In order for operators to execute blended service models successfully, a policy-based and predictive analytics-driven approach to end-to-end service management will be essential.
This balance is also important when looking at the interaction within a server between the network cards (which have some on-board buffering) and the DPDK managed buffer resources on the host. A better tuning of the buffer sizes can eliminate potential packet losses. This paper is summarizing what to do when going from one type of network card to another one that has different on-board buffer behavior. It also has the potential to explain and fix certain packet loss issues going from one generation of a NIC card to another (e.g. when moving from Intel® Ethernet Server Adapter X520 to Intel® Ethernet Controller XL710)
Basically it comes down to configuring the RX descriptors.
So, to avoid packet losses due to CPU core being interrupted when using Fortville (or when using Niantic and SRIOV), the number of RX descriptors should be configured high enough, for instance to 2048.
Wired Ethernet: Intel® Ethernet X520 to XL710 -… |Intel Communities : https://communities.intel.com/community/wired/blog/2017/01/09/intel-ethernet-x520-to-xl710-tuning-the-buffers-a-practical-guide-to-reduce-or-avoid-packet-loss-in-dpdk-applications
Link to local version PDF File for my future self (hi there!)
X520_to_XL710_Tuning_The_Buffers.pdf
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Software defined infrastructure sprawl is worst where it is compound.
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Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult.
The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource …
Refining Oil and Gas Discovery with Deep Learning was written by Nicole Hemsoth at The Next Platform.