This document provides an overview of cloud computing and demonstrates how to create and deploy a sample application in the cloud. It begins with definitions of cloud computing and discusses the benefits of the cloud model. It then demonstrates deploying a web application and web service to the Amazon EC2 platform as an example of Infrastructure as a Service (IaaS). The document compares IaaS to Platform as a Service (PaaS) and provides Google App Engine as an example of PaaS.
Big Data Analysis : Deciphering the haystack Srinath Perera
A primary outcome of Bigdata is to derive useful and actionable insights from large or challenges data collections. The goal is to run the transformations from data, to information, to knowledge, and finally to insights. This includes calculating simple analytics like Mean, Max, and Median, to derive overall understanding about data by building models, and finally to derive predictions from data. Some cases we can afford to wait to collect and processes them, while in other cases we need to know the outputs right away. MapReduce has been the defacto standard for data processing, and we will start our discussion from there. However, that is only one side of the problem. There are other technologies like Apache Spark and Apache Drill graining ground, and also realtime processing technologies like Stream Processing and Complex Event Processing. Finally there are lot of work on porting decision technologies like Machine learning into big data landscape. This talk discusses big data processing in general and look at each of those different technologies comparing and contrasting them.
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Introduction to Large Scale Data Analysis with WSO2 Analytics PlatformSrinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. Predictive analytics let us learn models from data often providing us ability to predict the outcome of our actions.
WSO2 Data analytics platform is fast and scalable platform that is being used by more than 40 organizations including Banks, Financial Institutions, Smart Cities, Hospitals, Media Companies, Telecom Companies, State and Federal Governments, and High Tech companies. This talk will start with a discussion on large scale data analysis. Then we will look at WSO2 Data analytics platform and discuss in detail how we can use the platform to build end to end Big data applications combining power of batch processing, real-time analytics, and predictive technologies.
With tens of thousands of Java servers running in production in enterprise, Java has become a language of choice for building production systems. If our machines are to exhibit acceptable performance, they require regular tuning.This talk takes a detailed look at techniques for tuning a Java Server.
This slide deck provides an overview to WSO2 Big data platform and discuss some of its customer case studies and applications. It discuss Big Data in general, real time analytics WSO2 CEP, batch analytics WSO2 BAM, and new products like predictive analytics with WSO2 Machine Learner. For more information, please reach us though architecture@wso2.org.
Introduction to WSO2 Analytics Platform: 2016 Q2 UpdateSrinath Perera
In this talk, we will discuss about the WSO2 Data Analytics platform that brings together all the technologies into one platform. It lets you collect data through a one sensor API, process it using batch, realtime or predictive technologies and communicate your results all within a single platform and user experience.
More details https://iwringer.wordpress.com/2015/03/18/introducing-wso2-analytics-platform-note-for-architects/
Big Data Analysis : Deciphering the haystack Srinath Perera
A primary outcome of Bigdata is to derive useful and actionable insights from large or challenges data collections. The goal is to run the transformations from data, to information, to knowledge, and finally to insights. This includes calculating simple analytics like Mean, Max, and Median, to derive overall understanding about data by building models, and finally to derive predictions from data. Some cases we can afford to wait to collect and processes them, while in other cases we need to know the outputs right away. MapReduce has been the defacto standard for data processing, and we will start our discussion from there. However, that is only one side of the problem. There are other technologies like Apache Spark and Apache Drill graining ground, and also realtime processing technologies like Stream Processing and Complex Event Processing. Finally there are lot of work on porting decision technologies like Machine learning into big data landscape. This talk discusses big data processing in general and look at each of those different technologies comparing and contrasting them.
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
Introduction to Large Scale Data Analysis with WSO2 Analytics PlatformSrinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. Predictive analytics let us learn models from data often providing us ability to predict the outcome of our actions.
WSO2 Data analytics platform is fast and scalable platform that is being used by more than 40 organizations including Banks, Financial Institutions, Smart Cities, Hospitals, Media Companies, Telecom Companies, State and Federal Governments, and High Tech companies. This talk will start with a discussion on large scale data analysis. Then we will look at WSO2 Data analytics platform and discuss in detail how we can use the platform to build end to end Big data applications combining power of batch processing, real-time analytics, and predictive technologies.
With tens of thousands of Java servers running in production in enterprise, Java has become a language of choice for building production systems. If our machines are to exhibit acceptable performance, they require regular tuning.This talk takes a detailed look at techniques for tuning a Java Server.
This slide deck provides an overview to WSO2 Big data platform and discuss some of its customer case studies and applications. It discuss Big Data in general, real time analytics WSO2 CEP, batch analytics WSO2 BAM, and new products like predictive analytics with WSO2 Machine Learner. For more information, please reach us though architecture@wso2.org.
Introduction to WSO2 Analytics Platform: 2016 Q2 UpdateSrinath Perera
In this talk, we will discuss about the WSO2 Data Analytics platform that brings together all the technologies into one platform. It lets you collect data through a one sensor API, process it using batch, realtime or predictive technologies and communicate your results all within a single platform and user experience.
More details https://iwringer.wordpress.com/2015/03/18/introducing-wso2-analytics-platform-note-for-architects/
Big data visualization frameworks and applications at Kitwarebigdataviz_bay
Big data visualization frameworks and applications at Kitware
Marcus Hanwell, Technical Leader at Kitware, Inc.
March 27th 2014
Kitware develops permissively licensed open source frameworks and applications for scientific data applications, and related areas. Some of the frameworks developed by our High Performance Computing and Visualization group address current challenges in big data visualization and analysis in a number of application domains including geospatial visualization, social media, finance, chemistry, biological (phylogenetics), and climate. The frameworks used to develop solutions in these areas will be described, along with the applications and the nature of the underlying data. These solutions focus on shared frameworks providing data storage, indexing, retrieval, client-server delivery models, server-side serial and parallel data reduction, analysis, and diagnostics. Additionally, they provide mechanisms that enable server-side or client-side rendering based on the capabilities and configuration of the system.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
Dmitry will show the audience on how get started with Mxnet and building Deep Learning models to classify images, sound and text.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Big Data Visualization
Kwan-Liu Ma
Professor of Computer Science and Chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis
January 22nd 2014
We are entering a data-rich era. Advanced computing, imaging, and sensing technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is expected to be even greater. To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis. One such a tool is visualization. I will present visualizations designed for gleaning insight from massive data and guiding complex data analysis tasks. I will show case studies using data from cyber/homeland security, large-scale scientific simulations, medicine, and sociological studies.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
Big and fast a quest for relevant and real-time analyticsNatalino Busa
Our retail banking market demands now more than ever to stay close to our customers, and to carefully understand what services, products, and wishes are relevant for each customer at any given time.
This sort of marketing research is often beyond the capacity of traditional BI reporting frameworks. In this talk, we illustrate how we team up data scientists and big data engineers in order to create and scale distributed analyses on a big data platform.
Introduction to WSO2 Data Analytics PlatformSrinath Perera
WSO2 have had several analytics products: WSO2 BAM and WSO2 CEP for some time (or Big Data products if you prefer the term). We are added WSO2 Machine Learner, a product to create, evaluate, and deploy predictive models and renamed WSO2 BAM to WSO2 DAS ( Data Analytics Server).
The platform let you publish ( collect data) once and process them through batch ( Spark) , realtime ( CEP), search the data ( Lucene) and build machine learning models.
This post describes how all those fit within to a single story.
For more information, see https://iwringer.wordpress.com/2015/03/18/introducing-wso2-analytics-platform-note-for-architects/
WorDS of Data Science in the Presence of Heterogenous Computing ArchitecturesIlkay Altintas, Ph.D.
ISUM 2015 Keynote
Summary: Computational and Data Science is about extracting knowledge from data and modeling. This end goal can only be achieved through a craft that combines people, processes, computational and Big Data platforms, application-specific purpose and programmability. Publications and provenance of the data products products leading to these publications are also important. With this in mind, this talk defines a terminology for computational and data science applications, and discuss why focusing on these concepts is important for executability and reproducibility in computational and data science.
Big Data Day LA 2016/ Big Data Track - Twitter Heron @ Scale - Karthik Ramasa...Data Con LA
Twitter generates billions and billions of events per day. Analyzing these events in real time presents a massive challenge. Twitter designed and deployed a new streaming system called Heron. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. This talk looks at Twitter's operating experiences and challenges of running Heron at scale and the approaches taken to solve those challenges.
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...Ilkay Altintas, Ph.D.
cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Big data visualization frameworks and applications at Kitwarebigdataviz_bay
Big data visualization frameworks and applications at Kitware
Marcus Hanwell, Technical Leader at Kitware, Inc.
March 27th 2014
Kitware develops permissively licensed open source frameworks and applications for scientific data applications, and related areas. Some of the frameworks developed by our High Performance Computing and Visualization group address current challenges in big data visualization and analysis in a number of application domains including geospatial visualization, social media, finance, chemistry, biological (phylogenetics), and climate. The frameworks used to develop solutions in these areas will be described, along with the applications and the nature of the underlying data. These solutions focus on shared frameworks providing data storage, indexing, retrieval, client-server delivery models, server-side serial and parallel data reduction, analysis, and diagnostics. Additionally, they provide mechanisms that enable server-side or client-side rendering based on the capabilities and configuration of the system.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
Dmitry will show the audience on how get started with Mxnet and building Deep Learning models to classify images, sound and text.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Big Data Visualization
Kwan-Liu Ma
Professor of Computer Science and Chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis
January 22nd 2014
We are entering a data-rich era. Advanced computing, imaging, and sensing technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is expected to be even greater. To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis. One such a tool is visualization. I will present visualizations designed for gleaning insight from massive data and guiding complex data analysis tasks. I will show case studies using data from cyber/homeland security, large-scale scientific simulations, medicine, and sociological studies.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
Big and fast a quest for relevant and real-time analyticsNatalino Busa
Our retail banking market demands now more than ever to stay close to our customers, and to carefully understand what services, products, and wishes are relevant for each customer at any given time.
This sort of marketing research is often beyond the capacity of traditional BI reporting frameworks. In this talk, we illustrate how we team up data scientists and big data engineers in order to create and scale distributed analyses on a big data platform.
Introduction to WSO2 Data Analytics PlatformSrinath Perera
WSO2 have had several analytics products: WSO2 BAM and WSO2 CEP for some time (or Big Data products if you prefer the term). We are added WSO2 Machine Learner, a product to create, evaluate, and deploy predictive models and renamed WSO2 BAM to WSO2 DAS ( Data Analytics Server).
The platform let you publish ( collect data) once and process them through batch ( Spark) , realtime ( CEP), search the data ( Lucene) and build machine learning models.
This post describes how all those fit within to a single story.
For more information, see https://iwringer.wordpress.com/2015/03/18/introducing-wso2-analytics-platform-note-for-architects/
WorDS of Data Science in the Presence of Heterogenous Computing ArchitecturesIlkay Altintas, Ph.D.
ISUM 2015 Keynote
Summary: Computational and Data Science is about extracting knowledge from data and modeling. This end goal can only be achieved through a craft that combines people, processes, computational and Big Data platforms, application-specific purpose and programmability. Publications and provenance of the data products products leading to these publications are also important. With this in mind, this talk defines a terminology for computational and data science applications, and discuss why focusing on these concepts is important for executability and reproducibility in computational and data science.
Big Data Day LA 2016/ Big Data Track - Twitter Heron @ Scale - Karthik Ramasa...Data Con LA
Twitter generates billions and billions of events per day. Analyzing these events in real time presents a massive challenge. Twitter designed and deployed a new streaming system called Heron. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. This talk looks at Twitter's operating experiences and challenges of running Heron at scale and the approaches taken to solve those challenges.
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...Ilkay Altintas, Ph.D.
cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.
Data Science with Spark - Training at SparkSummit (East)Krishna Sankar
Slideset of the training we gave at the Spark Summit East.
Blog : https://doubleclix.wordpress.com/2015/03/25/data-science-with-spark-on-the-databricks-cloud-training-at-sparksummit-east/
Video is posted at Youtube https://www.youtube.com/watch?v=oTOgaMZkBKQ
Taming the cost of your first cloud - CCCEU 2014Tim Mackey
Today everyone is talking about clouds, and a few are building them, but far fewer are operating successful clouds. In this session we'll examine a variety of paradigm shifts IT makes when moving from a traditional virtualization and management mindset to operating a successful cloud. For most organizations, without careful planning the hype of a cloud solution can quickly overcome its capabilities and pre-existing best practices can combine to create the worst possible cloud scenario -- a cloud which isn't economical to operate, and which is more cumbersome to manage than a traditional virtualization farm.
Key topics covered include:
- Successful transition of operational and management paradigm
- How the VM density of clouds change Ops
- What it means to monitor the network in a cloud environment, at hyper-dense virtualization levels
- Preventing storage costs from outpacing delivery costs
Fundamental Concepts are given regarding Cloud Computing, the Pros and Cons of Cloud computing, the History and Evolution of Cloud computing. A Comparison of Cluster vs Grid Vs Cloud Computing is also mentioned in slides.
Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services),
We are talking about Cloud adoption challenges and cloud failes. Like the AWS re:Invent event also talk about cost management, visibility and Governance. We pick one solution CliQr.com to show how to avoid obstacles and manage hybrid clouds as company. #hybridcloudsuccessful
Similar to IEEE Cloud 2012: Clouds Hands-On Tutorial (20)
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
We have critically evaluated how AI will shape integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study.
We observe that AI can significantly impact integration use cases and identify 13 AI-based use case classes for integration. Points to note include:
Enabling AI in an enterprise involves collecting, cleaning up, and creating a single representation of data as well as enforcing decisions and exposing data outside, each of which leads to many integration use cases. Hence, AI indirectly creates demand for integration.
AI needs data, which in some cases lead to significant competitive advantages. The need to collect data would drive vendors to offer most AI products in the cloud through APIs.
Due to lack of expertise and data, custom AI model building will be limited to large organizations. It is hard for small and medium size organization to build and maintain custom models.
The Role of Blockchain in Future IntegrationsSrinath Perera
We have critically evaluated blockchain-based integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study. Based on our analysis, we observe that blockchain can significantly impact integration use cases.
In our paper, we identify 30-plus blockchain-based use cases for integration and four architecture patterns. Notably, each use case we identified can be implemented using one of the architecture patterns. Furthermore, we also discuss challenges and risks posed by blockchains that would affect these architecture patterns.
Our webinar presents a critical analysis of serverless technology and our thoughts about its future. We use Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, as the methodology of our study. Based on our analysis, we believe that serverless can significantly impact applications and software development workflows.
We’ve also made two further observations:
Limitations, such as tail latencies and cold starts, are not deal breakers for adoption. There are significant use cases that can work with existing serverless technologies despite these limitations.
We see a significant gap in required tooling and IDE support, best practices, and architecture blueprints. With proper tooling, it is possible to train existing enterprise developers to program with serverless. If proper tools are forthcoming, we believe serverless can cross the chasm in 3-5 years.
A detailed analysis can be found here: A Survey of Serverless: Status Quo and Future Directions. Join our webinar as we discuss this study, our conclusions, and evidence in detail.
1. Blockchain potential impact is real. If successful, Blockchain technologies can transform the way we live our day to day lives.
2. We believe technology is ready for limited applications in Digital Currency, Lightweight financial systems, Ledgers (of identity, ownership, status, and authority), Provenance (e.g. supply chains and other B2B scenarios) and Disintermediation, which we believe will happen in next three years.
3. However, with other use cases, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. These are hard problems and
4. It is not clear whether blockchain can sustain the current level of effort for extended period of 5+ years. There are many startups and they run the risk of running out of money before markets are ready. Failure of startups can inhibit further funding and investments.
5. Value and need of decentralization compared to centralized and semi-centralized alternatives is not clear.
A Visual Canvas for Judging New TechnologiesSrinath Perera
In the fast-changing technology world, the technology landscape shifts faster and faster. The agents of thses changes are new emerging technologies, which sometimes even create, destroy, or transform segments. In a shifting world, prevailing advantages are fleeting. Organizations that can master change and ride technology waves owns the future.
Not all emerging technologies live up to their promise. Every year, as a part of annual planning, most organizations need to decide relevance, impact, and the probability of success of emerging technologies and pick their bets. Although it is a regular decision there is no widely accepted framework for evaluating emerging technologies.
As a solution to this problem, we present “Emerging Technology Analysis Canvas” (ETAC), a framework to assess an individual emerging technology as a solution to this problem. Inspired by the Business Model Canvas, It represents different aspects of technology visually on a single page. This approach includes a set of questions that probe the technology arranged around a logical narrative. The visual representation is concise, compact, and comprehensible in a glance.
The talk discusses how analytics can attack privacy and what we can do about it. It discusses the legal responses (e.g. GDPR) as well technical responses ( differential privacy and homomorphic encryption).
The video is in https://www.facebook.com/eduscopelive/videos/314847475765297/ from 1.18.
Blockchain is often cited as one of the most impactful technology along with AI. It has attracted many startups, venture investments, and academic research. If successful, Blockchain technologies can transform the way, we live our day to day lives.
However, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. They are hard problems, and likely it will take at least 5-10 years to find answers to those problems.
Given the risk involved as well as the significant potential returns, we recommend a cautiously optimistic approach for blockchain with the focus on concrete use cases.
Today's Technology and Emerging Technology LandscapeSrinath Perera
We have seen the rise and fall of many technologies, some disappearing without a trace while others redefining the world. Collectively they have shaped our world beyond recognition. In this talk, Srinath will start with past technologies exploring their behavior. Then he will explore current middleware landscape, its composition, and relationships between different segments. He will discuss significant developments and discuss their future. Further, he will discuss emerging technologies, forces that shape them, and the promise of each technology, and finally, speculate about their evolution. You will walk away with knowledge on the evolution of middleware, the status quo, and discussion about how, at WSO2, we think those technologies will evolve.
Some died, some get by, but some have woven themselves to today's middleware so much that we do not notice them. The point I want to make is that not all emerging technologies are fads. Some are, and some are too early, like AI. But some are lasting.
The Rise of Streaming SQL and Evolution of Streaming ApplicationsSrinath Perera
First-generation stream processors, such as Apache Storm, wanted us to write code. It was a great start. However, when building real-world apps, which are used for a long time and evolve, writing code gets us into trouble.
If we want to query a database or query data stored in storage with Hadoop, we use SQL. Why can't we query data streaming using SQL? We can. Almost all open source stream processors, including Storm, Flink, and Kafka, have switched to SQL.
In this webinar, Srinath will talk about the evolution of stream processing, streaming SQL, the status quo, and what this means to stream applications. He will also dissect the experience of building streaming applications by exploring common patterns and pitfalls.
Analytics and AI: The Good, the Bad and the UglySrinath Perera
Analytics let us question the data, which in effect questions the world around us. This let us understand, monitor, and shape the world. AI let us discover connections, predict the possible futures and automate tasks.
These twin technologies can change the world around us. On one hand, make us efficient, connected, and fulfilled. At the same time, the change of status quo can replace jobs, affect lives and build biases into our systems that can marginalize millions.
In this talk, we will discuss core ideas behind analytics and AI, their possible impact, both good and bad outcomes, and challenges.
The dawn of digital businesses is upon us, with reimagined business models that make the best use of digital technologies such as automation, analytics, integration and cloud. Digital businesses are efficient, continuously optimizing, proactive, flexible and are able to fully understand their customers. Analytics is a key technology that helps in doing so. It acts as the eyes and ears of the system and provides a holistic view on the past and present so that decision-makers can predict what will happen in the future. This webinar will explore
Why becoming a digital business is not a choice
The role of analytics in digital transformation with examples
How best to leverage state of the art analytics technology
SoC Keynote:The State of the Art in Integration TechnologySrinath Perera
This talk discusses Outline of the state of the art of Enterprise Software and how we get there, as I see it. Also second part describes Ballerina, a new programming language WSO2 has built for Enterprise Computing.
It is presented as a Keynote at 11th Symposium and Summer School On Service-Oriented Computing.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
1. Clouds Hands On
Tutorial
Srinath Perera Ph.D.
Senior Software Architect, WSO2 Inc.
Member, Apache Software Foundation
Visiting Faculty, University of Moratuwa
Research Scientist, Lanka Software
Foundation
2. Outline
• What is Cloud
Computing?
• What can I do with
the Cloud?
• How to do it?
• Conclusion
photo by John Trainoron Flickr http://www.flickr.com/photos/trainor/2902023575/, Licensed under CC
3. Quick EC2 Demo
• Lets us start our own Virtual machines in the
cloud.
• We call the virtual machines, AMIs (Amazon
machine images)
• You can reuse other’s AMIs or create your
own.
4. What is Cloud?
• Based on the idea that
computation and storage can
be rented as a utility from data
centers that runs somewhere
(in the cloud) on demand.
• Remote resources that are
rented
– On demand and in elastic
manner
– Pay as you go
5. Men, it has been well said, think in herds; it will
be seen that they go mad in herds, while they
only recover their senses slowly, and one by
one. ~ Charles Mackay
Copied from http://www.flickr.com/photos/54555810@N00/2848637190/, by Rambling Traveler
6. Gold Rush
• Very good example of a hype
• Only few actually made money
• But associated services (merchants and
transportations) made lot of money
7. The Cloud Bandwagon
• Is Cloud a hype? Of
course it is!
• Is it Just hype? may be
not, thats what we will
discuss.
• But don’t get me wrong,
• even if it is a hype, that
doesn’t mean we should
Image from
not be talking about it.
http://www.flickr.com/photos/88929764@N00/443
6978855/
8. Is Cloud Hype?
• We have to understand what it is and what
drives it? That would tell us how to navigation
9. Electricity as a Utility as an
Example
• Now, no one run generators themselves
• Use electricity that is remotely generated
• Can draw when need it
• Only pay for what you use
10. Electricity as a Utility: Benefits
• Small startup cost (do not
have to buy a generator)
• No operational cost
• Do not need to do
capacity planning
• Overall cheaper electricity
due to economics of scale
• Making it parts of the
everyday life, commodity
(accessibility)
Image from http://www.flickr.com/photos/infomofo/3220498521
11. Benefits of Cloud Computing
• Avoid/reduce operational
• costs by outsourcing
• Can scale up and down as
needed
• Pay as you go
• Making it parts of the
everyday life,
commodity(accessibility)
• Cheaper computing power
due to economy of scale
12. Drivers of Cloud
• Unused computing power
at Google, Amazon
• Max load >> average load
• High operational cost,
need for outsourcing
• Availability of large scale
solutions and
infrastructure as side
efforts of high tech
company operations
15. Private Cloud
• Run a Cloud within the
organization (mainly due to
security concerns).
• e.g. Amazon Virtual Private Cloud (VPC), IBM private
cloud, WSO2 Stratos private Cloud
• Idea is optimizing resource sharing, utilizations, and
operations
– e.g. testing environments
• Connection to public Cloud is possible (e.g. Amazon VPC
uses VPN) Image http://www.flickr.com/photos/glasgows/536185797/, Some rights reserved by M Glasgow
16. Cloud Computing Models
Control
Flexibility of Purpose
Level of Abstraction
Software as a Service
Platform as a Service
Infrastructure as a Service
Public Hybrid Private
Economy
18. What Can We do with the Cloud?
• If I am a developer, now it is
– Easy to get access to a machine
– Easy to host something
• If I am a startup
– Easy run my new business
– Easy to run my app
• If I am a Enterprise
– Outsource functionalities
– On demand H/W
• If I am a researcher
– Easy access computing power
19. More Room to Outsource Non
Key Functions of a Organization
• Organizations outsource their non-
competitive areas to reduce costs
and focus on their own expertise.
• IT departments are a major cost in
most organizations
• Cloud enables Organizations to
outsource some of the IT functions
20. Small Start Up Cost
• Small start up cost
• Reducing the gap between
visionaries and dreamers
• New organization has better
chance for outsourcing
operations through the Cloud
• Cloud competition likely to
drive down hosting prizes
21. 1 computer for 100days = 100
computers for 1 day
• Great tool for occasional
computations
– Research labs
• Reporting collecting data for
a story
– New york Times tiff to pdf
conversion
• Rise of analytics
– great tool for offline analysis
– Business Intelligence (BI)
– Audits
22. Increased Accessibility
• Large Scale Computation and
Storage Resources becoming
a Commodity
• Computer intensive desktop
apps (e.g. Excel, 3D Max)
• Normal people, not just
organizations can have
access to computing power
and storage
24. Explain the Scenario
• Web App
• A Web Service
• Web App call the Web Service and show a
hello world message got from Web Service.
25. Demo It Locally
• Install WSAS
• Show consoles
• Install the Service
• Try the Service
• Install the Web App
• Try the Web app
• Show SOAP traces
26. IaaS (Infrastructure As a Service)
• Let users allocate and use a computer on-
demand, and can be returned when not
needed.
• Once allocated, node behaves as a normal
node.
• None or minimal add-on services
27. E.g. Amazon Web Services
• Several Services
– S3 Storage
– EC2 computing cloud
• Based on Virtualization, where each user is given a
virtual machine and charged by the hour
• Need least amount of changes to move apps to the
Cloud. They in a way replace hosting services
• Least amount of out of the box services (e.g. DOS
attack prevention) and advanced services like scaling
etc., are a responsibility of the user.
• Often the best choice for ad-hoc computer users.
28. Demo 1
• Login to the AMI
• Download WSAS and install
• Login and show the console
• Upload the Service
• Tryout the service
• Upload the Webapp
• Tryout the Web App
• Show SOAP traces
29. Demo 2
• However, if we shutdown the Instance, all is
lost
• Create and save the AMI
• Restart and show
30. Enabling Technology: Virtualization
• IaaS uses Virtualization to provide
infrastructure as a service
• Virtualization can add significant
overhead (each instruction
become 2 instructions)
• New CPUs have hardware support
for virtualization,
– which make things better
– still I/O is a challenge
31. Pros & Cons
• Very easy to start
• Minimal changes to the App
• But not many add on features
• Lack of persistence make life tricky
32. PaaS (Platform as a Service)
• It provide servers (middleware platforms) as a
service
• Can deploy your application artifacts in the
cloud
• Unlike IaaS, it can give you add on services.
But it will only support specific application
types.
33. Why PaaS?
• IaaS only provides limited saving to someone
who needs to outsource their IT functions
• SaaS is great when they can be used
• They are usually very specific (e.g. email, CRM ..)
• If they match, then great, but if they are not, not
much choice for the user.
• PaaS stays in the middle ground
• Framework to host your apps
• Hopefully you can move your apps as it is (well not
the case with Azure or App Engine, but it is possible
with WSO2 Stratos !!!).
34. Google App Engine
• Support Java and Python
• Support web requests and run user written
web applications in an isolated environment
• Java version is based on servlets
• Support storage based on Big table,
memcache based cache, and auto scaling
• Can write apps locally, test it, and then upload
to the Cloud
35. WSO2 Stratos
• Support Java based SOA artifacts (services,
workflows, mediation) , messaging, CEP, Rules,
etc
• Support running user written web applications in
an isolated environment
• Built on Open source projects like Axis2, ODE etc
• Support storage based on MySQL, Cassandra, and
HDFS
• Can write apps locally, test it, and then upload to
the Cloud
40. Why Multi-tenancy? 1. Increased
sharing
• Cloud shares
resources across a
large pool of users.
• Now sharing
happens in the
application level as “There is no delight in owning
oppose to sharing at anything unshared.”
OS level for multiple Seneca (Roman philosopher, mid-1st century
processes and AD)
sharing at HW level
with VMs.
photo by Ben Gray on Flickr, http://www.flickr.com/photos/ben_grey/4582294721/,
• That can bring Licensed under CC
greater savings
41. Why Multi-tenancy? 2. Provide “pay
for what you use”
• Often there will be many accounts in
a PaaS or a SaaS, but only a fraction
of them will be in use.
• We cannot allocate runtime resource
per account (disk may be ok, as it is
cheap). For example, we cannot run
a VM per account.
• By sharing the same server with
many users, Multi-tenancy provides
much reduced runtime cost per
server.
42. Multi-tenancy vs. Virtual Machines
• Multi-tenancy provides much fine
grained sharing by many applications
sharing the same server.
• Say there are 100k accounts, but 10k
active users at a time. VM based
model needs 100k VMs, which
means there is a cost incurred per
account.
• With Multi-tenancy one server can handle many accounts, and by
mixing and matching heavy and light users, Multi-tenancy can
operate with much less number of servers.
photo by hans s on Flickr, http://www.flickr.com/photos/archeon/2359334908/
43. Cloud Native
• Elastic (Uses the cloud efficiently)
• Scales up and down as needed
• Works with the underlying IaaS
• Self-service (in the hands of users)
• De-centralized creation and management of tenants
• Automated Governance across tenants
• Multi-tenant (Only costs when you use it)
• Virtual isolated instances with near zero incremental cost
• Implies you have a proper identity model
• Granularly Billed and Metered (pay for just what you use)
• Allocate costs to exactly who uses them
• Distributed/Dynamically Wired (works properly in the cloud)
• Supports deploying in a dynamically sized cluster
• Finds services across applications even when they move
• Incrementally Deployed and Tested (seamless live upgrades)
• Supports continuous update, side-by-side operation, in-place testing and
incremental production
46. Software as a Service
• Provide a complete package as a service
• Very easy to get started
• Generally only provide limited customizability
• Might provide higher level domain specific
abstractions and functionality
47. SaaS Example: Salesforce
• Provide support for CRM (Customer
Relationship Management) software as a
Service
• The application available out of the box users
• just configure and use it.
• Salesforce handles all the details, and a ideal
choice for outsourcing IT functions
• However, applications are very specific and
customizations are limited.
48. SaaS App Market
• Mobile apps, Browser Apps etc often depends
on backend, that need to be hosted and
running.
• Also, there is the old user case of service
market place (e.g. Map Service, targeted
advertising suggestions)
• PaaS could provide an ideal environment to
develop and run them.
51. Latency/ Bandwidth
• Why does electricity as a Utility
was so successful? One key
aspect was almost unlimited
speed and capacity
• Is that the same for Cloud?
– Not really
– Most people dismiss this, and
does not even want to discuss
– But for some apps (e.g.
interactive apps like games) this
can be a issue
• Possibilities
– support for offline operation
– Fedex your data
52. Autoscaling and Cloud Bursting
• Max load >> average load
• Allocate based on the load
• Running internal machines in an average load
(because cloud is still expensive than hardware ) and
• Scale out to cloud when there is high load
• Mimic the Hybrid car
53. Performance
• In the Cloud, your software will run on an another layer of
abstractions
• It will inevitably slower (3-4 times if unlucky)
– Often the overhead comes from I/O
– Some hit on CPU power
• Expect the bottlenecks to shift
• Remember performance ≠ scalability
• Cloud likely to let you scale out, but performance on
individual nodes likely to go down
55. Security
• With cloud you will run your apps and put your
data in an outsider's administrative domain, Can
we trust the outsiders to not look at our data?
– Well it depends. But concern is normal for any out
sourcing
• Can we trust their security measures? Does the
isolations are good enough?
• If we are keeping data owned by others, what are
the legal implications etc.
56. Look back: Recommendations
• How can we benefits from the Cloud? My list of benefits
were by no means exhaustive.
– If you are small startup? Doing something part time (writing
Apps for App Store), then cloud is for you definitely.
– If you have a small data center, need to efficiently manage that
and increase utilization, you should think Private Cloud
Copyright Kirsty Smith and licensed for reuse under this Creative Commons
License
57. Look back: Recommendations (Contd.)
• If your services have a Max load >> Average load, you
should think about crowd bursting
• If you do heavy computations once in a while
(analytics, audits), then use IaaS there.
• If you want to outsource some of your IT functions,
think SaaS
• Before leap think about 3 major concerns of Cloud?
– Security/ Privacy, Latency and Performance
• How much sharing and savings I need?
– IaaS, PaaS, SaaS