IBM – Building Momentum to Win the Hybrid Cloud Platform War

IBM – Building Momentum to Win the Hybrid Cloud Platform War

IBM – Building Momentum to Win the Hybrid Cloud Platform War

By Ravi Shankar and Srini Chari, PhD., MBA – Cabot Partners, May 8, 2020.

As the evolving impacts of COVID-19 ripple globally through our communities, the new IBM CEO Arvind Krishna kicked-off the virtual IBM Think conference on May 5, 2020 with an apt assertion: “There's no question this pandemic is a powerful force of disruption and an unprecedented tragedy, but it is also a critical turning point.” Krishna said “History will look back on this as the moment when the digital transformation of business and society suddenly accelerated, and together, we laid the groundwork for the post-COVID world”.

Originally set to be held in San Francisco, the IBM 2020 Think Digital Experience quickly became one of the many tech events held online. The attendance was very large. About 100,000 non-IBM participants registered and over 170,000 unique visitors attended sessions and consumed content. On average, IBM clients and Business Partners joined 6.5 sessions and watched most of those sessions. This was 3 times the number of clients and 2 times the number of Business Partners compared to last year.

Think Digital featured many key announcements and offered virtual attendees an exciting array of speaker sessions, real-time Q&As and technical training highlighting how hybrid cloud and artificial intelligence (AI) are galvanizing digital transformation, and how IBM is building an agile and scalable platform for developers, partners and clients to overcome data and applications migration challenges from the edge to the cloud.

Lift and Shift to the Public Cloud is Inadequate for Most Enterprise Workloads[i]

The simplest enterprise workloads – about 20% of all enterprise workloads – have already been moved to the public cloud and have benefited from greater agility and scalability. However, the remaining 80% of workloads continue to remain on-premises.1 Contrary to what many public cloud providers proclaim, traditional Lift and Shift (Figure 1) cloud transformation is not always economical and easy, especially for the analytics and AI journey.

Figure 1: Traditional Lift and Shift of On-Premises Data and Applications to the Public Cloud is Inadequate

If public cloud migration were easy for many enterprise applications and data, most businesses would have already migrated most of their workloads to the public cloud and realized the associated benefits (Table 1). On-premises solutions still provide many benefits especially for analytics and AI (Table 1).

Public Cloud

On-Premises

·     High scalability and flexibility for unpredictable workload demands with varying peaks/valleys

·     Rapid software development, test and proof of concept pilot environments

·     No capital investments required to deploy and maintain infrastructure

·     Faster provisioning time and reduced requirements on IT expertise as this is managed by the cloud vendor

·    Bring compute to where data resides since it is hard to move existing data lakes into the cloud because of large data volumes

·    Supports analytics at the edge and other distributed environments to make immediate decisions

·    Provides dedicated and secure environments for compliance to stringent regulations and/or unique workload requirements

·    High/custom SLA performance and efficiency

·    Retains the value of investments in existing solutions

Table 1: Benefits of Public Cloud and On-Premises Infrastructures for Analytics/AI

To improve the agility and scalability of the remaining 80% of enterprise applications and data, it is possible, with hybrid clouds, to combine the benefits of a public cloud with that of an on-premises infrastructure. 

The Hybrid Multi-Cloud Platform is the New Battleground

Over the last decade, the term “cloud wars” has been used to describe the competition between public cloud providers, AWS, Microsoft Azure, Google Cloud, IBM Cloud and a few others.  But with 80% of the workloads still left on-premises, this is more a rift or a squabble than a war. The real “cloud war” is only beginning for rapidly-growing hybrid cloud platform – particularly for analytics and AI.

The worldwide data services for the hybrid cloud market is expected to grow at a healthy CAGR of 20.53% from 2016 to 2021[i] as enterprises prioritize a balance of public and private infrastructure. Only 31% of enterprises see public cloud as their top priority, while a combined 45% of enterprises see hybrid cloud as the future state.2

Today, large organizations leverage almost five clouds on average. The percentage of enterprises with a strategy to use multiple clouds is 84%[ii]and 56% of the organizations plan to increase the use of containers.[iii] 

Hybrid cloud platforms that support a multi-cloud architecture will be the winning platform in the future, especially as more data is ingested at the edge with the transition to 5G and stored on-premises or in the cloud.

Lift, Sift and Shift Data and Applications for Swift Connect from the Edge to Multi-cloud

In order to win the impending hybrid cloud war, the following four elements must be in place:

  • Lift: Ability to move/process data and applications all the way from the edge to the enterprise or to a multi-cloud environment and migrate workloads efficiently
  • Sift: Automate the current, tedious semi-manual, error-prone processes used to cleanse and prepare data, remove bias, prioritize it for analysis, and provide clear traceability
  • Shift: Ability to move compute to where the data resides to minimize data movement costs and improve performance
  • Swift: Multi-directionally execute all the above operations at scale with agility, flexibility and high-performance so that data can move between public, private and hybrid clouds, on-premises and edge installations, and can be updated as needed
  • Connect: Seamlessly connect the edge, on-premise and multi-cloud implementations to a cohesive and agile environment with a single dashboard for centralized observation across all platform entities.   

Figure 2 depicts this hybrid cloud platform which empowers customers to experiment with and choose the programming languages, tools, algorithms and infrastructure to build data pipelines, train and productionize analytics/AI models in a governed way for the enterprise and share insights throughout the organization from the edge to the cloud.

Figure 2: An Agile Hybrid Cloud Platform for Analytics/AI that Scales from the Edge to Multi-cloud

To win the hybrid cloud war, the platform must scale, be compliant, resilient and agile, and support open standards for interoperability. This gives clients the flexibility to adapt quickly to changing business needs and to choose the best components from multiple providers in the ecosystem.  

The IBM Hybrid Multi-cloud Vision and Key Think 2020 Announcements  

With the acquisition of Red Hat, IBM has laid the foundation to win the hybrid cloud war by enabling clients to avoid the pitfalls of single-vendor reliance. Clients can scale workloads across multiple systems and cloud vendors with increased agility through containers and unify the entire infrastructure from the edge to the data center to the cloud (Figure 3).

Red Hat provides open source technologies to bring a consistent foundation from the edge to on-premises or to any cloud deployment: public, private, hybrid, or multi: 

  • Red Hat OpenShift is a complete container application platform built on Kubernetes – an open source platform that automates Linux container operations and management
  • Red Hat Enterprise Linux and Red Hat OpenShift bring more security to every container and better consistency across environments.
  • Red Hat Cloud Suite combines a container-based development platform, private infrastructure, public cloud interoperability, and a common management framework into a single, easily deployed solution for clients who need a cloud and a container platform.

Figure 3:  IBM Hybrid Multi-cloud Vision and Key New Think 2020 Announcements

For decades, IBM’s core competency has been as a trusted technology provider for enterprise customers running mission critical applications. IBM delivers enterprise systems, software, network and services. Key hybrid cloud offerings include:

  • IBM Cloud Paks (Figure 3) are enterprise-ready, containerized services that give clients an open, faster and more secure way to move core business applications to any cloud. Each of the six IBM Cloud Paks includes containerized IBM middleware and common cloud services for development and management, on top of a common integration layer and runs wherever Red Hat OpenShift
  • IBM Cloud is built on open standards, with a choice of many cloud models: public, dedicated, private and managed, so clients can run the right workload on the right cloud model without vendor lock-in.
  • IBM Systems deliver reliable, flexible and secure compute, storage and operating systems solutions.
  • IBM Services help organizations by bringing deep industry expertise to accelerate their cloud journeys and modernize their environments.

Reinforcing the strength of the existing portfolio of products and offerings, IBM launched several AI and hybrid cloud offerings backed by a broad ecosystem of partners to help enterprises and telecommunications companies speed their transition to edge computing in the 5G era. (Figure 3):

  • IBM Cloud Satellite gives the customer the ability to use IBM Cloud services anywhere — on IBM Cloud, on premises or at the edge — delivered as-a-service from a single pane of glass controlled through the public cloud. IBM Cloud Satellite specifically extends the IBM Public Cloud with a generalized IaaS and PaaS environment, including support for cloud native apps and DevOps, while providing access to IBM Public Cloud Services in the location that works best for individual solutions.
  • IBM Watson AIOps uses AI to automate how enterprises self-detect, diagnose and respond to IT anomalies in real time to better predict and shape future outcomes, focus resources on higher-value work and build more responsive and intelligent networks that can stay up and running longer.
  • IBM Edge Application Manager is an autonomous management solution designed to enable AI, analytics and IoT enterprise workloads to be deployed and remotely managed, delivering real-time analysis and insight at scale. The solution enables the management of up to 10,000 edge nodes simultaneously by a single administrator.
  • IBM Telco Network Cloud Manager runs on Red Hat OpenShift, to deliver intelligent automation capabilities to orchestrate virtual and container network functions in minutes. Service providers will have the ability to manage workloads on both Red Hat OpenShift and on the Red Hat OpenStack Platform, which will be critical as telcos increasingly look for ways to modernize their networks for greater agility and efficiency, and to provide new services today and as 5G adoption expands.

With the Hybrid Cloud Strategy in Place, the Focus is on Execution

The new product announcements and initiatives launched during IBM’s 2020 Think Digital event reinforce IBM’s intent – even during this pandemic – to march full steam ahead to execute on its hybrid multi-cloud vision: Any application can run anywhere on any platform, at scale, wherever data resides, with resilience, agility and interoperability across all clouds on an open, secure and governed enterprise-grade environment.

IBM has also issued the Call for Code challenge to address the current pandemic.   This global challenge encourages innovators to create practical, effective, and high-quality applications based on one or more IBM Cloud services (for example, web, mobile, data, analytics, AI, IoT, or weather) that can have an immediate and lasting impact on humanitarian issues. Teams of developers, data scientists, designers, business analysts, subject matter experts and more are challenged to build solutions to mitigate the impact of COVID-19 and climate change.

With this compelling hybrid strategy and continuing focus of collaboratively solving challenging problems, we believe IBM is well-positioned to execute to win the hybrid cloud war because:

  1. A strong technical, business and sales savvy leadership is in place with Arvind Krishna and Jim Whitehurst (President of IBM).
  2. This pragmatic strategy builds on IBM’s traditional strengths of serving enterprise customers on their cloud journey by providing the much-needed technologies and momentum for modernization.  
  3. In addition to IBM Research, the autonomy that Red Hat enjoys will ensure its entrepreneurial growth culture will continue to be a lightning rod for IBM innovation.
  4. With the focus on an open platform, clients and the partner ecosystem will have the assurance to co-create high-value offerings and services to meet future challenges.

Last and perhaps most important, the technology industry is constantly disrupting, with new billion-dollar businesses emerging rapidly. This century’s first decade witnessed the rise of social media, mobile and cloud computing. As keen observers of IBM both from the inside and outside, we believe this is probably the first time in recent decades that IBM is endowed with a technology and business savvy leadership team that has a track-record of rapidly growing large billion-dollar businesses. As the cloud wars rage in the next decade, there will undoubtedly be many disruptions. Perhaps now more than at any time in the recent past, IBM will not only spot these opportunities but also boldly act to galvanize its incredible human resources and its vast ecosystem to build these next generation hybrid cloud and AI businesses.

[1] Nagendra Bommadevara, Andrea Del Miglio, and Steve Jansen, “Cloud adoption to accelerate IT modernization”, McKinsey & Company, 2018

[1] https://www.ibm.com/downloads/cas/V93QE3QG

[1] RightScale STATE OF THE CLOUD REPORT 2019 from Flexera

[1]  https://www.redhat.com/cms/managed-files/rh-enterprise-open-source-report-detail-f21756-202002-en.pdf

 

Cabot Partners is a collaborative consultancy and an independent IT analyst firm. We specialize in advising technology companies and their clients on how to build and grow a customer base, how to achieve desired revenue and profitability results, and how to make effective use of emerging technologies including HPC, Cloud Computing, Analytics and Artificial Intelligence/Machine Learning. To find out more, please go to www.cabotpartners.com.

 

Copyright® 2020. Cabot Partners Group. Inc. All rights reserved. Other companies’ product names, trademarks, or service marks are used herein for identification only and belong to their respective owner. All images and supporting data were obtained from IBM or from public sources. The information and product recommendations made by the Cabot Partners Group are based upon public information and sources and may also include personal opinions of both Cabot Partners Group and others, all of which we believe to be accurate and reliable. However, as market conditions change and not within our control, the information and recommendations are made without warranty of any kind. The Cabot Partners Group, Inc. assumes no responsibility or liability for any damages whatsoever (including incidental, consequential or otherwise), caused by your or your client’s use of, or reliance upon, the information and recommendations presented herein, nor for any inadvertent errors which may appear in this blog. This blog was developed with IBM funding. Although the blog may utilize publicly available material from various vendors, including IBM, it does not necessarily reflect the positions of such vendors on the issues addressed in this document. 

 

 

 

Published by: Srini Chari

Highlights from the Strata AI Conference in New York City

Last month (September 25 and 26), I attended the Strata AI Conference in New York City. The Strata AI Conference continues to provide an informative and comprehensive overview of artificial intelligence and its accelerating transition from research to industrialization. Sessions covered a broad spectrum of AI topics, including cutting-edge research, open source tools, regulatory considerations, use cases and best practices for implementation.

Over 5,000 people attended the conference. There were over 125 exhibitors and about 170 breakout sessions covering all aspects of AI establishing the Strata conference as one of the unique gathering in the area of Cloud/AI/ML/DL

Considering the number of keynote speeches and breakout sessions, in the interest of space, we will highlight only few of the topics.

The road to an enterprise cloud

Mick Hollison (CMO, Cloudera) discussed the essential elements of enterprise cloud and how Cloudera and its strategic partner IBM are working together in assisting customers to build a true enterprise cloud. He stated that “No one that we work with does more to force the issue around hybrid and multicloud, and to really drive that message home, than our most strategic partner in the world, IBM”.  He informed The IBM + Cloudera strategic partnership reinforces a combined commitment to open source and cloud for Analytics/AI initiatives. It offers clients an industry-leading, enterprise-grade Hadoop distribution plus an ecosystem of integrated products and services – all designed to help organizations industrialize Analytics/AI.

Reader’s may be interested in the latest Cabot Partners publication that describes the strengths of IBM and Cloudera alliance – Greater Choice and Value for Advanced Analytics and AI –  https://www.cabotpartners.com/wp-content/uploads/2018/07/IBM-Cloudera-Alliance-September-2019.pdf .

AI Ladder

Highlights of an interesting discussion between Rob Thomas, General Manager, IBM Data and Watson AI and Tim O’Reilly, Chairman of O’Reilly Media.

AI Ladder: Breaking an AI strategy down into pieces – or rungs of a ladder serves as a guiding principle for organizations to transform their business by providing four key areas to consider: how they collect data, organize data, analyze data, and then ultimately infuse AI into the organization. By using the ladder to AI as a guiding framework, enterprises can build the foundation for a governed, efficient, agile, and future-proof approach to AI.

AI challenges: The challenges companies face can be categorized as follows:

  • Lack of understanding – because of increasing popularity of AI, assume that it will fix any problem – which is not true
  • Getting a handle on their data – good data is essential for a successful AI implementation. Organizations suffer from a combination of lack of data, too much data, and bad data.
  • Lack of relevant skills – AI skills are rare and therefore high in demand and there is a shortage of skilled workers.
  • Trust – as more applications make use of AI, businesses need visibility into the recommendations made by their AI applications. Traceability and explainability are very important
  • Culture and business model change – is required to take advantage of the opportunity the new technology provides.

Concluding thoughts: Fear factor – managers who use AI will replace those who haven’t gotten through the hype phase of AI. Now is the time for AI.

Top blunders in Big Data

Michael Stonebraker, Computer scientist, winner of Turing award had an interesting take on top ten blunders of Big Data (in the interest of space – combined few of them). One need not agree with his list – however, they stimulate thinking and good discussion.

  1. Not planning for AI/ML
  2. Not solving your real data science problem – typical data scientist 90% of the time on data discovery and data cleaning,
  3. Belief that traditional data integration will be solved by data science
  4. Belief that data warehouses, data lakes and Hadoop/Spark will solve all your data science problems
  5. Succumbing to Innovator’s dilemma – got to reinvent yourself
  6. Not paying for few rocket scientists
  7. Outsourcing to an external service provider
  8. Not moving everything to cloud

Conclusion

AI is one of the greatest challenges and opportunities of our time. It will transform entire industries and the way enterprises operate. The pace of innovation continues to accelerate at a phenomenal pace. Events like the Strata AI Conference can provide analytics leaders with valuable insights and an understanding of the future.

Published by: Ravi Shankar

Highlights from the O’Reilly AI Conference in NY City

Last month (April 17 and 18), I attended the O’Reilly AI Conference in New York City. The O’Reilly AI Conference continues to provide an informative and comprehensive overview of artificial intelligence and its accelerating transition from research to industrialization. Sessions covered a broad spectrum of AI topics, including cutting-edge research, open source tools, regulatory considerations, use cases and best practices for implementation. Over 2,000 people attended the conference. Some of the important details of the proceedings shown below.

Computational Propaganda
In his keynote “Computational Propaganda,” Sean Gourley (CEO, Primer) discussed how advances in AI capabilities can be used to manipulate public opinion and threaten democratic institutions.
Sean noted that the financial services industry is being dominated by algorithms because algorithms are faster than humans. Although these algorithms have brought dramatic improvements in market efficiencies, they have also led to unexpected events like the Flash Crash. This incongruity is summarized by Wiener’s Laws, which generally state that automation will routinely tidy up ordinary messes, but will occasionally create an extraordinary mess.
All these advances are leading to the advent of Computational Propaganda where the ability of machines to generate images and language that we can’t tell from reality makes it increasingly difficult to know what is real and what is fake. To emphasize his point, Sean walked through several real-world examples of increasingly sophisticated applications of social bots by state and nonstate actors, including Russia. He showed a video of President Obama making an incendiary speech which was totally fake yet very realistic. In conclusion, Sean states his belief that democracies will need to regulate the use of these technologies and will have to walk a fine line between censorship and freedom of speech.

AI Success Stories
Several keynotes highlighted successful, real-world implementations of ML and AI technologies. These presentations demonstrated the maturation of these powerful technologies while also showing that it takes considerable investment and engineering capabilities to make it a success.

INTUIT
Desiree Gosby (VP of Identify and Profile) discussed how Intuit’s TurboTax unit is using advanced text analytics and image recognition to automatically generate tax returns using mobile phone pictures of tax documents.
Her keynote highlighted both the ability of AI to create disruptive new services and the practical challenges and sophistication required to successful deploy AI into production, which often requires the ability to combine multiple, advanced techniques.
For example, it is difficult for consumers to take high-quality, easy-to-analyze images. Intuit had to deploy a variety of image techniques (edge detection and brightness/focus/ contrast detection) and Convolutional Neural Networks (CNN) models to provide feedback and get usable images. Next, they had to implement multiple techniques (document classification using CNN models and layout matching using Recurrent Neural Networks (RNN) models) to determine the type of tax form and manage the format variability of many forms. Finally, they had to extract the tax information using traditional optical character recognition coupled with natural language processing, entity recognition modeling and contextual random field modeling

NETFLIX
Tony Jebra (Director of Machine Learning) from Netflix discussed how his company uses ML to personalize its service for its 140 million subscribers. ML is deployed to optimize almost all the customer-facing experience, including ranking, page generation, promotion, image selection, search, messaging and marketing.
Tony demonstrated how Netflix uses contextual bandits, instead of batch machine learning, to interleave learning with data collection and to personalize content artwork for each subscriber. A separate ML system selects the content that should interest a subscriber. For each piece of content, Netflix has multiple artwork options that highlight unique elements of the content (genre, themes, actors, age, etc.). Subscriber information, including viewing history and country, is used to choose which artwork to display for a piece of content. As an example, Tony showed how artwork displayed for the Netflix show “Stranger Things” is personalized based on your interest in horror films, sci-fi films or teenage dramas.

MASTERCARD
During his keynote, Nick Curcuru (VP of Data Analytics and Cyber Security) discussed MasterCard’s growing investment in AI. Mastercard has 2.5 billion customers that process 74 billion transactions per year. As fraud threats have become more sophisticated, Mastercard is turning to AI to protect customer data. Mastercard uses AI to provide real- time credit intelligence using hundreds of data points, to provide passive biometric identification for additional security and in its anti-money laundering monitoring. As an example, Mastercard has used AI to increase the number of data points used in card charge authorization from 15 to over 150 in five years.

FACEBOOK
Kim Hazelwood (Senior Engineering Manager, AI Infrastructure) presented how Facebook uses AI and ML to create highly customized experiences for its 2.8B subscribers. Facebook deploys a variety of ML models across its services and features. Multilayer Perception models are used for search ranking, news feed ranking and advertisement displays. Support Vector Machines and CNN are used for automatic facial recognition and image tagging while RNN are used for language translation, speech recognition and content understanding. All these algorithms are deployed at a massive scale. Facebook does over 200 trillion predictions per day and does over 6 billion language translations per day.

Conclusion
Analytics, Artificial Intelligence (AI) and Machine Learning (ML) are profoundly transforming how businesses and governments engage with consumers and citizens. Across many industries, high value transformative use cases in personalized medicine, predictive maintenance, fraud detection, cybersecurity and more are rapidly emerging. The pace of innovation continues to accelerate. events like the O’Reilly AI Conference can provide analytics leaders with valuable insights and an understanding of the future.

Published by: Ravi Shankar

Total Value of Ownership (TVO) Assessment of the IBM Private Cloud for Data Solution for Analytics

For strategic reasons, IT leaders are focused on moving existing workloads to the cloud, or building new workloads on the cloud and integrating those with existing workloads. Quite often, the need for data security and privacy makes some organizations hesitant about migrating to the public cloud. The business model for cloud services is evolving to enable more businesses to deploy a hybrid cloud, particularly in the areas of big data and analytics solutions.

 

IBM Cloud Private (ICP) for Data is an integrated data science, data engineering and app building platform built on top of ICP – a hybrid cloud that provides all the benefits of cloud computing inside the client’s firewall and provides a migratory path should the client want to leverage public clouds. ICP for Data clients can get significant value because of unique capabilities to connect their data (no matter where it is), govern it, find it, and use it for analysis. ICP for Data also enables users to collaborate from a single, unified interface and their IT staff doesn’t need to deploy and connect multiple applications manually.

 

These ICP for Data differentiators enable quicker deployments, faster time to value, lower risks of failure and higher revenues/profits. They also enhance the productivity of data scientists, data engineers, application developers and analysts; allowing clients to optimize their Total Value of Ownership (TVO), which is Total Benefits – Total Costs.

 

The comprehensive TVO analysis presented in this paper compares the IBM Private Cloud for Data solution with a corresponding In-house solution alternative for three configurations – small, medium and large. This cost-benefit analysis framework considers cost/benefit drivers in a 2 by 2 continuum: Direct vs. Derived and Technology vs. Business mapped into four quantified quadrants: Costs, Productivity, Revenues/Profits and Risks.

 

Compared to using an In-house solution, IBM Cloud Private for Data can improve the three-year ROI for all three configurations. Likewise, the Payback Period (PP) for the ICP for Data solution is shorter than the In-house solution; providing clients faster time to value. In fact, these ROI/PP improvements grow with configuration size; offering clients better investment protection as they progress in their Analytics and AI/ML journey and as data volumes and Analytics model complexities continue to grow.

For more details please click here

Published by: Ravi Shankar

The changing face of HPC in finance

Carrots and sticks: How new challenges are bringing fresh opportunities for HPC, data analytics, and AI

This Cabot Partners article sponsored by IBM was published at HPCwire at this URL. This article is identical, but graphics are presented in higher resolution.

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For decades, banks have relied on high-performance computing (HPC). When it comes to problems too hard to solve deterministically (like predicting market movements), Monte Carlo simulation is the only game in town.

Banks use proprietary pricing models to calculate the future value of various financial instruments. They generate thousands of scenarios (essentially randomized vectors of self-consistent risk factors), compute the value of each instrument across every scenario (often over multiple time steps) and roll-up the portfolio value for each scenario. The result is a probability distribution showing the range of probable outcomes. The more sophisticated the models, and the more scenarios considered, the higher the confidence in the predicted result.

Analysts often focus on value-at-risk (VaR) or left-tail risk referring to the left side of the curve representing worst-case market scenarios. Risk managers literally take these results to the bank relying on computer simulation to help grow portfolio values and manage risk.

Accuracy and speed provide a competitive edge

In the game of financial risk, both speed and accuracy matter. Banks need to maximize gains while maintaining capital reserves adequate to cover losses in worst-case scenarios. Nobody wants cash sitting idle, so accurately assessing VaR helps banks quantify needed reserves, maximize capital deployed, and boost profits.

Simulation is used for dozens of financial applications including model back-testing, stress-testing, new product development, and developing algorithms for high-frequency trading (HFT). Firms with more capable HPC can run deeper analysis faster, bid more aggressively, and model more scenarios pre-trade to make faster better-informed decisions.

While traditional HPC is often about raw compute capacity (modeling a vehicle collision in software for example), financial simulation demands both capacity and timeliness. Reflecting this need for urgency, vendors have responded with specialized, service-oriented grid software purpose-built for low-latency pricing calculations. State-of-the-art middleware can bring thousands of computing cores to bear on a parallel problem almost instantly with sub-millisecond overhead. As financial products grow in complexity, banks increasingly compete on the agility, capacity, and efficiency of their HPC infrastructure.

Post-2008 the plot thickens

Much has been written about the financial crisis of 2008, but an obvious consequence for banks has been an increase in regulation. 2008 served to highlight systemic vulnerabilities to credit risk, liquidity risk, and swap markets trading derivative products.

While these risks were already known, the crisis re-enforced that in addition to left-tail risk (the probability of market losses), firms also needed to emphasize right-tail risk (or credit risk), when paper gains become so large that counterparties are forced into insolvency (potentially leading to cascading bankruptcies). Faced with unpopular government bailouts, politicians and regulatory bodies unleashed a torrent of regulation including Basel III, Dodd-Frank, CRD IV and CRR, Solvency II all aimed at avoiding a repeat of the crisis.

Modeling Counterparty Credit Risk (CCR) is much harder than market risk, so banks were again forced to re-tool, investing in systems to calculate new metrics like Credit Value Adjustments (CVA), a fair-value adjustment to the price derivatives taking CCR into account.

The latest round of regulation affecting banks is Full-Review of Trading Book (FRTB), the regulation published by the Basel Committee on Banking Supervision expected to go fully into effect by 2022. FRTB will require banks to adopt Standard Approaches (SA) and compute and report on a variety of new metrics further increasing infrastructure requirements and compliance costs.

As new workloads emerge, banks become software shops

As if banks didn’t have enough on their plate already, competitive pressures are forcing them to invest in new capabilities for reasons unrelated to regulation. Big data environments are used for a variety of applications in investment and retail banking including cultivating loyalty, reducing churn, boosting cyber-security, and making better decisions when extending credit. Banks increasingly resemble software companies, employing hundreds of developers as software becomes ever more critical to competitiveness.

Faced with competition from alt-lenders and online start-ups, financial firms are racing to leverage Artificial Intelligence (AI) to improve service delivery and reduce costs. Today AI is being used in robo-advisors, improved fraud detection, and applications like loan and insurance underwriting. New predictive models rely on machine learning to supplement traditional predictive methods and enable better quality decisions. Applications on the horizon include automating customer service with chatbots, automating sales recommendations, and leveraging AI for deeper analysis of big data sources like news feeds to improve decision quality further.

An abundance of frameworks compounds infrastructure challenges

Banks face both challenges and opportunities. On the one hand, competitive pressures and new regulations are forcing banks to make new investments in systems and software; on the other hand, advances in technologies like AI, cloud computing, and container technology promise to reduce cost, improve agility, and boost competitiveness.

As new capabilities are added, legacy systems don’t go away, so it’s imperative that the high-performance infrastructure supports multiple software frameworks. Banks need to run not only core risk analytics, but big data (Hadoop and Spark), streaming analytics, and scalable software environments for training and deploying deep learning models. In the age of big data, information increasingly resides in distributed, scaled-out systems including not only HDFS and HBase but distributed caches, object stores, and NoSQL stores like Cassandra MongoDB.

Beyond simply considering where to run these applications (on-premises, in public clouds or both) the real challenge is the diversity of frameworks. Adding more compute capacity or additional siloed systems is not the answer. Banks need solutions that will provide flexibility and help them operate scaled-out high-performance environments more efficiently.

A shared infrastructure for high-performance financial workloads

As banks grapple with new regulation and embrace new technologies to deliver services more efficiently, many are seeing this as an opportunity to re-think their infrastructure. Production-proven in the world’s leading investment banks, IBM Spectrum Computing is a proven solution for accelerating and simplifying the full-range of high-performance financial applications including AI, big data, and risk analytics. Spectrum Computing can help banks seamlessly and efficiently share infrastructure resources on-premises or on their preferred cloud platform with minimal disruption to existing systems.
To learn how you can simplify and consolidate application environments, and build a future-proof, cloud-agnostic IT infrastructure, download IBM’s whitepaper Modernizing your Financial Risk Infrastructure with IBM Spectrum Computing.

 

 

 

Published by: Gord Sissons

Delivering superior throughput for EDA verification workloads

During September of 2018, members of our Cabot Partners team had the opportunity to work with talented engineers and industry experts from HPE, Cadence, Marvell, and Arm. We collaborated to develop a whitepaper and conduct preliminary benchmarks showing how new Cadence tools optimized for multi-core systems benefit from Marvell ThunderX2 Arm-based systems such as the HPE Apollo 70 system.

Arm datacenter systems are fast becoming a force to be reckoned with in HPC. You can read our published HPE whitepaper here.

Perhaps no industry is more competitive than modern electronics manufacturing and chip design. As consumers, we take it for granted that electronic devices continue to get faster, cheaper, and more capable with each generation. From smart watches to industrial controls to electronic heart-rate monitors, electronics manufacturers are challenged to build smarter, more complex devices leveraging system-on-a-chip (SoC) designs for an increasingly connected world. With the number of IoT devices forecast to grow to over 75 billion by 2025, consumers and manufacturers are all about durability, safety, battery life, and security including resistance to malware and hacking attempts.

This level of change is unprecedented. Chip designers are faced with seemingly irreconcilable pressures such as shorter product design cycles, increasing complexity, increased requirements for quality, and continuous pressures on costs.

In this paper, we discuss the challenge of device verification, a key issue in EDA, and explain how new high-performance systems and software are promising to improve the economics of chip design enabling firms to innovate faster and create high-quality products.

 

 

Published by: Gord Sissons

Fueling High Performance Computing (HPC) on Clouds with GPUs

Businesses are increasingly investing in HPC to manufacture higher quality products faster, optimize oil and gas exploration, improve patient outcomes, detect fraud and breaches, mitigate financial risks, and more. HPC also helps governments respond faster to emergencies, analyze terrorist threats better and accurately predict the weather – all vital for national security, public safety, and the environment. The economic and social value of HPC is immense.

 

HPC workloads are also getting larger and spikier with more interdisciplinary analyses, higher fidelity models, and larger data volumes. Hence, managing and deploying on-premises HPC is getting harder and more expensive, especially as the line between HPC and analytics is blurring in every industry.   Businesses are also challenged with rapid technology refresh cycles, limited in-house datacenter space, skills to cost-effectively operate an on-premises HPC environment customized to match performance, security and compliance requirements. So, businesses are increasingly considering cloud computing. Hence, HPC on the cloud is growing at over 4 times the growth rate of HPC.

 

As a pioneer in cloud computing, Amazon Web Services (AWS) continues to innovate and overcome many past issues with using public clouds for HPC. AWS is fueling the rapid migration of HPC to the cloud with some key differentiators such as the NVIDIA GPU-enabled cloud instances for compute and remote visualization and a growing ecosystem of highly-skilled partners.

 

Likewise, NVIDIA, as the leader in accelerated computing for HPC and Artificial Intelligence/Deep Learning (AI/DL), continues to invest in building a robust ecosystem of software for highly parallel computing. A recent analyst report shows that 70% of the most popular HPC applications, including 15 of the top 15 are accelerated by GPUs. These provide upwards of two orders of magnitude of speed up compared to CPUs. AWS and NVIDIA GPU Cloud ( NGC) container registry available on AWS marketplace provide NVIDIA GPU-optimized containers to simplify deployment of key HPC applications. AWS and NVIDIA is a winning combination for HPC.

 

This winning combination accelerates large-scale HPC workflows from data ingestion to computing to visualization with flexibility, reliability, and security. Further, it fosters unprecedented collaborative innovation between engineers and scientists, converts capital costs to usage-based operational costs and keeps pace with technology refresh cycles. 

 

Prominent real-world client examples highlighted here span many industries: manufacturing, oil and gas, life sciences and healthcare, and more. These examples demonstrate how AWS and NVIDIA help in reducing costs, enhancing productivity, increasing revenues and profits, and lowering risks for HPC clients.

For more details, please see – https://www.cabotpartners.com/wp-content/uploads/2018/10/FuelingHighPerformanceComputingCloudwithGPUs-September-2018.pdf

Published by: Ravi Shankar

From a Hunch to a Punch: Winning with Analytics

I am writing this after recently attending three conferences focused on Big Data Analytics in New York:
 

  1. HPC on Wall Street
  2. Strata Hadoop
  3. IBM’s Analytics for All.
The technical sessions and the end-user discussions in particular were superb and enlightening. Beyond, typical discussions on technology (Hadoop, Spark, Infrastructure, etc.), the end-user panels repeatedly emphasized that organizational and cultural changes were critical to implement and integrate Analytics into core business process and drive value and competitive advantage.

Analytics is a Journey of Progressive Value Creation and Competitive Advantage

 
Analytics is not just about implementing a set of technologies or solutions, although, this is a necessary foundation for end-users to get derive business value. Analytics is actually a journey from a hunch to the punch where data is iteratively and progressively analyzed by the user and the system to produce powerful insights and punchy actions to outflank competitors and improve profitability.

Progressing From Data to Information to Insights to Action to Wisdom

 

The prior figure depicts how clients can progressively get time critical insights and higher rewards. But the risks are also higher. The Analytics journey starts with Data and advances to Descriptive, Predictive and Prescriptive Analytics.

Data is fundamental in any Analytics initiative. A data warehouse is typically built to capture, store, secure, retrieve and manage the raw and processed data. Today, data warehousing is widely used by clients and traditional implementations are usually low risk activities. Modern warehouses with newer technologies such as Hadoop and Apache Spark are riskier to implement. But unless data is converted to insights, there is little reward.
Descriptive analytics is dominant today and with low to medium risk and reward. It condenses data into nuggets of insights summarizing what happened. Social media analytics is one prominent descriptive analytics example.

Predictive analytics (medium risk and reward) uses a combination of several statistical, modeling, data mining, and machine learning techniques to analyze data to make probabilistic time-critical forecasts about the future. Weather prediction and customer sentiment analysis are some noteworthy predictive analytics examples.
Prescriptive analytics goes beyond descriptive and predictive analytics. It recommends one or more courses of action and the likely outcome of each action, including the usually time-critical “next best action”. IBM’s Predictive Customer Intelligence solution is one example.
Cognitive computing systems continuously build knowledge over time by processing natural language and data. These systems learn a domain by experience just as humans do and can discover and suggest the “best course of action”; providing highly time-critical valuable guidance to humans. Cognitive computing spans Descriptive, Predictive and Prescriptive Analytics. IBM’s Watson is the premier cognitive system in the market today.
Judicious investments in progressive Analytics competencies give clients unprecedented capabilities to not only validate their hunch, but also deliver a punch through fact-based Cognitive Analytics. These prudent investments, using Financing or Cloud subscription models based on achieving milestones, provide an effective way to mitigate risks while maximizing rewards; sparking widespread adoption of Analytics.

Delivering the Analytics Punch with Cognitive Computing

  
After the dramatic win at the Jeopardy contest, IBM Watson received considerable buzz in the industry as a supercomputer with unparalleled intelligence. This unfortunately gave the impression that implementing IBM Watson is exotic and complex.

But with the recent IBM Watson Analytics initiative with Expert Storybooks unveiled at the Analytics for All event in New York, a large portion of the software technologies underpinning the IBM Watson supercomputer is now accessible to all and easy to implement. In fact, IBM claims to have over 500,000 (small and medium business and enterprise) users for Watson Analytics just in the last several months. This could be the one of the fastest adoption rates of Analytics software in history, mirroring Apache Spark which is a critical Analytics software infrastructure for implementing deep learning and cognitive computing.
Armed with critical and unique insights and recommendations from Analytics, get ready to punch your competition!

Published by: Srini Chari

Hey Supercomputer, Pause and Smell the Roses! Enjoy these Fall Colors!

I received an email on my Smart Phone a while ago from Herb Schultz (Marketing Manager, IBM Systems and Technology Group for Technical Computing and Analytics) with the agenda for IBM’s Technical Computing IT Analyst meeting the following week in NYC. I was having lunch then at one of my favorite restaurants in Duchess County, New York – McKinney and Doyle. While munching some delectable crispy calamari and realizing that the agenda covered Cognitive Computing, a train of thoughts sprouted in my mind. Was IBM planning to brief us on supercomputers that can smell, see, hear, touch and taste?

I was told once by a gourmand that fine food must appeal to all our five basic senses. As our waiter came from behind me and brought the food, I first smelled a pleasing aroma. Then, the calamari served over citrus coleslaw with sweet and sour chili sauce was a gorgeous sight. Each calamari piece that I touched was perfect – not soggy or oily. When I placed the warm, crispy and coarse calamari on my tongue, it produced a delightful tingle. I then began slowly munching each piece; enjoying the gentle rhythmic crackling sound with every crunch while simultaneously savoring the mouthwatering taste. I thought – Is IBM going to tell me that all these fine sensations will soon be replaced by a computer? Being a foodie, that prospect was disappointing!

Fortunately No! – It’s Also about the Next Evolution of Big Data Analytics!

 
When I went for the briefing the following week in New York, I realized what IBM’s vision and path to Cognitive Computing is not illusory, exotic and far out into the future. But it is a natural evolution of a Big Data trend that’s happening today in areas such as healthcare and in other industries that are leveraging Big Data for Insights, Knowledge and Wisdom. In many ways, this vision of Cognitive Computing is similar to the Georgia Tech Cognitive Computing Laboratory vision.

A few weeks later at the IBM Edge Conference, when I was having a dinner (again savoring another delectable appetizer) with Jay Muelhoefer (Worldwide Marketing Executive, IBM Platform and Technical Computing) and his team, I learnt that IBM had won a major competitive deal with an Asian Communications Service Provider (CSP) running a Big Data workload. Jay suggested that it would be good to write up this case study to highlight the use of Big Data in Telecommunications and how IBM improved Customer Service for this CSP.
 
For the customary Cabot Partners’ fee, I signed up to write this paper – Big Data: Delivering an Agile Infrastructure for Time-Critical Analytics in Telecommunications – which you can download by filling out a simple form.

Supercomputing: It’s more than Just About Absolute Performance

 
Just like the right balance of the five senses satiates and heightens an individual’s dining experience, to maximize business value, supercomputers must possess five critical elements – performance, reliability, manageability, efficiency and serviceability. This requires – as illustrated by the Telecom example – a combination of hardware, software and skilled people all working in tandem to maximize a client’s business value – just like a memorable dining experience requires fine food made with the freshest ingredients, a nice ambiance and great company.

As We Hurtle Towards Supercomputing 2013, Chill! There’s Value in Slowness!

 
When the world’s fastest supercomputers are unveiled next month in Denver, Colorado, there will undoubtedly be much media hoopla around the Top500 list. IT vendors will compete fiercely as they always do. They will incessantly brag about their position in this list and how fast their systems are in the Top500 benchmark. But amidst all this noise, you should also be cognizant of the other key elements (senses) that must also be measured and highlighted for supercomputers (cognitive computers).

But pause, let’s step back, take a deep breath and realize that according to some philosophers this sensory world is just an illusion – They call it Maya! In a Mayan world, does it matter that computers can smell, see, touch, hear and taste?

Published by: Srini Chari

Let’s Venerate the Centenarian Duo: One in Duodecimal the other in Regular Decimal

I called my parents in India this morning. I do this almost every day. But I realized that today (Oct/2) is Mahatma Gandhi’s birthday and his 144th birth anniversary. Then, during my morning jog, in the neighborhood park, on this lovely autumn day in Connecticut, a train of thoughts rushed through my mind. I want to keep this short — especially since I am now reading a book titled ;How to Write Short&lt — and post it on the Cabot Cloud Computing blog because everything is in the cloud these days!

In addition to my parents and grandfather, growing up as a young boy and attending the St. Vincent’s School in Pune, India, I was influenced by two centenarians who recently celebrated their centennial birth anniversary: Rev. Fr. Rudolf Schoch (100 years) and the other Mahatma Gandhi (144 (12 * 12) years – “100” in the Duodecimal (base 12) number system). Mankind decided to broadly adopt and popularize the decimal system out of sheer serendipity and convenience instead of alternative systems such as the duodecimal system. So today, we have ten digits instead of a possible twelve digits in the duodecimal system or sixteen in the hexadecimal system, etc.

Fr. Schoch was a great admirer of Gandhi. I have fond memories of listening to him teach classes on Gandhian thought and values. He was our principal at St. Vincent’s when I first entered St. Vincent’s in Grade 1. Then he returned as principal again when I was in Grade 10. I consider myself privileged to have had the opportunity to learn about Gandhi from Fr. Schoch. I paid close attention to his classes despite my singular fascination, those days, with math – even at the risk of failing at other subjects. Today, I am glad to have listened and learned from Fr. Schoch. He and his many Jesuit contemporaries built the strong educational system (schools and colleges) in India. Their efforts continue to make our world a better place.

So, let’s celebrate the centennial birthdays (in two different number systems) of two great men who each toiled to uplift mankind – one very personal to me – a Jesuit priest who dedicated his life to educate the poor and needy “making gentlemen out of boys” and the other a freedom fighter and an apostle of non-violence “shining light on a nation”. Yes – the intense focus of modern educators these days is on numbers and letters (reading, writing and arithmetic) and science, technology, engineering and mathematics (STEM). But when the cloud clears, the enduring lesson from these two centenarians is pure and simple: focus on building enduring values.

Published by: Srini Chari

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