#localized cloud

The Southeast Asia Hyperscale Data Center Market is fundamentally being reshaped by the rising importance of data localization and national digital sovereignty. As countries like Malaysia and Indonesia implement policies to keep sensitive data within their borders, the demand for localized hyperscale infrastructure has surged, creating a new wave of localized investment. Southeast Asia Hyperscale Data Center Market recorded a value of USD 12,900 million in 2024 and is estimated to reach a value of USD 89,683 million by 2033 with a CAGR of 26.3% during the forecast period. This trend is empowering local operators to form joint ventures with global giants, transferring critical technological expertise to the domestic market.

Analyzing the sea hyperscale data center capex drivers reveals that governments are now active participants in the infrastructure race, often providing land grants and tax incentives to attract major cloud regions. This collaborative approach between the public and private sectors is shortening permitting timelines and unlocking access to critical utility upgrades. However, the complexity of these projects remains high, as operators must balance the geopolitical tug-of-war between competing international tech stacks. The ability to provide a secure, neutral, and high-performance environment is becoming the ultimate competitive advantage for data center providers operating in this complex regional theater.

*The Amazon Stack is aggressive; they’re not backing down from anybody.

http://www.allthingsdistributed.com/2017/06/unlocking-value-device-data-aws-greengrass.html

Unlocking the Value of Device Data with AWS Greengrass.

By Werner Vogels on 07 June 2017 09:00 AM |

Unlocking the value of data is a primary goal that AWS helps our customers to pursue. In recent years, an explosion of intelligent devices have created oceans of new data across many industries. We have seen that such devices can benefit greatly from the elastic resources of the cloud. This is because data gets more valuable when it can be processed together with other data.

At the same time, it can be valuable to process some data right at the source where it is generated. Some applications – medical equipment, industrial machinery, and building automation are just a few – can't rely exclusively on the cloud for control, and require some form of local storage and execution. Such applications are often mission-critical: safeties must operate reliably, even if connectivity drops. Some applications may also rely on timely decisions: when maneuvering heavy machinery, an absolute minimum of latency is critical. Some use cases have privacy or regulatory constraints: medical data might need to be stored on site at a hospital for years even if also stored in the cloud. When you can't address scenarios such as these, the value of data you don't process is lost.

As it turns out, there are three broad reasons that local data processing is important, in addition to cloud-based processing. At AWS we refer to these broad reasons as "laws" because we expect them to hold even as technology improves:

Law of Physics. Customers want to build applications that make the most interactive and critical decisions locally, such as safety-critical control. This is determined by basic laws of physics: it takes time to send data to the cloud, and networks don't have 100% availability. Customers in physically remote environments, such as mining and agriculture, are more affected by these issues.

Law of Economics. In many industries, data production has grown more quickly than bandwidth, and much of this data is low value. Local aggregation and filtering of data allows customers to send only high-value data to the cloud for storage and analysis.

Law of the Land. In some industries, customers have regulatory or compliance requirements to isolate or duplicate data in particular locations. Some governments impose data sovereignty restrictions on where data may be stored and processed.

Today, we are announcing the general availability of AWS Greengrass, a new service that helps unlock the value of data from devices that are subject to the three laws described above.

AWS Greengrass extends AWS onto your devices, so they can act locally on the data they generate while still taking advantage of the cloud. AWS Greengrass takes advantage of your devices' onboard capabilities, and extends them to the cloud for management, updates, and elastic compute and storage.

AWS Greengrass provides the following features:

Local execution of AWS Lambda functions written in Python 2.7 and deployed down from the cloud.

Local device shadows to maintain state for the stateless functions, including sync and conflict resolution.

Local messaging between functions and peripherals on the device that hosts AWS Greengrass core, and also between the core and other local devices that use the AWS IoT Device SDK.

Security of communication between the AWS Greengrass group and the cloud. AWS Greengrass uses the same certificate-based mutual authentication that AWS IoT uses. Local communication within an AWS Greengrass group is also secured by using a unique private CA for every group.

Before AWS Greengrass, device builders often had to choose between the low latency of local execution, and the flexibility, scale, and ease of the cloud. AWS Greengrass removes that trade-off—manufacturers and OEMs can now build solutions that use the cloud for management, analytics, and durable storage, while keeping critical functionality on-device or nearby.

AWS Greengrass makes it easier for customers to build systems of devices (including heterogeneous devices) that work together with the AWS Cloud. Our goal is not to provide an alternative for the cloud, but to provide tools for customers to use the cloud to build applications and systems that can't be moved entirely to the cloud. Using AWS Greengrass for local execution, customers can identify the most valuable data to process, analyze, and store in the cloud.

With AWS Greengrass, we can begin to extend AWS into customer systems—from small devices to racks of servers—in a way that makes it easy to do the things locally that are best done locally, and to amplify those workloads with the cloud.

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