The Amazon Re:Invent annual conference, which concluded last Friday, was indeed a remarkable event in what regards to cloud computing developments. Several new products and services presented during the conference will set the stage for revolutionary upcoming developments in computing technology.
In previous articles, we have made some comments about various important new services launched during the past week. We also discussed speeches from Andy Jassy, Werner Vogel and other company leaders, as well as about several important new developments.
In this opportunity, we will discuss Jeff Barr´s presentation -the Chief Evangelist of AWS-, who had the honor of introducing to the public several services that, as he explained, will have a great positive impact on the market, as well as in customer’s performance.
“Several new products and services presented during the conference will set the stage for revolutionary upcoming developments in computing technology.”
AWS Global accelerator
The first important announcement made by Barr was the launching of AWS Global Accelerator, a software that allows companies and developers to improve availability and performance of their applications, no matter if final users of those applications are based locally or internationally. The software provides static IP addresses, which function as a fixed endpoint for an application within one or several AWS regions.
AWS Global Accelerator uses the global AWS network in order to optimize the access of its customers to applications, enhancing the efficiency of TCP and UDP traffic. Also, it monitors permanently the current state of endpoints used by customer´s applications. When an endpoint isn´t working properly, this tool will detect it and quickly redirect traffic to an endpoint that is currently functioning properly.
Another important announcement was the release of AWS Transit Gateway, a tool developed to simplifying customer´s network architecture.
Customers will be able to connect their VPCs, remote offices, data centers and remote access gates to a managed Transit Gateway, maintaining total control over the network´s security and routing. This will make possible for organizations to reduce operational overload and centrally managing essential aspects of external connectivity, including security.
AWS IoT SiteWise
In what regards to the internet of things, AWS IoT SiteWise was one of the most interesting applications for customers and partners presented during Re:Invent. Iot SiteWise helps industrial companies to structure, compile and search thousands of IoT sensors´ data streams in different facilities. A gateway device compiles data from OPC-UA servers and afterward send it to AWS.
AWS IoT Things Graph was another big announcement made by Jeff Barr. The purpose of this product is to make it easier for developers the process of creating new IoT applications for edge gateways that run AWS IoT Greengrass.
On the other hand, in the field of quantum computing, Amazon Braket is a platform designed to support developments in quantum computing, providing a suitable environment to design quantum coding, test them and run them in quantum hardware. This platform permits users to work with traditional and quantum code on hybrid infrastructure. Its relevance is such that it could become one of the main computing services provided by AWS in the future.
In order to unleash resources in Amazon Elastic Service Cloud (EC2), and therefore enhancing performance, a service called Project Nitro was launched, which offload storage, network, and hypervisor to custom chips. Based on those types of custom chips, Amazon is already releasing supplementary EC2 instances.
In the field of AI, Amazon is working to keep human intervention in the loop, for the sake of increasing accuracy and, at the same time, training machine learning. To that effect, AWS launched SageMaker Ground Truth, a data labeling application that is nurtured by users.
In that same vein, Amazon Augmented AI, also known as Amazon A2I, is supported in human verification of machine learning models. Real humans receive predictions produced by machine learning models in order to verify them. This feature helps to increase the efficiency and sharpness of models.