressing, automated host discovery, and hash-based address resolution. PortLand uses a tree topology with encoded switch positions and a fabric manager for address mapping. TRILL standardizes encapsulation and IS-IS routing between routing bridges.
This document provides information about Infrastructure as a Service (IaaS) offerings from various cloud providers. It includes a table comparing features of services from VMware vCloud Air, SoftLayer, Microsoft Azure, Ingram Micro VPS, Amazon Web Services, 365 Data Center, Intermedia Private Label Cloud, and dinCloud Business Provisioning. It also includes sections that provide more detailed information about IaaS offerings and contact information for inquires.
4 Ways To Save Big Money in Your Data Center and Private Cloudtervela
The thirst for real-time access to rich content and big data is turning enterprise datacenters into private computing clouds. However, making exabyte-scale data available and responsive to a global application network gets expensive. Fortunately there are things you can do to save big money in these sophisticated new environments. In this presentation you will learn how to save money, avoid costs, and create significant efficiencies in your private cloud by: Consolidating databases and data warehouses, Slashing big data storage and storage-based data replication , Replacing expensive middleware, and Eliminating cold disaster recovery
Open Cloud Consortium: An Update (04-23-10, v9)Robert Grossman
The Open Cloud Consortium (OCC) is a non-profit organization that supports the development of cloud computing standards and technologies. It manages several testbeds and working groups focused on areas like large data clouds, interoperability, and disaster relief applications. The document provides updates on the OCC's Intercloud Testbed, which aims to address gaps in cloud standards, as well as its Open Cloud Testbed which offers resources to members through a "condominium cloud" model.
Cloud Computing Standards and Use Cases (Robert Grossman) 09-v8pRobert Grossman
This document provides an overview of cloud computing standards organizations and use cases. It begins with definitions of cloud computing and describes early use cases like migrating applications between clouds without changes. It then outlines several standards bodies and their focuses, such as the Distributed Management Task Force working on virtual machine portability and the Storage Networking Industry Association developing a cloud data management interface. Finally, it presents additional use cases such as moving large data applications between cloud storage and compute services and sharing information across clouds with security requirements.
Cloud computing and grid computing 360 degree comparedMd. Hasibur Rashid
Cloud computing builds upon concepts from cluster and grid computing. Cluster computing links multiple computers to share workloads, while grid computing dynamically aggregates distributed resources for tasks. Cloud computing provides scalable resources and services over the internet. It extends concepts from grid computing by offering virtualized, dynamically provisioned resources on-demand. Key differences are that cloud computing has loose coupling between providers and consumers, supports scaling, and offers services under a pay-per-use business model. Common cloud services are SaaS, PaaS, and IaaS. Challenges include dynamic scalability, security, and standardization. Cloud computing shows promise for further research in areas like security, interoperability and dynamic pricing models.
The Open Cloud Consortium (OCC) is a non-profit organization that supports cloud computing standards and develops testbeds for interoperability. It has members from companies, universities, and government agencies. The OCC manages the Open Cloud Testbed, Intercloud Testbed, and Open Science Data Cloud. It also has working groups focused on large data clouds, applications, and cloud services. The Intercloud Testbed aims to address gaps in linking infrastructure and platform services. Benchmarks like Gray Sort and MalStone are used to evaluate large data cloud performance. The Open Cloud Testbed provides shared cloud resources through a "condominium cloud" model. The Open Science Data Cloud hosts scientific data sets for research.
Lecture #6 - ET-3010
Cloud Computing - Overview and Examples
Connected Services and Cloud Computing
School of Electrical Engineering and Informatics SEEI / STEI
Institut Teknologi Bandung ITB
Update April 2017
Data centers are large physical facilities that house computing infrastructure for enterprises. They provide utilities like power, cooling, security and shelter for servers and storage equipment. Modern data centers are designed with regions and availability zones for fault tolerance, with each zone consisting of one or more data centers within close network proximity. Key challenges for data centers include efficient cooling of equipment, improving energy proportionality as servers are often idle, optimizing resource utilization through virtualization and dynamic allocation, and managing the immense scale of infrastructure and traffic as cloud providers operate millions of servers globally.
Cyberinfrastructure and Applications Overview: Howard University June22marpierc
1) Cyberinfrastructure refers to the combination of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people that enable knowledge discovery through integrated multi-scale simulations and analyses.
2) Cloud computing, multicore processors, and Web 2.0 tools are changing the landscape of cyberinfrastructure by providing new approaches to distributed computing and data sharing that emphasize usability, collaboration, and accessibility.
3) Scientific applications are increasingly data-intensive, requiring high-performance computing resources to analyze large datasets from sources like gene sequencers, telescopes, sensors, and web crawlers.
Lessons Learned from a Year's Worth of Benchmarking Large Data Clouds (Robert...Robert Grossman
The document summarizes Sector, an open-source large data cloud computing platform, and compares it to Hadoop. Sector uses a file-based storage system instead of Hadoop's block-based HDFS, and features a more flexible UDF programming model compared to MapReduce. Benchmark results show Sector outperforming Hadoop on the Terasort and MalStone benchmarks, with speedups of up to 19x, due to its dataflow balancing, UDP-based transport, and other architectural advantages over Hadoop for data-intensive computing at scale. Lessons learned include the importance of data locality, load balancing, and fault tolerance in large-scale systems.
Spatial data infrastructure in the cloud, 2011Moullet
The document discusses spatial data infrastructures (SDIs) and the potential advantages and disadvantages of using cloud computing for SDIs. Some key points:
- An SDI is a framework of spatial data, metadata, users and tools that allow for efficient and flexible use of spatial data.
- Cloud computing provides on-demand access to configurable computing resources over a network. There are different service and deployment models.
- Potential advantages of cloud computing for SDIs include scalability, pay-as-you-go costs, and not needing dedicated servers. However, organizations need to keep control of important components and ensure security and privacy.
- Switzerland's Federal Office of Topography uses an infrastructure
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
This document discusses cloud computing concepts and applications in a military context. It defines cloud computing and describes common cloud themes like scalability, on-demand access, and location independence. It outlines business benefits like automation, data intensive computing, and accessibility from any device. The document also discusses DISA's focus on infrastructure/platform capabilities and lists several of DISA's cloud-related efforts.
This document provides an overview of cloud computing. It begins with learning objectives and defines cloud computing according to NIST as a model for enabling network access to a shared pool of configurable computing resources that can be rapidly provisioned with minimal management effort. It describes the five essential cloud characteristics, three service models (SaaS, PaaS, IaaS), and four deployment models (private, public, hybrid, community). Examples are given for each along with issues and benefits of cloud computing. The document provides a comprehensive introduction to cloud computing concepts.
Cloud computing is the natural evolution of computing where resources are provided as a service over the internet. There are different deployment models and types of cloud services including infrastructure as a service, platform as a service, and software as a service. Popular cloud frameworks include Google AppEngine, PubNub, and Jclouds which provide development platforms and services for storage, databases, and notifications in the cloud.
Fundamental question and answer in cloud computing quiz by animesh chaturvediAnimesh Chaturvedi
The document contains questions and answers related to a cloud computing exam. It includes 5 questions worth 5 marks each on topics like the 2013 ACM Turing Award winner and their contributions to distributed systems and cloud computing, different cloud computing models, data transfer methods, descriptions of Google File System and Hadoop Distributed File System, and architectures for Hadoop on Google Cloud Platform and web applications on Google App Engine. The answers to the questions are provided in slides within the linked website.
Cloud computing is a general term for services and infrastructure that are hosted remotely over the internet. It allows users to access computing resources and data storage on demand from any device. Key characteristics include pay-as-you-go pricing, ubiquitous network access, and elastic scalability. Cloud services can be categorized as infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS). Major advantages include lower costs, easier collaboration, automatic updates, and unlimited storage. Disadvantages include reliance on internet connectivity and potential security and performance issues.
Similar to My Other Computer is a Data Center (2010 v21) (20)
Some Frameworks for Improving Analytic Operations at Your CompanyRobert Grossman
I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
This a talk that I gave at BioIT World West on March 12, 2019. The talk was called: A Gen3 Perspective of Disparate Data:From Pipelines in Data Commons to AI in Data Ecosystems.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
This is an overview of the Data Biosphere Project, its goals, its architecture, and the three core projects that form its foundation. We also discuss data commons.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
1. Data commons co-locate large biomedical datasets with cloud computing infrastructure and analysis tools to create shared resources for the research community.
2. The NCI Genomic Data Commons is an example of a data commons that makes over 2.5 petabytes of cancer genomics data available through web portals, APIs, and harmonized analysis pipelines.
3. The Gen3 platform is an open source software stack for building data commons that can interoperate through common APIs and data models to support reproducible, collaborative research across projects.
How Data Commons are Changing the Way that Large Datasets Are Analyzed and Sh...Robert Grossman
Data commons are emerging as a solution to challenges in analyzing and sharing large biomedical datasets. A data commons co-locates data with cloud computing infrastructure and software tools to create an interoperable resource for the research community. Examples include the NCI Genomic Data Commons and the Open Commons Consortium. The open source Gen3 platform supports building disease- or project-specific data commons to facilitate open data sharing while protecting patient privacy. Developing interoperable data commons can accelerate research through increased access to data.
This document discusses best practices for deploying analytic models from development environments into operational systems. It describes how modeling environments often use different languages than deployment environments, requiring significant effort to move models. The document outlines the life cycle of analytic models, from exploratory data analysis to model deployment and monitoring. It also discusses standards like PMML and PFA that can be used to export models between different applications and analytic engines that integrate models into operational workflows.
This document discusses big data and analytics, outlining five trends and five research challenges. It begins by defining big data in terms of volume, velocity, variety, veracity and value. It then discusses the origins and evolution of big data, from early statistics to modern data science. Analytics is defined as using data to make empirically-derived, statistically valid decisions. The document outlines how hardware choices led to scaling out data processing across clusters rather than scaling up on single machines. It also provides examples of fields that generate huge volumes of data from billion dollar instruments like CERN's Large Hadron Collider and genomic sequencing facilities.
AnalyticOps: Lessons Learned Moving Machine-Learning Algorithms to Production...Robert Grossman
The document discusses lessons learned from moving machine learning algorithms to production environments, referred to as "AnalyticOps". It introduces AnalyticOps as establishing an environment where building, validating, deploying, and running analytic models happens rapidly, frequently, and reliably. A key challenge is deploying analytic models into operations, products, and services. The document discusses strategies for deploying models, including scoring engines that integrate analytic models into operational workflows using a model interchange format. It provides two case studies as examples.
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
Practical Methods for Identifying Anomalies That Matter in Large DatasetsRobert Grossman
This document summarizes four approaches to identifying anomalies in large datasets: 1) statistical modeling of populations, 2) identifying clusters and distances of outliers from clusters, 3) examining neighborhoods and densities, and 4) ranking and packaging candidate anomalies for expert review. It also provides a case study on detecting active voxels in fMRI data from a salmon's brain during a mentalizing task. Several active voxels were found in a cluster in the brain, but the resolution was too coarse to identify specific brain regions.
Biomedical Clusters, Clouds and Commons - DePaul Colloquium Oct 24, 2014Robert Grossman
This document discusses how biomedical discovery is being disrupted by big data. Large genomic, phenotype, and environmental datasets are needed to understand complex diseases that result from combinations of many rare variants. However, analyzing large biomedical data is costly and difficult given the standard model of local computing. The document proposes creating large "commons" of community data and computing as an instrument for big data discovery. Examples are given of the Cancer Genome Atlas project, which has petabytes of research data on thousands of cancer patients, and how tumors evolve over time. Overall, the document argues that new models of shared biomedical clouds and commons are needed to enable cost-effective analysis of big biomedical data.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
8. Idea Dates Back to the 1960s 8 App App App CMS CMS MVS IBM VM/370 IBM Mainframe Native (Full) Virtualization Examples: Vmware ESX Virtualization first widely deployed with IBM VM/370.
9. What Do You Optimize? Goal: Minimize latency and control heat. Goal: Maximize data (with matching compute) and control cost.
14. What Resource is Managed? Scarce processors wait for data Manage cycles wait for an opening in the queue scatter the data to the processors and gather the results Persistent data wait for queries Manage data persistent data waits for queries computation done locally results returned Supercomputer Center Model Data Center Model
15. Part 2. Data Centers as the Unit of Computing Cloud computing is at the top of the Gartner hype cycle. “Cloud computing has become the center of investment and innovation.”Nicholas Carr, 2009 IDC Directions 15
18. Transition Taking Place A hand full of players are building multiple data centers a year and improving with each one. This includes Google, Microsoft, Yahoo, … A data center today costs $200 M – $400+ M Berkeley RAD Report points out analogy with semiconductor industry as companies stopped building their own Fabs and starting leasing Fabs from others as Fabs approached $1B 18
19. Which is the Operating System? 19 … … VM 1 VM 5 VM 50,000 VM 1 Data Center Operating System Hyperviser workstation data center
21. Some Programming Models for Data Centers Operations over data center of disks MapReduce (“string-based”) User-Defined Functions (UDFs) over data center SQL and Quasi-SQL over data center Data analysis / statistics over data center Operations over data center of memory Grep over distributed memory UDFs over distributed memory SQL and Quasi-SQL over distributed memory Data analysis / statistics over distributed memory
23. U.S. 501(3)(c) not-for-profit corporation Supports the development of standards and interoperability frameworks. Supports reference implementations for cloud computing. Manages testbeds: Open Cloud Testbed, IntercloudTestbed, Open Science Data Cloud Develops benchmarks. 23 www.opencloudconsortium.org
24. OCC Members Companies: Aerospace, Booz Allen Hamilton, Cisco, InfoBlox, Open Data Group, Raytheon, Yahoo Universities: CalIT2, Johns Hopkins, Northwestern, University of Illinois at Chicago, University of Chicago Government agencies: NASA Organizations: Sector Project 24
33. Open Science Data Cloud sky cloud Planning to work with 5 international partners (all connected with 10 Gbps networks). biocloud 27
34. MalStone (OCC-Developed Benchmark) Sector/Sphere 1.20, Hadoop 0.18.3 with no replication on Phase 1 of Open Cloud Testbed in a single rack. Data consisted of 20 nodes with 500 million 100-byte records / node.
35. Some Lessons Learned (So Far) Python over Hadoop Distributed File System surprisingly powerful. Tuning Hadoop can be a large (unacknowledged) cost. Performance of a cloud computation can be significantly impacted by just 1 or 2 nodes that are a bit slower. Wide area clouds can be practical in some cases. 29
36. Part 4. Sector 30 http://sector.sourceforge.net
37. Sector Overview Sector is fast As measured by MalStone & Terasort Sector is easy to program Supports UDFs, MapReduce & Python over streams Sector does not require extensive tuning. Sector is secure A HIPAA compliant Sector cloud is being set up Sector is reliable Sector v1.24 supports multiple master node servers 31
38. Google’s Large Data Cloud Compute Services Data Services Storage Services 32 Applications Google’s MapReduce Google’s BigTable Google File System (GFS) Google’s Stack
39. Hadoop’s Large Data Cloud Compute Services Storage Services 33 Applications Hadoop’sMapReduce Data Services Hadoop Distributed File System (HDFS) Hadoop’s Stack
40. Sector’s Large Data Cloud 34 Applications Compute Services Sphere’s UDFs Data Services Sector’s Distributed File System (SDFS) Storage Services UDP-based Data Transport Protocol (UDT) Routing & Transport Services Sector’s Stack
41. Generalization: Apply User Defined Functions (UDF) to Files in Storage Cloud map/shuffle reduce 35 UDF UDF
42. Hadoopvs Sector 36 Source: Gu and Grossman, Sector and Sphere, Phil. Trans. Royal Society A, 2009.
43. Terasort - Sector vsHadoop Performance Sector/Sphere 1.24a, Hadoop 0.20.1 with no replication on Phase 2 of Open Cloud Testbed with co-located racks.
44. Sector Applications Distributing the 15 TB Sloan Digital Sky Survey to astronomers around the world (joint with JHU, 2005) Managing and analyzing high throughput sequence data (Cistrack, University of Chicago, Cistrack, 2007). Detecting emergent behavior in distributed network data (Angle, won SC 07 Analytics Challenge) Image processing for high throughput sequencing. Wide area clouds (won SC 09 BWC with 100 Gbps wide area computation) New ensemble-based algorithms for trees Graph processing 38
45. Cistrack Web Portal & Widgets Cistrack Elastic Cloud Services Cistrack Database Analysis Pipelines & Re-analysis Services Cistrack Large Data Cloud Services Ingestion Services
46. Thank you For more information, please see blog.rgrossman.com 40