«I focus on what we are calling CAST Highlight, which is our SaaS application portfolio analysis tool. It is an extremely lightweight tool that can integrate with pretty much any build process right now,» explained Andrew Siegmund, Application Migration Specialist for CAST, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.

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Want to know a secret? Amazon Web Services (AWS) is beefing up its government capability by announcing the AWS Secret Region, which can operate workloads up to the highest level of security classification.

The move, claims AWS, makes it the only commercial cloud provider to offer regions serving workloads across the full range of data classifications, from ‘unclassified’, to ‘sensitive’, to ‘secret’, and then finally to ‘top secret’.

John Edwards, Central Intelligence Agency (CIA) CIO, said that the Secret region is a ‘key component of the Intel Community’s multi-fabric cloud strategy’, while Teresa Carlson, vice president for AWS worldwide public sector, added it was an ‘important milestone’.

“The U.S. Intelligence Community can now execute their missions with a common set of tools, a constant flow of the latest technology and the flexibility to rapidly scale with the mission,” said Carlson.

The region will complement the work Amazon already does with the CIA. Regular readers of this publication will remember the protracted legal action between AWS and IBM in 2013 with the final decision going to AWS in October of that year. Speaking at an industry event in early 2015 Doug Wolfe, the then-CIO at the CIA, confirmed AWS’ cloud had attained “final operational capability”, and praised the company for getting the project up and running in less than 18 months.

“Ultimately, this capability allows more agency collaboration, helps get critical information to decision makers faster, and enables an increase in our nation’s security,” added Carlson.

Last month, Microsoft announced the launch of new Azure government capabilities for classified mission-critical workloads, called Azure Government Secret. “Azure Government is the mission-critical cloud, providing more than 7,000 federal, state, and local customers the exclusivity, highest compliance and security, hybrid flexibility, and commercial-grade innovation they need to better meet citizen expectations,” wrote Tom Keane, Microsoft Azure head of global infrastructure.

As many know, the first generation of Cloud Management Platform (CMP) solutions were designed for managing virtual infrastructure (IaaS) and traditional applications. But that’s no longer enough to satisfy evolving and complex business requirements.
In his session at 21st Cloud Expo, Scott Davis, Embotics CTO, explored how next-generation CMPs ensure organizations can manage cloud-native and microservice-based application architectures, while also facilitating agile DevOps methodology. He explained how automation, orchestration and governance are fundamental to managing today’s hybrid cloud environments and are critical for digital businesses to deliver services faster, with better user experience and higher quality, all while saving money.

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To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources.
In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to show how companies develop, deploy, and dynamically update these applications and how this data-first approach is fundamentally different from traditional applications. He covered examples of how leading companies have identified ways to simplify data streams in a publish-and-subscribe framework (for example, how focusing on a stream of electronic medical records simplified the deployment of real-time applications for hospitals, clinics, and insurance companies). He also detailed how a data-first approach can lead to rapid deployment of additional real-time applications as well as centralize and simplify many data management and administration tasks.

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With AWS re:Invent just around the corner, another piece of research which affirms Amazon’s top position in the cloud infrastructure biz; IHS Markit says the company secured more than a quarter of total infrastructure as a service (IaaS) revenue in the first half of 2017.

Total revenues for what IHS defines as off-premises cloud services – IaaS, PaaS, SaaS and the curious ‘cloud as a service’ (CaaS) – grew 26% year over year to total $72 billion (£54.4bn), while IBM took first place for SaaS revenue, grabbing 19% of the market. By 2021, this number is forecast to hit $343 billion, at a CAGR of 22%.

In terms of trends fuelling cloud providers’ strategies, IHS argues CSPs are ‘continually looking for ways to innovate by integrating machine learning and artificial intelligence techniques into their services.’ Alongside this, container technologies were cited – as was the importance of partnerships in what can be considered a relatively mature ecosystem.

“The competitive landscape has changed as off-premises cloud service providers establish strong relationships with key companies that have large enterprise customer bases,” wrote Cliff Grossner and Devan Adams, IHS Markit analysts. “Providers cannot depend on mergers and acquisitions as their sole paths to growth.”

Key partnerships cited include IBM and VMware’s deals, as well as the move by Google, Pivotal and VMware to launch Pivotal Container Services (PKS), enabling enterprises and service providers to use Kubernetes on VMware vSphere and Google Cloud Platform. On the acquisition side, Rackspace’s buy of Datapipe was cited – officially completed last week – as was Digital Realty’s merger with DuPont Fabros in the data centre realm.

AWS re:Invent, taking place from November 27 to December 1, will see a litany of news from the major players, the partners and the hangers-on. As for the company itself, the most recent financial analysis from Synergy Research argued the major players were all progressing as usual, with AWS ‘in a league of its own’ and Microsoft the best of the rest, ahead of Google and Alibaba. Amazon posted total revenue across all segments of $43.74bn for its most recent quarter, with AWS contributing just over 10% of that figure at $4.58bn.

This all dovetails with a recent two-part opinion article from Robert X. Cringely, the first of which detailing how cloud computing has reached its tipping point, and the second opining on how Amazon will become the new Microsoft – or rather, the old, pre-Satya Nadella walled garden Microsoft.

You can find out more about the IHS Markit report here (subscription required).

Most of us understand that artificial intelligence (AI) offers opportunities for productivity improvements in the form of speed, automation, standardized actions and responses, plus the opportunity for continuous improvements via machine learning. These opportunities are enabled by data inputs that are analyzed and processed through AI algorithms that execute a desired decision and action. For all of the great capabilities and benefits that AI can provide, there is also a potential dark side. AI solutions can easily codify our prejudices, bias, gender stereotypes and promote injustices intentionally or unintentionally. This threat, as real and serious as it is, can also be seen as an opportunity to evaluate who we are, what we want the future to look like, and then codify a better tomorrow.

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Most of us understand that artificial intelligence (AI) offers opportunities for productivity improvements in the form of speed, automation, standardized actions and responses, plus the opportunity for continuous improvements via machine learning. These opportunities are enabled by data inputs that are analyzed and processed through AI algorithms that execute a desired decision and action. For all of the great capabilities and benefits that AI can provide, there is also a potential dark side. AI solutions can easily codify our prejudices, bias, gender stereotypes and promote injustices intentionally or unintentionally. This threat, as real and serious as it is, can also be seen as an opportunity to evaluate who we are, what we want the future to look like, and then codify a better tomorrow.

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Blockchain is a shared, secure record of exchange that establishes trust, accountability and transparency across business networks. Supported by the Linux Foundation’s open source, open-standards based Hyperledger Project, Blockchain has the potential to improve regulatory compliance, reduce cost as well as advance trade. Are you curious about how Blockchain is built for business? In her session at 21st Cloud Expo, René Bostic, Technical VP of the IBM Cloud Unit in North America, discussed the basics of Blockchain, previewed the Blockchain Reference Architecture, and introduced the mechanics of a Blockchain Business Network.

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Many IT organizations have come to learn that leveraging cloud infrastructure is not just unavoidable, it’s one of the most effective paths for IT organizations to become more responsive to business needs.

Yet with the cloud comes new challenges, including minimizing downtime, decreasing the cost of operations, and preventing employee burnout to name a few. As companies migrate their processes and procedures to their new reality of a cloud-based infrastructure, an incident management solution can and should be adopted to help overcome these challenges. This is particularly true when larger enterprises operate with a hybrid environment, which mixes traditional infrastructure with cloud-based infrastructure and, by extension, requires a hybrid approach to incident management. It takes time to migrate to the cloud, it’s not something that can be done in one sweep.

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While Artificial Intelligence (AI) may not be a new concept, its contribution to automation may just change the face of business. AI’s conception dates as far back as 1950, when Alan Turing proposed the Turing test in order to evaluate a machine’s ability to exhibit intelligent behavior. Fast-forward a couple of decades and research led to the creation of well-known theoretical tools such as Fuzzy Logic, Bayesian Networks, Markov Models and Neural Networks. Concurrently, new types of programming languages such as Prolog, LISP and Smalltalk set the scene for most of the modern interpreted languages used today.

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