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in-network-hadoop-ndn-cachesimulator's Introduction

Spotlight

Spotlight is a Tailwind UI site template built using Tailwind CSS and Next.js.

Getting started

To get started with this template, first install the npm dependencies:

npm install

Next, create a .env.local file in the root of your project and set the NEXT_PUBLIC_SITE_URL variable to your site's public URL:

NEXT_PUBLIC_SITE_URL=https://example.com

Next, run the development server:

npm run dev

Finally, open http://localhost:3000 in your browser to view the website.

Customizing

You can start editing this template by modifying the files in the /src folder. The site will auto-update as you edit these files.

License

This site template is a commercial product and is licensed under the Tailwind UI license.

Learn more

To learn more about the technologies used in this site template, see the following resources:

  • Tailwind CSS - the official Tailwind CSS documentation
  • Next.js - the official Next.js documentation
  • Headless UI - the official Headless UI documentation
  • MDX - the MDX documentation

in-network-hadoop-ndn-cachesimulator's People

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in-network-hadoop-ndn-cachesimulator's Issues

Missing spark-traces-aws-cluster-2017-11-22?

Hi,

I am very interested in your project and tried running the runTests.sh script but the repository seems to be missing the spark-traces-aws-cluster-2017-11-22 directory which should be above the root directory of this project?
It would be very helpful to me if you could provide me with that directory and its contents or even with a small readme to run your application?

Best

TinyLFU?

Hi!

I was happily surprised to see that you adapted policies from my cache simulator for your research. Hopefully its been helpful.

My evaluation led to adopting TinyLFU after making some modifications. I found it often performed as well as or better than LIRS, but is far simpler to implement and make efficient. Implementing LIRS from the papers and original C code is more reverse engineering than understanding, though it does extremely well. I've been very happy with TinyLFU and contributed towards a longer paper, so I would find it interesting to see how it does in your analysis.

Also, when appropriate please send me your paper / results.

Cheers,
Ben

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