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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

The idea CFS operates by very frequently (every few microseconds) applying a set of heuristics which encapsulate a general concept of best practices around CPU hardware use. The second placement looks better as each CPU is given its own L1/L2 caches, and we make better use of the two L3 caches available.

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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Additionally, DBMS is critical in reservation systems, where it stores and manages records like ticket bookings, schedules, seat allocation, and other pertinent transaction data for airlines, hotels, and railways. By implementing data abstraction techniques, these challenges can be addressed more effectively.

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AI for everyone - How companies can benefit from the advance of machine learning

All Things Distributed

As a result, there is a critical mass of data available. That is now changing, as packages of AI and ML services, frameworks and tools are today available to all sorts of companies and organizations, including those that don't have dedicated research groups in this field. They form the basis for new business models.

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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. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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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. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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COVID-19 Hazard Analysis using STPA

Adrian Cockcroft

The opening paragraph above is the same as my previous discussion focused on hardware, software and operational failure modes , but we are now in the middle of a pandemic, so I’m going to adapt the discussion of hazards and failure modes to our current situation. They could spend too long deciding what to do.

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Using Parallel Query with Amazon Aurora for MySQL

Percona

On multi-core machines – which is the majority of the hardware nowadays – and in the cloud, we have multiple cores available for use. I’m using the “Airlines On-Time Performance” database from [link] (You can find the scripts I used here: [link] ). With faster disks (i.e. AWS Aurora (based on MySQL 5.6) MySQL on ec2.

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