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IT carbon footprint: Dynatrace Carbon Impact and Optimization app helps organizations measure cloud computing carbon footprint

Dynatrace

Energy efficiency is a key reason why organizations are migrating workloads from energy-intensive on-premises environments to more efficient cloud platforms. But while moving workloads to the cloud brings overall carbon emissions down, the cloud computing carbon footprint itself is growing. Certainly, this is true for us.

Energy 233
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From observability to sustainability: Reduce your IT carbon footprint with Dynatrace Carbon Impact

Dynatrace

Some interesting facts: Moving a workload to the cloud can reduce its carbon footprint by up to 96%. Cloud computing has a greater carbon footprint than the airline industry. Average cloud server idle time exceeds 70%. The table breaks down emissions by data center, listing your cloud and on-premises instances.

Energy 184
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As-Salaam-Alaikum: The cloud arrives in the Middle East!

All Things Distributed

Today, I am excited to announce plans for Amazon Web Services (AWS) to bring an infrastructure Region to the Middle East! This move is another milestone in our global expansion and mission to bring flexible, scalable, and secure cloud computing infrastructure to organizations around the world.

Cloud 152
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Real-Time Digital Twins Can Help Expedite Vaccine Distribution

ScaleOut Software

We are faced with quickly building a nationwide logistics network and standing up well more than 50,000 vaccination centers. Widely used to track ecommerce shopping carts, financial transactions, airline flights and much more, in-memory computing can quickly store, retrieve, and analyze large volumes of live data.

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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

When you’re running in the cloud your containers are in a shared space; in particular they share the CPU’s memory hierarchy of the host instance. Resource allocation problems can be efficiently solved through a branch of mathematics called combinatorial optimization, used for example for airline scheduling or logistics problems.

Cache 251
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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. This can quickly saturate the network (and bog down the client).

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. This can quickly saturate the network (and bog down the client).