Today's Heavy Networking continues a conversation about the broadcast industry's transition to IP. We consider the challenges IP networks face when implemented in a broadcasting environment, and why IP is moving ahead anyway. Our guests are Ricki Cook and Cyrus Hira.
The post Heavy Networking 424: Broadcast Media Using IP Networks Part 2 appeared first on Packet Pushers.
Huawei and ZTE are vying for supremacy in China’s 5G R&D trials against the backdrop of an increasingly hostile international environment.
HTTPS was created to ensure end to end encryption of web traffic but both good guys and attackers circumvent this with man-in-the-middle interception. In this Short Take, Russ talks about some of the mechanics of HTTPS interception as well as some implications of doing it intentionally.
The post Short Take – HTTPS Interception appeared first on Network Collective.
It’s all about the journey. The path to automation is all about becoming more efficient in all the right areas to make your data center an asset and not an anchor.
It might be a bit early to call generative adversarial networks (GANs) the next platform for AI evolution, but there is little doubt we will hear much more about this beefed up approach to deep learning over the next year and beyond. …
Deep Learning Hardware for the Next Big AI Framework was written by Nicole Hemsoth at .
Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR’19
Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser.
Query optimisers are traditionally composed of carefully tuned and complex heuristics based on years of experience. Feedback from the actual execution of query plans can be used to update cardinality estimates. Database cracking, adaptive indexing, and adaptive query processing all incorporate elements of feedback as well.
In this vision paper, we argue that recent advances in deep reinforcement learning (DRL) can be applied to query optimization, resulting in a “hands-free” optimizer that (1) can tune itself for a particular database automatically without requiring intervention from expert DBAs, and (2) tightly incorporates feedback from past query optimizations and executions in order to improve the performance of query execution plans generated in the future.
If we view query optimisation as a DRL problem, then in reinforcement learning terminology the optimiser is the agent, the current query plan is the state, and each available action Continue reading
If you want to see what the future of iron to support machine learning looks like, then perhaps the best place to look at what the hyperscalers and cloud builders who account for the vast majority of processing and applications in this field are deploying. …
Peering Into The Future Of Machine Learning Hardware was written by Timothy Prickett Morgan at .
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https://codingpackets.com/blog/books-i-read
https://codingpackets.com/blog/books-i-read