Figure 7

Figure 7 (page 9) repeats the experiment shown in Figure 5, but shows a comparision between QJump and a collection of other systems. It compares against an idle network case, a contended case, Ethernet Flow Control, Explicit Congestion Notifications, DCTCP and QJump.

Figure 7

Software Required

In addition to these general software requirements, we also used:

Physical Configuration

The basic physical network configuration is described here. In this experiment the following configuration was used:

We configured the switch for Ethernet Flow Control or Explicit Congestion Notifications as per the configuration manual .

Patches

Memaslap

To obtain more detailed results from memaslap, we patched it to collect statistics in memory and dump them to standard output when complete. The patch file should be applied to the clients sub-directory of the libmemcached 1.0.15 source tree listed above. To build memaslap, run "./configure --enable-memaslap" followed by "make && sudo make install".

DCTCP

In experiments using DCTCP, we upgraded the DCTCP kernel patch to run on a Linux 3.4.55 kerene. The upgraded version of the DCTCP patch found here. We installed and configured DCTCP following the instructions found here.

Sofware Configuration

The full Hadoop Map-Reduce configuration can be found here. The PTPd client and server were configured as follows:

Server

e=1; p=0; ./qjau.py -p $${p} -c "ptpd -c -b eth6.2 -y 0 -D -h -T 10"

Client

e=0; p=7; ./qjau.py -p $${p} -c "ptpd -x -c -g -D -b eth0 -h -T 10 -f ../data/ptpdCFRS201_memcachedBS206_hadoop_R2D2P$${p}_PTPD_$${e}

Where "$$e" is th experiment number and "$$p" is the QJump prioirty level.

Memcached and memaslap were configued as follows:

Server

e=1; p=0; sudo ./qjau.py -p $${p} -c "/usr/bin/memcached -m 64 -p 11211 -u memcache"

Client

e=1; p=0; for {1..25}; do
   ./qjau.py -p $${p} -c "../clients/memaslap -s 10.10.0.3:11211 -S 1s -B -T2 -c 128 -X 1024" > ../data/ptpdCFRS201_memcachedBS206_hadoop_R2D2P$${p}_$$(printf "%02i" $${i})_MEMD_$${e};
done

Where "$$e" is th experiment number and "$$p" is the QJump prioirty level.

When QJump was enabled, it was configured as follows

bytesq=256 timeq=5 p0rate=1 p4rate=100 p7rate=9999999

Raw Data

PTPd Data

Our original PTPd unprocessed datasets can be found here (478kB). Inside the archive are file names of form:

Each file contains around 600 entiries and 7 lines of headers. Entires in the file are of the form:

2014-01-22 19:51:27:363605, slv, 90e2bafffe27fbc8/01, 0.000140635, -0.000019134, 0.000197000, 0.000108000, 21106

Where the first column is the timestamp, and the fifth coloum is the syncornization offset.

Memcached Data

Our original unprocessed memcached datasets can be found here. There are 47 archives of approx 1GB each (~5GB uncompressed). The file names are of the form:

Each archive contains 25 files, each with approximately 6,000,000 entiries and 7 lines of headers/footers. Entires in each file are of the form:

{GET | SET | TOTAL} , REQUEST ID, LATENCY

Hadoop Data

Our original unprocessed Hadoop datasets can be found here (881B). The file names are of the form:

TOTAL_TIME: 126

Where time is a value in seconds that each join took.

Experimental Configurations

Exp. No. Description
0-2 Memcached baseline - no contention
0 -1 PTPd baseline - no contention
1-5 Hadoop baseline - no contention
101-105 Contended, no mitigation
106-110 Contended, using QJump
111-115 Contended, using Ethernet Flow Control
116 Contended, using Explicit Congestion Notifications, configured with min = 5 segements, max = 10 segments
117 Contended, using Explicit Congestion Notifications, configured with min = 10 segements, max = 20 segments
118 Contended, using Explicit Congestion Notifications, configured with min = 20 segements, max = 40 segments
119 Contended, using Explicit Congestion Notifications, configured with min = 40 segements, max = 80 segments
120 Contended, using Explicit Congestion Notifications, configured with min = 80 segements, max = 160 segments
122 Contended, using Explicit Congestion Notifications, configured with min = 160 segements, max = 320 segments
123 Contended, using Explicit Congestion Notifications, configured with min = 320 segements, max = 640 segments
124 Contended, using Explicit Congestion Notifications, configured with min = 640 segements, max = 1280 segments
125 Contended, using Explicit Congestion Notifications, configured with min = 1280 segements, max = 2560 segments
126 Contended, using Explicit Congestion Notifications, configured with min = 2560 segements, max = 5120 segments
127-131 Contended, using DCTCP

Processing and plotting the results

In the figure we use the Root Mean Squared (RMS) value of each application specific metric. We do this to to make easy comparisons of the magnitude of variance of the these time varying qunatities. Scripts to process the data and produce RMS outputs can be found here. All of the PTPd and Hadoop data can be processed at once by running;

process_ptpd_rms.py ../data/ptpd_data/* > ptpd.rms

process_hadoop_rms.py ../data/hadoop_data/* > hadoop.rms

Memcached files are large and need to be done one archive at a time:

tar -xvf ptpdCFRS201_memcachedBS206_hadoop_R2D2P0_MEMD_104.tar.bz2
process_memd_rms.py TOTAL ptpdCFRS201_memcachedBS206_hadoop_R2D2P0_MEMD_104

The results from the processing scripts above should look something like this:

5.314597 438 17 ../data/ptp_data/ptpdCFRS201_memcachedBS206_hadoop_R2D2P0_PTPD_119

Where 5.314... is the RMS value, 438 is the number of data points, 17 is the number lines skipped and ../data/ptp_data/ptpd... is the name of the source of the data.

To speed up processing, we provide the process_rms_out.py to summarise the above values by taking averages of multiple measurements.

./process_rms_out.py ptpd.rms
001 Alone 5.636801 (2)
002 Contended 1792.462081 (5)
003 Qjump 6.640541 (5)
004 Eth Flow. Cnt 71144.949588 (5)
005 DCTCP 12.296890 (4)
116 ECN-5:10 0.000000 (0)
117 ECN-10:20 4.605076 (1)
118 ECN-20:40 0.000000 (0)
119 ECN-40:80 5.314597 (1)
120 ECN-80:160 5.165254 (1)
122 ECN-160:320 9.894793 (1)
123 ECN-320:640 24.357411 (1)
124 ECN-640:12800 80.392287 (1)
125 ECN-1280:2560 99.046768 (1)
126 ECN-2560:5120 155.500869 (1)

These results where then manually compiled into a csv file like the one that can be foud here. Then, to produce Figure 7, run something like the following:

python plot_pf_ecn_dctcp_barchart-all.py ../data/bars-rms.csv \
Alone Ideal \
Hadoop "Contended" \
PauseFrames "Eth. Flow Ctrl." \
ECN "ECN" \
DCTCP "DCTCP" \
QJump "\\textbf{\textsc{QJump}}" out.pdf \
figure7.pdf