VMware completes acquisition of Pivotal, connects infrastructure and application owners to boost software delivery, business outcomes

VMware announced Monday that it has completed the acquisition of Pivotal Software. With the completion of the acquisition, Pivotal’s Class A common stock was removed from listing on the New York Stock Exchange with trading suspended prior to the open of the market today, and Pivotal will now operate as a wholly owned subsidiary of VMware. The transaction represented an enterprise value for Pivotal of approximately $2.7 billion.

Under the terms of the transaction, Pivotal’s Class A common stockholders are entitled to receive $15.00 per share cash for each share held (without interest and less applicable tax withholdings), and Pivotal’s Class B common stockholder, Dell Technologies, received approximately 7.2 million shares of VMware Class B common stock, at an exchange ratio of 0.0550 shares of VMware Class B common stock for each share of Pivotal Class B common stock.

Pivotal’s offerings will be core to the VMware Tanzu portfolio of products and services designed to help customers transform the way they build, run and manage their most important applications, with Kubernetes as the common infrastructure substrate. 

The combination of Pivotal’s developer-centric offerings with VMware’s upstream Kubernetes run-time infrastructure and management tools will deliver a comprehensive enterprise solution that enables dramatic improvements in developer productivity in the creation of modern applications. VMware is able to offer product building blocks and integrated solutions that are tested and proven with technical expertise that customers need to accelerate software delivery across data center, cloud and edge environments.

“It's my pleasure to announce Ray O'Farrell as the leader of VMware’s new Modern Applications Platform business unit—uniting the Pivotal and VMware Cloud Native Applications teams,” said Pat Gelsinger, CEO, VMware. “And as Pivotal is now part of VMware, I want to thank the Pivotal leadership team for building a great company. Together, we’re poised to be the leading enabler of Kubernetes with a deep understanding of both operators and developers.”

“Digital transformation and the applications that drive it should not be restricted only to cloud and software giants,” said Ray O’Farrell, executive vice president and general manager, Modern Applications Platform Business Unit, VMware. “We believe that modern application development solutions and practices need to be easily accessible to everyday enterprises across the globe. With Pivotal’s developer capabilities as the foundation, we’ll focus on delivering consumable, enterprise-ready cloud native offerings to customers to help them achieve better business outcomes.”  

“Pivotal has fundamentally changed how the world’s biggest brands build and manage software with a focus on developer productivity through platform abstractions and development techniques as well as connecting the business with the developer,” said Edward Hieatt, senior vice president, customer success, Pivotal. “The combination of Pivotal and VMware offers the most comprehensive application platform in the industry and is a win for our customers, a win for Pivotal, and a win for VMware. We’re excited to team up with VMware to help more enterprises become like modern software companies by adopting DevOps and Lean techniques developed by internet giants and the startup community.”

Numerous mutual customers including Raytheon have reacted positively to the news of the acquisition. “By working with both Pivotal and VMware, we’ve been able to completely transform how we write software for our military and government customers,” said Todd Probert, vice president for C2, Space and Intelligence at Raytheon. “Combining these companies under a single umbrella is going to make it possible for my team to get code to our customers even faster and easier.”

Portshift syncs Kubernetes policies to container vulnerabilities in CI/CD pipelines for remediation

Portshift announces its new capability that delivers runtime policies for vulnerability remediation, allowing more secure workload communications. Portshift’s risk mitigation engine  connects Kubernetes network policies with discovered vulnerabilities in production workloads, allowing to mitigate the risk potential of vulnerable containers till its replacement with new version that remove the vulnerable component.

With Portshift, the company has taken DevSecOps to the next level with a platform that connects identified vulnerabilities with the identity of the workload, providing a measured balance that prevents workload communications based on the risk level and the potential threat to certain applications.

The technology has the ability to block traffic based on the vulnerability level discovered, providing a single picture for complete visualization of these processes during runtime. This provides protection that is matched to the DevOps applications in production.

According to a 2019 Gartner report, “Security can’t be an afterthought. It needs to be embedded in the DevOps process, which Gartner refers to as “DevSecOps…Integrate an image-scanning process to prevent vulnerabilities as part of an enterprise’s continuous integration/continuous delivery (CI/CD) process, where applications are scanned during the build and run phases of the software development life cycle.”

Portshift mitigates vulnerabilities with greater sophistication. Available as part of the company’s identity-based cloud native workload security and risk management platform, the technology ensures that Kubernetes environments are protected from development to runtime. With Portshift, app security is simplified and speeded-up by replacing multiple fragmented firewalls, security groups, and ACLs with automated identity-based workload security that is decoupled from the network infrastructure.

When unknown, and possibly malicious workloads are detected, they are quickly identified and rapidly removed using Portshift’s innovative DevOps security platform. The company’s workload management processes offer an alternative to the use of IP addresses, ports and firewalls to secure the network perimeter as it addresses the unique security requirements of cloud-native microservices running in containers both inside and outside of the network perimeter.

“With the availability of this identity-based approach, we are actively collaborating with industry leading vulnerability scanning providers including Twistlock, Aqua and Clair to move the industry forward,“ said Zohar Kaufman, co-founder and VP, R&D for Portshift. “Having Portshift’s information-rich view of containers in real time will be exceedingly important in 2020 as more determined hackers continue their efforts to attack earlier in the development process in order to exploit vulnerabilities before they are addressed by DevSecOps.“

National Science Foundation Awards grant to develop next-generation cloud computing testbed powered by Red Hat

Red Hat announced that the National Science Foundation (NSF) division of Computer and Network Systems has awarded a grant to a research team from Boston University, Northeastern University and the University of Massachusetts Amherst (UMass) to help fund the development of a national cloud testbed for research and development of new cloud computing platforms.

The testbed, known as the Open Cloud Testbed, will integrate capabilities previously developed for the CloudLab testbed into the Massachusetts Open Cloud (MOC), a production cloud developed collaboratively by academia, government, and industry through a partnership anchored at Boston University’s Hariri Institute for Computing. 

As a founding industry partner and long-time collaborator on the MOC project, Red Hat will work with Northeastern University and UMass, as well as other government and industry collaborators, to build the national testbed on Red Hat’s open hybrid cloud technologies.

Testbeds such as the one being constructed by the research team, are critical for enabling new cloud technologies and making the services they provide more efficient and accessible to a wider range of scientists focusing on research in computer systems and other sciences.

By combining open source technologies and a production cloud enhanced with programmable hardware through field-programmable gate arrays (FPGAs), the project aims to close a gap in computing capabilities currently available to researchers. 

As a result, the testbed is expected to help accelerate innovation by enabling greater scale and increased collaboration between research teams and open source communities. Red Hat researchers plan to contribute to active research in the testbed, including a wide range of projects on FPGA hardware tools, middleware, operating systems and security.

Beyond this, the project also aims to identify, attract, educate and retain the next generation of researchers in this field and accelerate technology transfer from academic research to practical use via collaboration with industry partners such as Red Hat.

Since its launch in 2014, Red Hat has served as a core partner of the MOC, which brings together talent and technologies from various academic, government, non-profit, and industry organizations to collaboratively create an open, production-grade public cloud suitable for research and development. The MOC’s open cloud stack is based on Red Hat Enterprise Linux, Red Hat OpenStack Platform and Red Hat OpenShift.

Beyond creating the national testbed, the grant will also extend Red Hat’s collaboration with Boston University researchers to develop self-service capabilities for the MOC’s cloud resources. For example, via contributions to the OpenStack bare metal provisioning program (Ironic), the collaboration aims to produce production quality Elastic Secure Infrastructure (ESI) software, a key piece to enabling more flexible and secure resource sharing between different datacenter clusters. 

By sharing new developments that enable moving resources between bare metal machines and Red Hat OpenStack or Kubernetes clusters in open source communities such as Ironic or Ansible, Red Hat and the MOC’s researchers are helping to advance technology well beyond the Open Cloud Testbed.

Broadcom debuts Automation.ai, its AI-driven platform that boosts digital business decision-making and execution

Broadcom has announced availability of Automation.ai, an AI-driven software intelligence platform purpose built to accelerate decision-making across multiple business and technology domains that support digital transformation initiatives. As enterprises increase digital investments, they must contend with increasing complexity and overwhelming volumes of data that slow even simple decision-making. 

Automation.ai correlates and analyzes this data and powers Digital BizOps from Broadcom, a new solution that uses these AI-driven insights to deliver intelligent recommendations—across business and development and operations to transform customer experience, increase employee productivity, improve operational efficiency and speed innovation.

“Digital transformation creates strain and pressure on businesses who need to move more quickly and confidently through the constant change rippling through their organizations,” said Ashok Reddy, senior vice president and general manager, Enterprise Software Division, Broadcom. “Silos of teams, tools and data impede decision-making. Automation.ai is central to Broadcom’s strategy to leverage AI and automation to create collective intelligence that speeds the informed decision-making critical to achieving digital business success.”

“At Sun Life, we put the client at the center of all we do,” said Barbara Mitchell, assistant vice president, IT Operations, Sun Life Financial, Inc. “As we continue on our digital transformation journey, we are always looking for new ways to harness the power of data. Our partnership with Broadcom will help us take that data and turn it into predictive insights to prevent outages that impact our client experience.”

Automation.ai harnesses the power of advanced machine learning (ML), intelligent automation and internet-scale open source frameworks to transform massive volumes of data from disparate toolsets, providing a unified approach to enterprise decision-making such as AI-driven to provide a predefined and out of box set of AI-driven analysis, correlation, recommendation and remediation services that are fully automated; open to ingest AIOps, DevOps, ValueOps, Automation COE domain data from a full-range of software, including Broadcom, third party and open source.

The offering is Always Learning so that users can continuously validates and improves decisions based on real-world outcomes; extensible so as to operate independently or within existing AI and machine learning ecosystems; and comes multi-cloud with fully containerized Kubernetes-based orchestration on public or private cloud.

AIOps from Broadcom leverages Automation.ai to correlate and analyze a broad range of IT monitoring data sources, and acts as a trusted proof point for the IT Operations analytics offered in Broadcom’s Digital BizOps solution. AIOps from Broadcom now includes new intelligent recommendations and auto-remediation capabilities that help IT teams predict and prevent problems before they impact user experience. 

Its capabilities include SRE DevOps dashboard that provides a new level of DevOps context to Site Reliability Engineers for historic and upcoming releases for production apps; ML-driven Recommendation Engine that provides prescriptions on how to resolve common problems, intelligent actions triggered by Automic Automation, and tribal knowledge recommendations based on customer data; new user journey analysis for flow and funnel visibility for faster triage of customer experience related issues; and enhanced Kubernetes monitoring and Prometheus integration, making it easier to monitor modern applications and infrastructures with smart instrumentation.

In related news, Broadcom recently completed the acquisition of privately-held Terma Software, whose solutions model workload dependencies, optimize workloads, enable intelligent SLAs and improve overall operational efficiency, in real-time. This acquisition will enrich the Automation.ai platform by providing new sources of workload intelligence and analytics to produce actionable insights from multiple vendors.

Diamanti joins AI/ML sector with its GPU platform that supports containerized workloads on Kubernetes

Diamanti announced availability of its enterprise platform with GPU support for running containerized workloads under Kubernetes, ideal for the demanding requirements of emerging artificial intelligence (AI) and machine learning (ML) applications. 

In conjunction with its recent announcement of Diamanti Spektra, customers can now provide to their end users GPU capacity in cloud clusters for scaling AI/ML workloads on premises out to public clouds to accelerate model development and training.

Diamanti recently announced the close of a $35 million Series C funding round that the company plans to use to ramp global go-to-market initiatives, along with increased investment in engineering resources to drive the roadmap for Diamanti Spektra, as well as new software, SaaS, and hardware solutions for emerging AI and ML workloads.

“AI is quickly proving to be the most disruptive set of new technologies in decades thanks to breathtaking advances in computing power, volume, velocity and variety of data,” said Tom Barton, CEO of Diamanti. “Our platform offers unmatched extensibility and flexibility for containerized workloads and now our customers can add GPU support for a heterogeneous environment under the same Kubernetes umbrella to help with even the most demanding AI/ML requirements.”

The Diamanti platform for AI/ML workloads fully supports Nvidia’s NVLink cross connect GPU card technology for higher performing workloads, as well as Kubeflow, a machine learning framework for Kubernetes that provides highly-available Jupyter notebooks and ML pipelines.

“Cloud-native methodology and software are crossing over with AI and machine learning, with Kubernetes an increasingly attractive option for data scientists to orchestrate the distributed architecture required to run multiple machine learning libraries and frameworks in production at scale,” said Matt Aslett, research vice president, 451 Research. “One key use case across several verticals involves infrastructure that can be quickly spun up or down to support massive simulations.”

Early access Diamanti customers are already benefiting from the new platform support for GPUs in industries as varied as financial services, energy and travel, among others.

For AI/ML applications requiring GPUs, the new Diamanti Spektra solution can also now manage the full lifecycle of containerized workloads across on-premises and public clouds, moving applications and data between Kubernetes clusters as necessary. 

Diamanti Spektra combines the power of Diamanti’s hardware-boosted x86 platform along with cloud-based infrastructure to provide Kubernetes-as-a-Service. Diamanti Spektra is in technology preview.

Amazon SageMaker Operators for Kubernetes capability helps developers, data scientists to train, tune, deploy ML models

AWS released on Monday Amazon SageMaker Operators for Kubernetes capability that makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create Amazon SageMaker jobs natively using the Kubernetes API and command-line Kubernetes tools such as ‘kubectl’.

Many AWS customers use Kubernetes, an open-source general-purpose container orchestration system, to deploy and manage containerized applications, often via a managed service such as Amazon Elastic Kubernetes Service (EKS). This enables data scientists and developers, for example, to set up repeatable ML pipelines and maintain greater control over their training and inference workloads. 

Amazon SageMaker brings down deep learning inference costs by up to 75 percent using Amazon Elastic Inference to attach elastic GPU acceleration to Amazon SageMaker instances. For most models, a full GPU instance is over-sized for inference. Also, it can be difficult to optimize the GPU, CPU, and memory needs of your deep learning application with a single instance type. 

Elastic Inference allows users to choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the right amount of GPU acceleration required for inference.

However, to support ML workloads these customers still need to write custom code to optimize the underlying ML infrastructure, ensure high availability and reliability, provide data science productivity tools, and comply with appropriate security and regulatory requirements. 

For example, when Kubernetes customers use GPUs for training and inference, they often need to change how Kubernetes schedules and scales GPU workloads in order to increase utilization, throughput, and availability. Similarly, for deploying trained models to production for inference, Kubernetes customers have to spend additional time in setting up and optimizing their auto-scaling clusters across multiple Availability Zones.

Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting Monday, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale. 

With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near full utilization. 

Now with Amazon SageMaker Operators for Kubernetes, customers can continue to enjoy the portability and standardization benefits of Kubernetes and EKS, along with integrating the many additional benefits that come out-of-the-box with Amazon SageMaker, no custom code required.

Each Amazon SageMaker Operator for Kubernetes provides users with a native Kubernetes experience for creating and interacting with jobs, either with the Kubernetes API or with Kubernetes command-line utilities such as kubectl. Engineering teams can build automation, tooling, and custom interfaces for data scientists in Kubernetes by using these operators—all without building, maintaining, or optimizing ML infrastructure. 

Data scientists and developers familiar with Kubernetes can compose and interact with Amazon SageMaker training, tuning, and inference jobs natively, as users would with Kubernetes jobs executing locally. Logs from Amazon SageMaker jobs stream back to Kubernetes, allowing consumers to natively view logs for model training, tuning, and prediction jobs in command line.

Hewlett Packard Enterprise introduces Kubernetes-based platform for bare-metal and edge to cloud deployments

Hewlett Packard Enterprise (HPE) announced the HPE Container Platform, its enterprise-grade Kubernetes-based container platform designed for both cloud-native applications and monolithic applications with persistent storage. 

With the HPE Container Platform, enterprise customers can accelerate application development for new and existing apps – running on bare-metal or virtualized infrastructure, on any public cloud, and at the edge.

The new platform addresses requirements for large-scale enterprise Kubernetes deployments across a range of use cases, from machine learning and edge analytics to CI/CD pipelines and application modernization. 

IT teams can manage multiple Kubernetes clusters with multi-tenant container isolation and pre-integrated persistent storage. Developers have secure on-demand access to their environments so they can develop apps and release code faster, with the portability of containers to build once and deploy anywhere.

The HPE Container Platform is built on proven innovations from HPE’s acquisitions of BlueData and MapR, together with 100 percent open source Kubernetes. This next-generation solution dramatically reduces cost and complexity by running containers on bare-metal, while providing the flexibility to deploy on virtual machines and cloud instances. Customers benefit from greater efficiency, higher utilization, and improved performance by “collapsing the stack” and eliminating the need for virtualization.

The HPE Container Platform is a turnkey solution that uniquely addresses these challenges, with BlueData software as the control plane for container management, the MapR distributed file system for persistent data with containers, and Kubernetes for container orchestration. 

This approach extends benefits of containers beyond cloud-native microservices-architected applications, providing the ability to containerize non cloud-native monolithic applications with persistent data storage.

The HPE Container Platform modernizes non cloud-native monolithic applications without re-architecting them, elevating the experience to modern cloud standards; provides the ability to build applications once and run them anywhere, bridging the gap between on-premises, public clouds and the edge; improves productivity for developers and delivers new code releases faster, with simplified Kubernetes deployment and multi-cluster management; and ensures enterprise-class security, performance, and reliability at lower cost, with bare-metal containers and data persistence.

This offering complements existing HPE services to assist customers with container strategies, application modernization, and hybrid cloud deployments. HPE Pointnext provides advisory and consulting services built upon experience from over one thousand hybrid cloud engagements, with expertise and best practices from the acquisitions of Cloud Technology Partners and RedPixie.

HPE Container Platform software can be ordered early next year, along with advisory, consulting, deployment and support services from HPE.