Amazon VPC Ingress Routing and Trend Micro help simplify network security

Amazon Web Services (AWS) announced availability of Amazon Virtual Private Cloud (Amazon VPC) Ingress Routing service. As a Launch Partner for Amazon VPC Ingress Routing, Trend Micro continues to innovate alongside AWS to provide solutions to customers—enabling new approaches to network security. 

Trend Micro TippingPoint and Trend Micro Cloud One integrate with Amazon VPC Ingress Routing deliver network security that allows customers to obtain compliance by inspecting both ingress and egress traffic, thereby providing user with a deployment experience designed to eliminate any disruption in the business.

Amazon VPC Ingress Routing is a service that helps customers simplify the integration of network and security appliances within their network topology. With Amazon VPC Ingress Routing, customers can define routing rules at the Internet Gateway (IGW) and Virtual Private Gateway (VGW) to redirect ingress traffic to third-party appliances, before it reaches the final destination. This makes it easier for customers to deploy production-grade applications with the networking and security services they require within their Amazon VPC.

By enabling customers to redirect their north-south traffic flowing in and out of a VPC through internet gateway and virtual private gateway to the Trend Micro cloud network security solution. Not only does this enable customers to screen all external traffic before it reaches the subnet, but it also allows for the interception of traffic flowing into different subnets, using different instances of the Trend Micro solution.

Trend Micro customers now have the ability to have cloud network layer security in AWS leveraging Amazon VPC Ingress Routing. With this enhancement, customers can deploy in any VPC, without any disruptive re-architecture and without introducing any additional routing or proxies. Deploying directly inline is the ideal solution and enables simplified network security without disruption in the cloud.

A defense-in-depth or layered security approach is important to organizations, especially at the cloud network layer. That being said, customers need to be able to deploy a solution without re-architecting or slowing down their business, the problem is, previous solutions in the marketplace couldn’t meet both requirements.

So, when customers wanted TippingPoint intrusion prevention system (IPS) capabilities to be brought to the cloud, Trend Micro responded with a solution. Backed by research from Trend Micro Research, including the Zero Day Initiative, Trend Micro created a solution that includes cloud network IPS capabilities, incorporating detection, protection and threat disruption—without any disruption to the network.

At AWS re:Invent 2018, AWS announced the launch of Amazon Transit Gateway. This architecture enables customers to route traffic through a hub and spoke topology, and leverage this as a primary deployment model in the Cloud Network Protection, powered by TippingPoint, cloud IPS solution, announced in July this year. This enabled customers to gain broad security and compliance, without re-architecting, and the company will soon add a flexible deployment model.

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.

Amazon Web Services updates its AWS DeepRacer program with new sensing capabilities and training algorithms

Amazon Web Services (AWS) announced on Wednesday that it is upgrading its DeepRacer program by adding more chances to compete at AWS events & at own events, more chances to win, with new races including head-to-head multi-car competitions, and an upgraded DeepRacer car with new sensing capabilities.

AWS DeepRacer is an autonomous 1/18th scale race car designed to test RL models by racing on a physical track. Using cameras to view the track and a reinforcement model to control throttle and steering, the car shows how a model trained in a simulated environment can be transferred to the real-world.

AWS DeepRacer Evo is the next generation in autonomous racing. Take your car to the tracks and master brand new AWS DeepRacer challenges including racing head to head against other cars in the 2020 season of the AWS DeepRacer League. AWS DeepRacer Evo comes with dual stereo cameras, allowing the car to detect objects on the track, and LIDAR (light detection and ranging) helping to determine when to overtake another car and beat it to the finish line.

AWS DeepRacer offers hands-on experience with Reinforcement Learning (RL). Following launch of the AWS DeepRacer car and the AWS DeepRacer League, users could have the opportunity to get experience and new skills in a fun, competitive environment. In less than a year, tens of thousands of developers have participated in hands-on and virtual races located globally. 

The upcoming AWS DeepRacer Evo car will include a stereo camera and a Light Detection and Ranging (LIDAR) sensor. The added sensors will enable DeepRacer Evo to skillfully detect and respond to obstacles, including other DeepRacers. This will help users learn more about the field of reinforcement learning, which is ideal for use in autonomous driving.

The new sensors will be available soon in virtual form for use in the new My Garage section of the DeepRacer Console. AWS will release more details on production plans for AWS DeepRacer Evo, including a sensor upgrade kit for existing DeepRacer car, early next year.

AWS is also expanding the DeepRacer League in 2020. The company is adding eight additional races across five countries as part of an expanded AWS Summit presence, and 18 additional virtual races. There will also be a track (and a race) at re:MARS 2020. As a result, customers have the opportunity to participate in 30 events and join for an in-person AWS DeepRacer League Summit race, along with 24 Virtual Circuit races from anywhere in the world.

In addition to the existing time trial race, AWS is adding two new race types to give some new RL challenges, and the opportunity to experiment with different sensors. The object detection and avoidance features uses the sensors to detect and (hopefully) avoid obstacles, and the head-to-head racing against another DeepRacer that is on the same track. 

AWS uses Step Functions to orchestrate Amazon EMR workloads

Amazon Web Services announced its AWS Step Functions that allows users to add serverless workflow automation to their applications. The steps of the workflow can run anywhere, including in AWS Lambda functions, on Amazon Elastic Compute Cloud (EC2), or on-premises. 

To simplify building workflows, Step Functions is directly integrated with multiple AWS Services: Amazon ECS, AWS Fargate, Amazon DynamoDB, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), AWS Batch, AWS Glue, Amazon SageMaker, and (to run nested workflows) with Step Functions itself.

Workflows are made up of a series of steps, with the output of one step acting as input into the next. Application development is simpler and more intuitive using Step Functions, because it translates workflow into a state machine diagram that is easy to understand, easy to explain to others, and easy to change. 

Users can monitor each step of execution as it happens, which means they can identify and fix problems quickly. Step Functions automatically triggers and tracks each step, and retries when there are errors, so that the application executes in order and as expected.

Step Functions connects to Amazon EMR to create data processing and analysis workflows with minimal code, saving time, and optimizing cluster utilization. For example, building data processing pipelines for machine learning is time consuming and hard. With this new integration, users have a simple way to orchestrate workflow capabilities, including parallel executions and dependencies from the result of a previous step, and handle failures and exceptions when running data processing jobs.

Specifically, a Step Functions state machine can create or terminate an EMR cluster, including the possibility to change the cluster termination protection. In this way, consumers can reuse an existing EMR cluster for their workflow, or create one on-demand during execution of a workflow. It also can add or cancel an EMR step for their cluster. 

Each EMR step is a unit of work that contains instructions to manipulate data for processing by software installed on the cluster, including tools such as Apache Spark, Hive, or Presto.

The offering can also modify the size of an EMR cluster instance fleet or group, allowing users to manage scaling programmatically depending on the requirements of each step of the workflow. For example, the user may increase the size of an instance group before adding a compute-intensive step, and reduce the size after it has completed.

When creating or terminating a cluster or add an EMR step to a cluster, users can use synchronous integrations to move to the next step of the workflow only when the corresponding activity has completed on the EMR cluster.

Rackspace gives boost to its managed cloud services segment with Onica acquisition

Rackspace announced that it has agreed to acquire Onica, an Amazon Web Services (AWS) Partner Network (APN) Premier Consulting Partner and AWS Managed Service Provider. This acquisition brings Onica’s professional services capabilities – including strategic advisory, architecture and engineering and application development – to the Rackspace portfolio, complementing its existing managed cloud services capabilities. Terms of the transaction were not disclosed.

Founded in 2014 and headquartered in Santa Monica, California, Onica has rapidly grown to more than 350 highly-skilled consultants across North America. 

The company holds nine AWS competencies across Data and Analytics, DevOps, Education, Healthcare, Industrial Software, IoT, Microsoft Workloads, Migration and Storage. Onica has been a regular on Inc. Magazine’s Best Workplaces list and ranked fifth on the CRN® Fast Growth 150 list. 

As a cloud-native services company, Onica helps customers build new revenue streams, increase efficiency and deliver incredible experiences by bringing the innovative capabilities of the cloud to some of the most complex technology projects in the world.

“As a cloud pioneer, Onica has established itself as one of the largest pure-play AWS consultancies, with an unmatched reputation for true capability leadership with AWS and customers,” said Kevin Jones, CEO of Rackspace. “This acquisition will strengthen our ability to meet all of our customer needs on AWS, and together, we will have the most complete set of professional services and managed service capabilities in the industry. Rackspace is known for providing Fanatical Experience™ to its customers and Onica’s customer-first mindset is a natural culture fit. We are thrilled to welcome Stephen, Tolga and the talented Onica team to the Racker family.”

The demand for professional services for public cloud is rapidly growing and customers are continuing to adopt advanced cloud solutions. The acquisition of Onica will enhance and extend Rackspace’s offering to customers who are quickly maturing on AWS and seeking high-end professional services spanning the full spectrum of expertise in the cloud.

“By combining our capabilities with Rackspace’s global presence, resources and scale, we will be better positioned to achieve our mission of helping customers innovate using AWS,” said Stephen Garden, CEO of Onica. “We pride ourselves on delivering results for customers, and in Rackspace we have found a partner that shares our passion for providing a customer-obsessed experience.”

The transaction is expected to close in late fourth quarter of 2019. Garden and Onica chief technology officer Tolga Tarhan will continue to lead the Onica team, reporting to Sid Nair, general manager of Americas at Rackspace.

Both companies are privately held, with Rackspace owned by affiliates of certain funds of Apollo Global Management LLC and certain co-investors. Onica is a portfolio company of Sunstone Partners. Evercore acted as sole financial advisor to Rackspace on the transaction. Barclays acted as sole financial advisor to Onica.

PayGo secures high availability of SQL server in the AWS cloud with SIOS DataKeeper

SIOS Technology announced Wednesday that PayGo is using SIOS DataKeeper on Amazon Web Services (AWS) utilizing Elastic Compute Cloud (EC2) virtual servers with solid-state drive (SSD)-only storage for rapid, automatic failover needed to ensure high availability (HA) for the company’s mission-critical SQL Server applications.

PayGo is an integrated utility payment solution provider that manages the largest energy company prepay programs in the United States. 

PayGo is currently running four production environments in AWS, with another coming online soon, with SQL Server 2017 Standard Edition running on Windows Server 2012 R2, and plans to migrate to Windows Server 2019 after testing is completed.