Google Cloud Platform offers services and tools for network configuration, infrastructure management, and server provisioning. GCP also offers a number of modular cloud services, including machine learning, application development, data storage, data analytics, and computing. Cloud administrators, developers, and IT professionals can access GCP via dedicated networks.
Popular Google Cloud Computing Services in 2023
Let us now look at the new Google Cloud Computing Tools and Services to help customers better incorporate Google Cloud products ranging from a new Kubernetes cost estimator to a high-performance computing (HPC) toolkit.
Google Kubernetes Engine
Using the new cost estimator, customers can now see how much it will cost to run a specific Google Kubernetes Engine (GKE) cluster. The new cost estimator tool demonstrates how different configurations and feature selections affect costs, as well as what the impact of autoscaling may be on a client’s bill. The Google Cloud console now includes a seamless integration of the GKE cost estimator. The GKE estimator is part of the GKE cluster creation flow, and it displays a number of variables that can affect a customer’s compute running costs by allowing people to see the breakdown of costs between management fees, individual node pools, licenses, and other factors. According to Google Cloud, the new tool is part of the company’s commitment to making Google Cloud the most cost-effective cloud by providing leading price performance and customer-friendly licensing, as well as predictable and transparent pricing.
Google Workspace SAP Integrations
Google Cloud has developed new integrations between its Workspace solution and SAP’s cloud ERP, SAP S/4HANA Cloud, allowing customers to connect core SAP software with the collaborative capabilities of Google Docs and Google Sheets.
SAP and Google Cloud’s ultimate goal here is to innovate how work is complete across the enterprise. Customers can export and import data between SAP software and Google Docs and Google Sheets for instant access to real-time editing and concurrent collaborative engagements on documents and spreadsheets. Customers will also benefit from a clean data source as a result of the one-step integration, which allows for version control and eliminates layers of potential translation when sharing application data and documents, according to Google.
Gmail, Chat, Calendar, Docs, Sheets, Meet, and other popular apps now come in a single integrated workspace in Google Workspace. Customers benefit from new business capabilities, use cases, and opportunities for collaborative tasks within small and large organizations alike when Google Docs and Google Sheets integrates with the SAP S/4HANA Cloud.
Google Distributed Cloud Virtual
Google Cloud will release its Google Distributed Cloud (GDC) portfolio of hardware, software, and services in 2021, bringing its cloud infrastructure to the edge and into customer data centers.
furthermore, Google Distributed Cloud Virtual is a new software and service solution that incorporates Google’s existing Anthos on-premise; for VMware vSphere and Anthos bare metal services into the GDC portfolio. GDC Virtual is a software-only extension of Google Cloud that is cloud-managed and deployed on a client’s infrastructure; allowing customers to automate provisioning and management of GKE clusters on VMs and existing bare metal infrastructure with the correct requirements and form factors; as well as use the Google Cloud Console to provision Anthos clusters on vSphere.
The new GDC Virtual allows developers to create and deploy container-based workloads directly to Kubernetes or an application runtime. Customers using GDC Virtual can also use federated security, access control, and identity management across cloud and on-premises clusters. Customers of Anthos on-premises will continue to receive the same consistent management and developer experience, with no changes to current capabilities or pricing structures.
Cloud HPC Toolkit
The new Cloud HPC Toolkit is the next step in Google Cloud high-performance computing (HPC). The new open-source tool makes it simple for users to build repeatable, turnkey HPC clusters based on proven best practices.
The HPC Toolkit has a modular design that allows for the creation of composable HPC environments. This enables it to define and deploy both simple and advanced HPC solutions with ease. An HPC blueprint is a high-level YAML-formatted file that combines Terraform modules, Packer templates, and Ansible playbooks to define the infrastructure and software configuration of an HPC environment. Customers can use an existing blueprint to create a cluster or modify it to meet their specific requirements. Organizations can easily modify the configuration to provide the necessary infrastructure and industry-specific tools by modifying a few text lines in the blueprint.
The HPC Toolkit includes blueprints for several example configurations, including a small basic cluster and a high I/O cluster. These can be used as-is to familiarize yourself with the HPC Toolkit’s operations, or they can be modified to build different configurations.
Cloud Fleet Routing API
Google Cloud recently released its Cloud Fleet Routing API, which focuses on the delivery route planning phase and allows operators to perform advanced fleet-wide optimization. This enables them to control the allocation of packages to delivery vans as well as the sequencing of delivery tasks. The new Cloud Fleet Routing API, which is natively integrated with Google Maps route data, can solve simple route planning requests in near-real time and scale to the most demanding workloads via parallelized request batching. Customers can specify time windows, package weights, and vehicle capacities, among other things. The Cloud Fleet Routing API also assists carriers in meeting sustainability goals by reducing the distance traveled, delivery van count, and CO2 emissions from cloud computing.
Confidential GKE Nodes
This year, Google made Confidential GKE Nodes generally available, which use hardware to ensure data is encrypt in memory. Google launched the Confidential GKE Nodes to help increase the security of GKE clusters. These nodes enable in-use encryption for data processed within a GKE cluster without causing significant performance degradation. Confidential GKE Nodes use AMD Secure Encrypted Virtualization and are on the same technology foundation as Confidential VM. Customers can use this feature to keep data encrypted in memory using node-specific, dedicated keys generated and managed by the processor.
Confidential GKE Nodes also use Shielded GKE Nodes to provide additional protection against rootkits and boot kits; thereby ensuring the integrity of the Confidential GKE Nodes’ operating system. The Confidential GKE Nodes security setting can be enabled at the cluster or node pool level. When enabled at the cluster level, the solution forces all worker nodes to use Confidential VMs.
Network Analyzer
With the release of its Network Analyzer, Google Cloud said its networking group created a solution to avoid the manual, time-consuming, reactive status quo.
Customers can use the new module to turn reactive workflows into proactive processes, reducing network and service downtime. The Network Analyzer detects failures caused by misconfigurations such as setup errors as well as regressions caused by unintended changes. Google Cloud Network Analyzer empowers businesses by automatically detecting network failures and recommending best practices to improve service availability, performance, and security. It also identifies the root cause of the failure, such as an invalid route or a firewall rule that prevents the service from being reached.
The Network Analyzer provides an out-of-the-box suite of always-on analyzers that continuously monitor the network configuration of Google Compute Engine (GCE) and Google Kubernetes Engine (GKE). These analyzers monitor network services such as load balancers, hybrid connectivity, and connectivity to Google services such as Cloud SQL in the background.
Vertex AI Tabular Workflows
The new Vertex AI Tabular Workflows tool includes a glass-box and managed AutoML pipeline, allowing users to see and interpret each step of the AI model creation and deployment process. Vertex AI by Google Cloud is a collection of cloud services for building AI models. Customers can train datasets of over a terabyte without sacrificing accuracy with the new Vertex AI Tabular Workflows by selecting which parts of the process they want AutoML to handle versus which parts they want to engineer themselves. The new tool can be used to create neural networks that process tabular data, which is data that is organized into rows and columns. According to Google, a significant portion of business information is stored in rows and columns.
Certain Tabular Workflows elements can also be integrated into a customer’s existing Vertex AI pipelines. Google has introduced new managed algorithms, such as advanced research models such as TabNet, as well as new algorithms for feature selection and model distillation.
Manufacturing Data Engine
The new Manufacturing Data Engine is a full-stack solution for processing, contextualizing, and storing factory data on Google Cloud’s market-leading data platform. The Manufacturing Data Engine offers a configurable and customizable blueprint for factory data ingestion, transformation, storage, and access.
It combines Google Cloud products such as Cloud Dataflow, PubSub, BigQuery, Cloud Storage, Looker, Vertex AI, Apigee, and others into a manufacturing-specific solution. Manufacturers can use the solution to connect previously siloed assets, process and standardize data, and improve visibility from the factory floor to the cloud. Once the data is unified, the solution enables three AI and analytics-based use cases: manufacturing analytics and insights, predictive maintenance, and machine-level anomaly detection.
The goal is to assist manufacturers in unifying their data and empowering their workforce by providing simple analytics and AI solutions built on cloud infrastructure.
Manufacturing Connect
Google Cloud’s new Manufacturing Connect platform, developed in collaboration with Litmus Automation; connects to and streams data from nearly any manufacturing asset and industrial system to Google Cloud. This new solution is on a large library of over 250 machine protocols.
Deep integration with the Manufacturing Data Engine enables fast data intake into Google Cloud for machine and sensor data processing. According to Google Cloud, the ability to deploy containerized applications and ML models to the edge opens up new dimensions of use cases.
Once data is centralize and harmonize, it is in use to address a growing set of industry-specific use cases, such as machine-level anomaly detection; which helps manufacturers identify anomalies as they occur and provides alerts on the real-time machine and sensor data such as noise; vibration, or temperature by leveraging Google Cloud’s Time Series Insights API.
Engineers and plant managers can automatically set up new machines and factories when Manufacturing Connect is integrate with the Manufacturing Data Engine; enabling standardized dashboards, KPIs, and on-demand data insights to uncover new opportunities throughout the factory.
