Remove c
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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Its goal is to assign running processes to time slices of the CPU in a “fair” way. Instead, what if we reduced the frequency of interventions (to every few seconds) but made better data-driven decisions regarding the allocation of processes to compute resources in order to minimize collocation noise? So why mess with it?

Cache 251
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Human Factors Behind Incidents: Why Settings Like “idle_session_timeout” Can Be a Bad Idea

Percona

Unexpected connection leaks: Unanticipated connection leaks can also arise from programming errors or faulty application logic. It should be a tight scrutiny like airline security. The point forgotten is that c onnections are owned by clients but maintained by a database. The problem generally begins as a conflict of interests.

Servers 79
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Using Real-Time Digital Twins for Corporate Contact Tracing

ScaleOut Software

In addition, they can log contacts outside the company, such as taxi rides, airline flights, and meals at restaurants, so that community members can be notified if an employee was exposed to COVID-19. As illustrated in the first diagram, each real-time digital twin hosts two components, state data and a message-processing method.

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Using Real-Time Digital Twins for Corporate Contact Tracing

ScaleOut Software

In addition, they can log contacts outside the company, such as taxi rides, airline flights, and meals at restaurants, so that community members can be notified if an employee was exposed to COVID-19. As illustrated in the first diagram, each real-time digital twin hosts two components, state data and a message-processing method.

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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. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.

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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. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.