The Morning Paper

SEPTEMBER 24, 2019

Each app was then executed on a physical mobile phone equipped with a custom OS and network monitor. The apps are driven using Android’s Application Exerciser Monkey which injects a pseudo-random stream of simulated user input events into the app (a UI fuzzer). most apps). most apps). Finding out how those apps leak data.

Systems 74

Systems Traffic Network Google 74

Percona

DECEMBER 5, 2023

Certainly, you can take advantage of this when you work with a large data set, and the initial copy could lead to long timeframes or network saturation. The scenario Service considerations In this exercise, we wanted to perform a major version upgrade from PostgreSQL v12.16 to PostgreSQL v15.4.

Database 103

Database Traffic C++ Servers 103

The Morning Paper

JULY 4, 2019

degraded hardware, transient networking problem) or, more often, because of some change deployed by Netflix engineers that did not have the intended effect. In this type of environment, there are many potential sources of failure, stemming from the infrastructure itself (e.g. Defining and running experiments.

Latency 77

Latency Engineering Metrics Traffic 77

Adrian Cockcroft

NOVEMBER 11, 2019

There are many possible failure modes, and each exercises a different aspect of resilience. They are concerned with physical locations and cloud regions, networking failures, problems with infrastructure hardware, and failures of the control planes used to provision and manage infrastructure.

Latency 53

Latency Systems Engineering Hardware 53

Adrian Cockcroft

NOVEMBER 11, 2019

There are many possible failure modes, and each exercises a different aspect of resilience. They are concerned with physical locations and cloud regions, networking failures, problems with infrastructure hardware, and failures of the control planes used to provision and manage infrastructure.

Latency 52

Latency Systems Engineering Hardware 52

John McCalpin

AUGUST 1, 2018

This is an intellectually challenging and labor-intensive exercise, requiring detailed review of the published details of each of the components of the system, and usually requiring significant “detective work” (using customized microbenchmarks, hardware performance counter analysis, and creative thinking) to fill in the gaps.

Hardware 40

Hardware Transportation Performance Latency 40

John McCalpin

AUGUST 1, 2018

This is an intellectually challenging and labor-intensive exercise, requiring detailed review of the published details of each of the components of the system, and usually requiring significant “detective work” (using customized microbenchmarks, hardware performance counter analysis, and creative thinking) to fill in the gaps.

Hardware 40

Hardware Transportation Performance Latency 40

SQL Server According to Bob

FEBRUARY 10, 2019

Network Trace. Looking at network traces helps build our understanding of the problem. Network Layer Overloaded. In contrast to the failing SQL Server login, this is what a network trace looks like when the network layer becomes overloaded and is not completing requests to the SQL Server. Windows TCP Settings.

Servers 40

Servers Network Database Systems 40

SQL Performance

JUNE 10, 2020

Even if you don’t end up with bugs, you could end up generating unnecessary network traffic by returning columns that the application doesn’t really need. Now this could be a really interesting exercise in patients to hit F5 repeatedly until you get two different values. The application could end up with bugs.

C++ 109

C++ Database Servers Code 109

SQL Performance

JUNE 10, 2020

Even if you don’t end up with bugs, you could end up generating unnecessary network traffic by returning columns that the application doesn’t really need. Now this could be a really interesting exercise in patients to hit F5 repeatedly until you get two different values. The application could end up with bugs.

C++ 93

C++ Database Servers Code 93

The Morning Paper

OCTOBER 8, 2019

Ours is a story of the elements we found useful in applying neural networks to a real life product. From this foundation, they took their first steps towards neural networks. Airbnb moved from their starting point with a Gradient Boosted Decision Tree (GBDT) model towards deep neural networks in stages. Is it worth it?

Network 82

Network Architecture Tuning Traffic 82