Tag: IT cloud computing

Cloud computing trends in 2024 include the need for complex ecosystems, real-time capabilities, security, and sustainability. Rising popularity of hybrid and multi-cloud systems offer reduced costs and risks. The focus would also be on cloud-native development, integrated cloud environments, and data portability. Increased adoption of cloud-driven technologies such as blockchain and IoT, zero-trust security, privacy-enhancing technologies, data encryption, AI/ML integration and real-time data analytics platforms are also anticipated.

The article outlines the process and benefits of running the CP2K application, a quantum chemistry and solid-state physics software package, on a virtual machine (VM) deployed on Azure. It details performance tests for single-node and multi-node configurations and recommends that fewer CPUs be utilized for optimal performance. The article also provides model-specific cost calculations and suggests that Azure’s scalability offers significant speedup for all models.

The post discusses the growing importance of conversational interfaces and the usage of analytics and dashboards to monitor performance. It introduces Amazon Managed Grafana (AMG), a service that provides rich data visualizations for analyzing conversation logs from Amazon Lex. The post provides detailed steps on setting up AMG to visualize and report insights for missed utterances, user requests, and sentiment metrics, thereby aiding the continuous improvement of conversational interfaces.

Amazon has launched CloudWatch Container Insights, an observability service providing visibility into containerized applications and microservices environments. The enhanced version offers observability for Amazon Elastic Kubernetes Service (EKS), sending telemetry from Kubernetes control plane components and facilitating faster problem isolation. Additionally, users can set alarms for anomalies, prioritize resource allocation, and preemptively mitigate potential risks. This tool can be easily enabled on a user’s Amazon EKS cluster, offering granular monitoring at the cluster, service, pod, and container levels.

Azure Stack HCI and AKS Hybrid offer a solution for hosting virtualized workloads and their storage in a hybrid environment. This article is part two of a series and describes the process of creating an AKS Hybrid Proof of Concept (PoC) to meet a customer’s requirements for a resilient, flexible architecture for their call centre triage service. The PoC was successful in demonstrating the functionality and benefits of the proposed solution.

AWS Managed Services (AMS) utilizes automation for 95% of its operations, demonstrating the transformative power of automation for operational efficiency within an organization. Introducing automation demands a cultural shift towards an “automation first” stance, backed by technology, cross-collaboration, and new roles. Key principles involve automating repetitive tasks, focusing on high-impact tasks, leveraging existing code, and continuous monitoring. AMS used this approach to automate Identity and Access Management tasks, significantly improving efficiency and saving large amounts of operational time.

The article discusses enhancing existing message broker architecture with near real-time analytics using Azure Data Explorer and Azure Service Bus. The data is sourced from an OLTP application and flows in two directions; to operational storage databases and Azure Data Explorer for analytics. The application of near real-time analytics spans across healthcare, manufacturing, finance and commerce sectors.

This post provides detailed steps on migrating an on-premises GitHub Enterprise Server (GHES) to AWS. This allows organisations to scale freely, benefit from improved team collaboration and faster deployment cycles, and avoid constraints of on-premise infrastructure. The process includes key design considerations, creating a backup of data, setting up a new primary GHES instance on AWS, and managing post-migration cleanup. Operating GHES on AWS also offers cost-efficiency through AWS’s elasticity.

Daniel Wirjo and Justin Plock discuss webhooks, powerful integrative tools used in software, transactions, and for enhancing user experience. They provide guide to set up two scenarios on AWS, the Webhooks Provider – a SaaS application delivering webhooks to an external API, and Webhooks Consumer – an API receiving large payload capacity webhooks. They provide reference architectures, best practices, considerations, and sample code for implementation, sending and receiving webhooks.

Amazon announces EC2 Capacity Blocks for Machine Learning (ML), addressing scarcity of GPUs due to the high demand for ML applications. Users can reserve GPU instances for high-performance ML workloads using the pay-as-you-go model. Initially available for EC2 P5 instances in the AWS US East (Ohio) region, this approach also allows planned ML development with predictable access to high-performance instances.

This blog post by Amazon S3 details how the Aggregator pattern can handle coupling issues in event-driven architectures (EDA). Using the AWS Step Functions, the object storage service can process data files uploaded by different teams in a particular order. The process involves asynchronous communication between system components that enable these components to function autonomously. This method allows for accurate data correlation and workflow orchestration.

Streaming data processing ability can significantly differentiate successful organizations from competitors. AWS provides reliable data pipelines to process real-time streaming data, enabling agile, data-informed decisions and insights into customer behavior. Its modern data architecture breaks down data silos, while ensuring security. The blog highlights how Samsung optimized their own streaming data analytics by migrating to Amazon’s service for Apache Flink, shifting focus from infrastructure to delivering business value.

The post discusses steps to improve resource efficiency and resiliency in hyperscale environments using containerized applications and cell-based design. The team uses Amazon EKS for container orchestration, AWS App2Container for Java and .NET applications, CloudWatch and Prometheus for logging, and Kubecost for resource utilization evaluation. Deployment strategies were revised and a cell-based architecture using shuffle sharding was introduced to manage black swan events, improve resiliency, and scale.