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This project provides simple tools to replay http request traces to evaluate the performance of caching systems and webservers. We also build a simulator available here.

There are three components:

  • the client: which reads in the trace file and generates valid http requests
  • the proxy: the cache server. We build on top of Apache Traffic Server.
  • the origin: which emulates a database or storage server

These tools were built to evaluate the Learning relaxed Belady algorithm (LRB), a new machine-learning-based caching algorithm. The client and origin were build on top of webtracereplay, see References for more information.

Install webtracereplay

Please follow the instruction to install the origin, client and cache proxy.

Request traces

We will need request traces for the client (to request objects) and the origin (to emulate objects). We preprocess the trace for you: download link. To uncomress:

tar -xzvf wiki2018_4mb.tar.gz
#move origin trace ( to origin:~/webtracereplay/
#move client trace ( and to client:~/webtracereplay/

Trace format

The trace format is similar to LRB simulator. The differences:

  • Request is chunked to 4MB at maximum. This is to simply ATS caching without considering internal storage chunking.
  • Origin trace only has key, size, extra feature information without time. It it used to generate "fake" response.
  • Client trace only has time, id, size information without extra features. The size information is used only for measurement.

Run an experiment

We made a scripts to run the experiment on Google Cloud. To run it

  • Create a VM in google cloud with Ubuntu 18.04 OS
  • Install client/origin/proxy on this machine.
  • Download the Wikipedia trace to this machine.
  • Make a snapshot of this machine, and then terminate this machine.
  • On your local machine, fill in the Google Cloud variables in scripts/ Note that you may need to modified the home directory and username in the scripts.
  • Use scripts/ from your local machine to run an experiment. usage

./ trace algorithm trace_timestamp test_bed trail
  • trace: the trace file prefix
  • algorithm: current support: LRB, LRU, FIFO, Unmodified
  • trace_timestamp: 0 (clients send requests in a closed-loop) or 1 (clients send request based on trace timestamp)
  • test_bed: current only support: gcp
  • trail: versioning purpose. Not effect in results.

Result log will be download to current local folder after experiment finishes.

Example: test Unmodified ATS under max workload

# on your local machine
./ wiki2018_4mb Unmodified 0 gcp 0

Example: test LRB under normal workload (trace timestamp)

# on your local machine
./ wiki2018_4mb LRB 1 gcp 0

Result format

throughput.log: client side throughput

reqs/s bytes/s sampled latency time since last print (usually 100ms)

origin.log: origin side throughput

reqs/s bytes/s time since last print (usually 1000ms)

latency.log: user latency histogram

type (end-to-end latency or first-byte latency) latency (log10(ns)) count

top.log: log from top command

ps.log: log from ps command with pcpu,rss,vsz fields.

Contributors are welcome

Want to contribute? Great! We follow the Github contribution work flow. This means that submissions should fork and use a Github pull requests to get merged into this code base.

If you come across a bug in webcachesim, please file a bug report by creating a new issue.


We ask academic works, which built on this code, to reference the LRB/AdaptSize papers:

Learning Relaxed Belady for Content Distribution Network Caching
Zhenyu Song, Daniel S. Berger, Kai Li, Wyatt Lloyd

AdaptSize: Orchestrating the Hot Object Memory Cache in a CDN
Daniel S. Berger, Ramesh K. Sitaraman, Mor Harchol-Balter


Replay http request traces to evaluate the performance of webservers or caching systems.







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