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Seamless offloading of web app computations from mobile device to edge clouds via HTML5 Web Worker migration

The Morning Paper

Edge servers are the middle ground – more compute power than a mobile device, but with latency of just a few ms. The client MWW combines these estimates with an estimate of the input/output transmission time (latency) to find the worker with the minimum overall execution latency. The opencv app has the largest state (4.6

Mobile 104
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Redis vs Memcached in 2024

Scalegrid

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Snapshots provide point-in-time captures of the dataset, which are efficient for recovery on startup.

Cache 130
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bpftrace (DTrace 2.0) for Linux 2018

Brendan Gregg

Screenshot: tracing read latency for PID 181: # bpftrace -e 'kprobe:vfs_read /pid == 30153/ { @start[tid] = nsecs; } kretprobe:vfs_read /@start[tid]/ { @ns = hist(nsecs - @start[tid]); delete(@start[tid]); }'. Here's a comparison for an average-sized DTraceToolkit script (seeksize.d) It's shaping up to be a DTrace version 2.0:

C++ 110
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Mobile browser testing – what is it and when is it done?

Testsigma

Each smartphone comes with various screen sizes and resolutions, operates on different network speeds, and has different hardware capabilities. Test how user-friendly an application is: Google search engine gives high priority to websites in comparison to desktop apps. Google ranks applications based on how user friendly it is.

Mobile 52
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Compression Methods in MongoDB: Snappy vs. Zstd

Percona

As the amount of data grows, the need for efficient data compression becomes increasingly important to save storage space, reduce I/O overhead, and improve query performance. In this blog, we will discuss both data and network-level compression offered in MongoDB. Network compression MongoDB also offers network compression.

Storage 111
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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

Figure 1: A Simplified Video Processing Pipeline With this architecture, chunk encoding is very efficient and processed in distributed cloud computing instances. Uploading and downloading data always come with a penalty, namely latency. It is worth pointing out that cloud processing is always subject to variable network conditions.

Cloud 237
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bpftrace (DTrace 2.0) for Linux 2018

Brendan Gregg

Screenshot: tracing read latency for PID 181: # bpftrace -e 'kprobe:vfs_read /pid == 30153/ { @start[tid] = nsecs; } kretprobe:vfs_read /@start[tid]/ { @ns = hist(nsecs - @start[tid]); delete(@start[tid]); }'. Here's a comparison for an average-sized DTraceToolkit script (seeksize.d) It's shaping up to be a DTrace version 2.0:

C++ 40