Converged Infrastructure as a Go Forward StrategyJames Charter
Converged Infrastructure as the Go Forward Strategy. Overview of CI versus traditional architecture of compute, storage, networking and the benefits of adopting CI. Presented at VMware Virtualization Forum December 2010, February 2011.
Converged Infrastructure as a Go Forward StrategyJames Charter
Converged Infrastructure as the Go Forward Strategy. Overview of CI versus traditional architecture of compute, storage, networking and the benefits of adopting CI. Presented at VMware Virtualization Forum December 2010, February 2011.
Amazon Web Services (AWS) can make hosting scalable, highly-available websites and web applications easier and less expensive for the Enterprise Education customers. Join us for an informative webinar on tools AWS provides to elastically scale your architecture to avoid underutilized resources while reducing complexity with templates, partners, and tools to do much of the heavy lifting of creating and running a website for you.
Thesis Proposal: User Application Profiles for Publishing Linked Data in HTM...Sean Petiya
User Application Profiles for Publishing Linked Data in HTML/RDFa: Building a Semantic Web of Comic Book Metadata.
Kent State University - July 30, 2014
The objective is to present a case study for building a domain ontology and extending the usability and usage of that vocabulary by developing metadata application profiles for specific user groups. These objectives will be realized by a metadata vocabulary for the description of comic books and comic book collections, titled the Comic Book Ontology (CBO) and a series of schemata for encoding records using appropriate members of that ontology, specifically an XML schema and a corresponding minimal version. A set of metadata application profiles will also be developed to guide the publication of comic book data using the vocabulary by identified user groups, which include libraries, collectors, creators, retailers, and publishers, and will present recommended elements, guidelines, and examples of encoding data in the markup of existing hypertext systems using HTML5 and RDFa. The study then aims to extend the usability and usage of those schemata by presenting a methodology for building application profiles guided by the development of assumptive, data-driven personas. It will generate these personas through a review of systems used by each participant and an analysis of existing content. The study also seeks to demonstrate how an ontology can be applied to existing collaborative indexing projects, datasets, or research to enhance the visibility, reference, and utilization of those endeavors through their publication as Linked Data. The overall, and long-term, goal is to explore methods for bringing enhanced bibliographic control and organization to the comic book domain, allowing the creative and intellectual efforts of writers, artists, contributors, scholars, researchers, and collectors to be better combined and shared, and well represented in the Semantic Web.
Identifying Frequent User Tasks from Application LogsHimel Dev
Reference: Himel Dev, and Zhicheng Liu, "Identifying Frequent User Tasks from Application Logs", 22nd ACM International Conference on Intelligent User Interfaces (IUI), Limassol, Cyprus, March, 2017.
Cloud vs. Traditional Hosting - Andrei Yurkevich @ CloudCamp Denmark 2011Altoros
Andrei Yurkevich—President and Chief Technology Officer at Altoros—compared cloud vs. regular hosting in terms of which could be a better choice for a startup. He dwelled on how usage of cloud-based hosting might affect not only efficiency of spending on hardware, but also success of the whole venture.
On October 11, 2011, Altoros organized and took part in the first CloudCamp in Denmark held within the GOTO Aarhus Conference.
High-Availability Infrastructure in the Cloud - Evan Cooke - Web 2.0 Expo NYC...Twilio Inc
Designing a massively scalable highly available persistence layer has been one of the great challenges we’ve faced building out Twilio’s cloud communications infrastructure. Robust Voice and SMS APIs have strict consistency, latency, and availability requirements that cannot be solved using traditional sharding or scaling approaches. In this talk we first look to understand the challenges of running high-availability services in the cloud and then describe how we’ve architected “in-flight” and “post-flight” data into separate datastores that can be implemented using a range of technologies.
Building Cost-Aware Cloud Architectures - Jinesh Varia (AWS) and Adrian Cockc...Amazon Web Services
5 ways you can build cost-awareness into your cloud architectures and maximize your savings (business-driven auto scaling, mixing and matching reserved/on-demand, iterating and optimizing fungible resources, follow the customer (run auto scaling web servers) during the day and follow the money (run hadoop and transcoding jobs) at night and soak up your reservations.
Try Amazon cloud. Avail our 360 degree report on your Benefits / ROI / Migration Timeline exclusive for your business. Amazon Cloud can bring your maintenance/investment reduced, giving worlds efficient IT infrastructure that is required to meet your scaling up/down online software application/data access and processing speed for your business you have/use may get moved to Amazon cloud with all required compliances. We request an hour time slot with your stakeholders, next week, between Monday and Friday , 8am and 5pm EST.
ENT201 How Much Can You Save with the Cloud? - AWS re: Invent 2012Amazon Web Services
There isn’t an IT department out there that isn’t under pressure to reduce costs. For thousands of enterprises, the AWS cloud has become part of that lower cost strategy. But how much could you really save with AWS? Where will those savings come from, and how does shifting to a model where you pay only for what you use impact your IT spend? In this session, we are joined by Kris Bliesner, chief executive officer of 2nd Watch, who have successfully helped over one hundred organizations reduce their IT costs. We share detailed best practices on how to calculate detailed apples-to-apples comparisons, how to build the models, and where to look to identify the biggest cost saving opportunities. We are also joined by Vladimir Mitevski, Vice President Product Management Core Services at Thomson Reuters who will walk through by line item how and where their actual operating and capital expenses changed when they migrated to the cloud, so you can learn from their experiences, and take home some
Amazon Web Services (AWS) can make hosting scalable, highly-available websites and web applications easier and less expensive for the Enterprise Education customers. Join us for an informative webinar on tools AWS provides to elastically scale your architecture to avoid underutilized resources while reducing complexity with templates, partners, and tools to do much of the heavy lifting of creating and running a website for you.
Thesis Proposal: User Application Profiles for Publishing Linked Data in HTM...Sean Petiya
User Application Profiles for Publishing Linked Data in HTML/RDFa: Building a Semantic Web of Comic Book Metadata.
Kent State University - July 30, 2014
The objective is to present a case study for building a domain ontology and extending the usability and usage of that vocabulary by developing metadata application profiles for specific user groups. These objectives will be realized by a metadata vocabulary for the description of comic books and comic book collections, titled the Comic Book Ontology (CBO) and a series of schemata for encoding records using appropriate members of that ontology, specifically an XML schema and a corresponding minimal version. A set of metadata application profiles will also be developed to guide the publication of comic book data using the vocabulary by identified user groups, which include libraries, collectors, creators, retailers, and publishers, and will present recommended elements, guidelines, and examples of encoding data in the markup of existing hypertext systems using HTML5 and RDFa. The study then aims to extend the usability and usage of those schemata by presenting a methodology for building application profiles guided by the development of assumptive, data-driven personas. It will generate these personas through a review of systems used by each participant and an analysis of existing content. The study also seeks to demonstrate how an ontology can be applied to existing collaborative indexing projects, datasets, or research to enhance the visibility, reference, and utilization of those endeavors through their publication as Linked Data. The overall, and long-term, goal is to explore methods for bringing enhanced bibliographic control and organization to the comic book domain, allowing the creative and intellectual efforts of writers, artists, contributors, scholars, researchers, and collectors to be better combined and shared, and well represented in the Semantic Web.
Identifying Frequent User Tasks from Application LogsHimel Dev
Reference: Himel Dev, and Zhicheng Liu, "Identifying Frequent User Tasks from Application Logs", 22nd ACM International Conference on Intelligent User Interfaces (IUI), Limassol, Cyprus, March, 2017.
Cloud vs. Traditional Hosting - Andrei Yurkevich @ CloudCamp Denmark 2011Altoros
Andrei Yurkevich—President and Chief Technology Officer at Altoros—compared cloud vs. regular hosting in terms of which could be a better choice for a startup. He dwelled on how usage of cloud-based hosting might affect not only efficiency of spending on hardware, but also success of the whole venture.
On October 11, 2011, Altoros organized and took part in the first CloudCamp in Denmark held within the GOTO Aarhus Conference.
High-Availability Infrastructure in the Cloud - Evan Cooke - Web 2.0 Expo NYC...Twilio Inc
Designing a massively scalable highly available persistence layer has been one of the great challenges we’ve faced building out Twilio’s cloud communications infrastructure. Robust Voice and SMS APIs have strict consistency, latency, and availability requirements that cannot be solved using traditional sharding or scaling approaches. In this talk we first look to understand the challenges of running high-availability services in the cloud and then describe how we’ve architected “in-flight” and “post-flight” data into separate datastores that can be implemented using a range of technologies.
Building Cost-Aware Cloud Architectures - Jinesh Varia (AWS) and Adrian Cockc...Amazon Web Services
5 ways you can build cost-awareness into your cloud architectures and maximize your savings (business-driven auto scaling, mixing and matching reserved/on-demand, iterating and optimizing fungible resources, follow the customer (run auto scaling web servers) during the day and follow the money (run hadoop and transcoding jobs) at night and soak up your reservations.
Try Amazon cloud. Avail our 360 degree report on your Benefits / ROI / Migration Timeline exclusive for your business. Amazon Cloud can bring your maintenance/investment reduced, giving worlds efficient IT infrastructure that is required to meet your scaling up/down online software application/data access and processing speed for your business you have/use may get moved to Amazon cloud with all required compliances. We request an hour time slot with your stakeholders, next week, between Monday and Friday , 8am and 5pm EST.
ENT201 How Much Can You Save with the Cloud? - AWS re: Invent 2012Amazon Web Services
There isn’t an IT department out there that isn’t under pressure to reduce costs. For thousands of enterprises, the AWS cloud has become part of that lower cost strategy. But how much could you really save with AWS? Where will those savings come from, and how does shifting to a model where you pay only for what you use impact your IT spend? In this session, we are joined by Kris Bliesner, chief executive officer of 2nd Watch, who have successfully helped over one hundred organizations reduce their IT costs. We share detailed best practices on how to calculate detailed apples-to-apples comparisons, how to build the models, and where to look to identify the biggest cost saving opportunities. We are also joined by Vladimir Mitevski, Vice President Product Management Core Services at Thomson Reuters who will walk through by line item how and where their actual operating and capital expenses changed when they migrated to the cloud, so you can learn from their experiences, and take home some
Microsoft StreamInsight, part of the recent SQL Server 2008 R2 release, is a new platform for building rich applications that can process high volumes of event stream data with near-zero latency.
Mark Simms of Microsoft's SQLCAT will demonstrate the core skill sets and technologies needed to deliver StreamInsight enabled solutions, and discuss some of the core scenarios.
Mark will provide a detailed walkthrough of the three major components of StreamInsight: input and output adapters, the StreamInsight engine runtime, and the semantics of the continuous standing queries hosted in the StreamInsight engine.
This presentation includes hands-on demos, including building out a real-time data processing solution interacting with SQL Server and Sharepoint.
You will learn:
• The new capabilities StreamInsight brings to data processing and analytics, unlocking the ability to extract real time business intelligence from streaming data.
• How StreamInsight interacts with and compliments other components of SQL Server and the rest of the Microsoft technology stack.
• How to ramp up on the skills and technology necessary to build out end to end solutions leveraging streaming data sources.
Understanding the Value of the Cloud - Centare Lunch & Learn - June 2, 2011Eric D. Boyd
Cloud computing is one of the most important shifts in computing since PC/Client-Server from the 90s. In this presentation we will reminisce about the major milestones in computing history, look at where we are now, and dream about what the future will look like with the introduction of the cloud. Next, we will examine the challenges of the traditional data center and dig into the benefits and value provided by leveraging the cloud. Finally, we will discuss how you can identify opportunities in your organization that are a good fit for the cloud and explore strategies for getting started.
Cloud architecture and deployment: The Kognitio checklist, Nigel Sanctuary, K...CloudOps Summit
CloudOps Summit 2012, Frankfurt, 20.9.2012 Track 2 - Build and Run
by Nigel Sanctuary, VP Propositions at Kognitio (www.kognitio.com)
http://cloudops.de/sprecher/#nigelsanctuary
Find the video of this talk at http://youtu.be/wQrHQNOMlKc
EvoApp Bermuda (patent pending) is a highly scalable, cloud-native, in-memory analytic engine capable of analyzing large amounts of data extremely fast. Bermuda provides cost-effective, real-time, Big Data analysis and insight for both unstructured and structured data, enabling a wide range of business applications. Bermuda is capable of performing sub-second queries over billions of items, leveraging virtual machines and a cloud-scale storage system providing transactional, persistent storage of data.
In addition to world-leading performance on the data sets for which it is optimized, the other major benefit of Bermuda is that a user does not have to define specific queries ahead of time, as is required with traditional business intelligence systems or a platform like Hadoop. Bermuda was built to support real-time, ad-hoc queries over large datasets. With Bermuda, a user can change queries on the fly, adjusting charts and reports and seeing results immediately. This expands the options associated with analytics on big data--more closely resembling a web search than traditional business intelligence STET reports.
Bermuda can achieve such exceptionally fast query response times because data is organized in a proprietary, patent-pending architecture that facilitates scan-intensive queries. These make up the bulk of business intelligence analytics computations (i.e. time series, computing averages or sums, grouping by day, hour, etc. over large datasets); by optimizing Bermuda for this type of query, the engine is able to allocate workload across hundreds or even thousands of servers, easily accommodating terabytes of information. Additionally, all queries are non-blocking to the writing of new information or updates to existing data.
The Bermuda architecture is unique because it combines the scalability of NoSQL databases, the performance of pure in-memory processing, and the cost/benefit advantages of a cloud-native deployment. It creates value by allowing EvoApp customers to make decisions and gain insights from massive quantities of data in an iterative, real-time environment. This represents a huge advance in the state of the art of unstructured data analytics and delivers on the promise of real-time/ad-hoc queries at scale.
EvoApp Bermuda (patent pending) is a highly scalable, cloud-native, in-memory analytic engine capable of analyzing large amounts of data extremely fast. Bermuda provides cost-effective, real-time, Big Data analysis and insight for both unstructured and structured data, enabling a wide range of business applications. Bermuda is capable of performing sub-second queries over billions of items, leveraging virtual machines and a cloud-scale storage system providing transactional, persistent storage of data.
In addition to world-leading performance on the data sets for which it is optimized, the other major benefit of Bermuda is that a user does not have to define specific queries ahead of time, as is required with traditional business intelligence systems or a platform like Hadoop. Bermuda was built to support real-time, ad-hoc queries over large datasets. With Bermuda, a user can change queries on the fly, adjusting charts and reports and seeing results immediately. This expands the options associated with analytics on big data--more closely resembling a web search than traditional business intelligence STET reports.
Bermuda can achieve such exceptionally fast query response times because data is organized in a proprietary, patent-pending architecture that facilitates scan-intensive queries. These make up the bulk of business intelligence analytics computations (i.e. time series, computing averages or sums, grouping by day, hour, etc. over large datasets); by optimizing Bermuda for this type of query, the engine is able to allocate workload across hundreds or even thousands of servers, easily accommodating terabytes of information. Additionally, all queries are non-blocking to the writing of new information or updates to existing data.
The Bermuda architecture is unique because it combines the scalability of NoSQL databases, the performance of pure in-memory processing, and the cost/benefit advantages of a cloud-native deployment. It creates value by allowing EvoApp customers to make decisions and gain insights from massive quantities of data in an iterative, real-time environment. This represents a huge advance in the state of the art of unstructured data analytics and delivers on the promise of real-time/ad-hoc queries at scale.
The mighty cloud draws businesses and developers who seek its agility and productivity. But which type of cloud is best? We moved eBay Marketplace, a major eCommerce site, from a traditional infrastructure to a cloud model. We will present the strategic, technical and cost factors we weighed when deciding between cloud versus automation, and porting applications versus rewriting them. We will explain why we ended up with a hybrid: developing our own internal cloud while leveraging the massive infrastructure of public cloud providers.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
2. Infrastructure Costs
Traditional Infrastructure Capacity Model
Estimated Demand
Hardware Demand
Actual Demand
Time
3. Infrastructure Costs
Traditional Infrastructure Capacity Model
Estimated Demand
Hardware Demand
Actual Demand
Time
Conclusion:
4. Infrastructure Costs
Traditional Infrastructure Capacity Model
Estimated Demand
Hardware Demand
Actual Demand
Time
Conclusion:
• Large CAPEX due to hardware investment
5. Infrastructure Costs
Traditional Infrastructure Capacity Model
Estimated Demand
Hardware Demand
Actual Demand
Time
Conclusion:
• Large CAPEX due to hardware investment
• Financing overcapacity
6. Infrastructure Costs
Traditional Infrastructure Capacity Model
Estimated Demand
Hardware Demand
Actual Demand
Time
Conclusion:
• Large CAPEX due to hardware investment
• Financing overcapacity
• Customer dissatisfaction when actual demand outperforms available capacity
7. Infrastructure Costs
Jitscale Infrastructure Capacity Model
Estimated Demand
Jitscale Capacity
Actual Demand
Time
8. Infrastructure Costs
Jitscale Infrastructure Capacity Model
Estimated Demand
Jitscale Capacity
Actual Demand
Time
Conclusion:
9. Infrastructure Costs
Jitscale Infrastructure Capacity Model
Estimated Demand
Jitscale Capacity
Actual Demand
Time
Conclusion:
• No investment in hardware; from CAPEX to OPEX
10. Infrastructure Costs
Jitscale Infrastructure Capacity Model
Estimated Demand
Jitscale Capacity
Actual Demand
Time
Conclusion:
• No investment in hardware; from CAPEX to OPEX
• No nancing of overcapacity
11. Infrastructure Costs
Jitscale Infrastructure Capacity Model
Estimated Demand
Jitscale Capacity
Actual Demand
Time
Conclusion:
• No investment in hardware; from CAPEX to OPEX
• No nancing of overcapacity
• Customer satisfaction because capacity automatically scales to meet actual demand
14. Traditional Batch Processing Capacity Model
Infrastructure Costs
Time Hardware Demand
Actual Demand
Conclusion:
• Large CAPEX to meet actual demand peak
15. Traditional Batch Processing Capacity Model
Infrastructure Costs
Time Hardware Demand
Actual Demand
Conclusion:
• Large CAPEX to meet actual demand peak
• Financing overcapacity between batch processes
17. Jitscale Batch Processing Capacity Model
Infrastructure Costs
Time Actual Demand
Jitscale Capacity
Conclusion:
18. Jitscale Batch Processing Capacity Model
Infrastructure Costs
Time Actual Demand
Jitscale Capacity
Conclusion:
• No CAPEX, only operational expenditure
19. Jitscale Batch Processing Capacity Model
Infrastructure Costs
Time Actual Demand
Jitscale Capacity
Conclusion:
• No CAPEX, only operational expenditure
• No need to nance overcapacity between batch processes
20. Jitscale Batch Processing Capacity Model
Infrastructure Costs
Time Actual Demand
Jitscale Capacity
Conclusion:
• No CAPEX, only operational expenditure
• No need to nance overcapacity between batch processes
• Capacity automatically available when needed
21. Traditional Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Hardware Demand
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
22. Traditional Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Hardware Demand
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
23. Traditional Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Hardware Demand
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• Large capital expenditure due to unpredictable demand of campaigns
24. Traditional Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Hardware Demand
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• Large capital expenditure due to unpredictable demand of campaigns
• Inef cient use of available capacity for campaigns
25. Traditional Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Hardware Demand
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• Large capital expenditure due to unpredictable demand of campaigns
• Inef cient use of available capacity for campaigns
• Customer dissatisfaction when campaign becomes too successful
26. Jitscale Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Jitscale Capacity
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
27. Jitscale Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Jitscale Capacity
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
28. Jitscale Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Jitscale Capacity
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• No need to invest in hardware
29. Jitscale Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Jitscale Capacity
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• No need to invest in hardware
• Ef cient use of capacity, since it automatically scales per campaign
30. Jitscale Campaign Capacity Model
Infrastructure Costs
Estimated Demand
Jitscale Capacity
Actual Demand Campaign 1
Actual Demand Campaign 2
Time
Conclusion:
• No need to invest in hardware
• Ef cient use of capacity, since it automatically scales per campaign
• Campaigns are always available, even when very successful
34. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
35. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
Your infrastructure is made available only when necessary, on an hourly basis. Jitscale also manages all
technical aspects of your application and platform pro-actively, 24/7, 365 days a year, anywhere in the
world.
36. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
Your infrastructure is made available only when necessary, on an hourly basis. Jitscale also manages all
technical aspects of your application and platform pro-actively, 24/7, 365 days a year, anywhere in the
world.
Service level agreement
37. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
Your infrastructure is made available only when necessary, on an hourly basis. Jitscale also manages all
technical aspects of your application and platform pro-actively, 24/7, 365 days a year, anywhere in the
world.
Service level agreement
The entire service provided by Jitscale will be detailed in a comprehensive SLA.
38. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
Your infrastructure is made available only when necessary, on an hourly basis. Jitscale also manages all
technical aspects of your application and platform pro-actively, 24/7, 365 days a year, anywhere in the
world.
Service level agreement
The entire service provided by Jitscale will be detailed in a comprehensive SLA.
Pay per success
39. Why Jitscale?
Cloud over cloud infrastructure
Your data can be diffused over multiple clouds.
Auto-scaling infrastructure and management
Your infrastructure is made available only when necessary, on an hourly basis. Jitscale also manages all
technical aspects of your application and platform pro-actively, 24/7, 365 days a year, anywhere in the
world.
Service level agreement
The entire service provided by Jitscale will be detailed in a comprehensive SLA.
Pay per success
You only invest in server capacity and management that is actually used and can cut costs by 75%
compared to traditional infrastructures.
40. Jitscale provides fully managed, on demand, global,
auto-scaling, virtualized and shared IT infrastructure
as-a-service.
For more information, please visit our website: www.jitscale.com