Azure Local for Edge Deployments
Whilst Cloud computing has provided scalability, burst capacity, and simplified management to millions of workloads, in many scenarios this comes at the expense of network latency and dependence on connectivity that can impact some operations.Edge computing brings compute resources close to end users and sensors, and as such, can increase system responsiveness. This can be important for applications needing consistent millisecond response times, such as factory and warehouse automation, safety related systems, customer interaction systems, and evolving areas, such as augmented reality, where network bandwidth also becomes a material consideration.For such use cases, Azure Local is the natural choice for organisations already invested in Microsoft Azure as it extends the central control plane of Azure Arc, Azure PaaS services, and embedded lifecycle automation to high-performance, low-latency and always-on capabilities for on-premises deployments.
Achieving continuous availability for on-premises systems has traditionally introduced significant complexity, management overhead, and cost, but with Azure Local stretched-clusters and platform services, such as Managed SQL Instances, always-on systems within site boundaries can be easily established, without the need for specialist skills and complex monitoring.
As well the advantages in terms of performance and reliability, Trusted Launch, disk encryption via BitLocker (available at both hypervisor and guest levels), and full memory encryption capabilities ensure data can be secured against the physical threat vectors ever present in deployments outside protected data centre environments.
Azure Local enables the deployment of modern, containerised applications via Azure IoT Edge. This open-source solution by Microsoft is built to support edge workloads where near real-time responsiveness is required, and is managed via the Azure IoT Hub in the Azure Portal.IoT Edge supports offline and intermittent connectivity environments and combines AI, Cloud and edge computing to containerise Cloud workloads - such as Azure Cognitive Services, Machine Learning, Stream Analytics, and Functions - and is available in the AI Toolkit for Azure IoT Edge, which offers programmability support for Java, .NET Core 2.0, Node.js, C, and Python.Supporting DevOps via the Azure CI/CD pipeline application deployment framework, the solution enables organisations to deploy containerised applications iteratively whilst still allowing traditional VM management processes and tools to be retained.
Scalability and burst capacity
Centralised management and control
Data sovereignty and compliance
Cost optimisation
Improved application performance
Other
