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apps
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result/rmem
rmem
srmem
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README.md
download-trace.sh

README.md

#Disaggregation Emulator# This is an emulator for evaluating current application performance in disaggregated datacenter. The current code can run on EC2 without any modification. So you need an EC2 account to run the experiment. But there is no fundamental limitation that prevents you from using your own cluster.

The RDMA-based swap device is available at https://github.com/pxgao/rdma_swap_dev

Memory access trace is available on s3 https://github.com/NetSys/disaggregation/blob/master/download-trace.sh

##Quick Start## On your local computer, go to ec2 folder

cd disaggregation/ec2

create an ec2 cluster with 5 worker nodes

with VPC (You need to setup VPC first)

./spark-ec2 -k AWS_KEY_ID -i AWS_KEY_FILE -s 5 --region us-east-1 --zone us-east-1a --vpc-id VPC-ID --subnet-id SUBNET-ID --instance-type m3.2xlarge --ami ami-ee9d14f9 launch ddc-exp

without VPC

./spark-ec2 -k AWS_KEY_ID -i AWS_KEY_FILE -s 5 --region us-east-1 --zone us-east-1a --instance-type m3.2xlarge --spot-price 0.4 --no-ganglia --copy-aws-credentials --ami ami-ee9d14f9 launch ddc-exp

once the cluster is craeted, login

./spark-ec2 -k AWS_KEY_ID -i AWS_KEY_FILE login ddc-exp

Now you have a 5-node EC2 cluster, go to /root/disaggregation/rmem

cd /root/disaggregation/rmem

Everytime a new cluster is created, we need to first prepare the environment for the experiments.

python execute.py --task prepare-env

You may need to press "Y" when formatting the HDFS

To run a experiment, you need to prepare the data. I will demonstrate how to run terasort here. First, you need to prepare the data by running

python execute.py --task terasort-prepare

This will load the input data to HDFS. To run experiment,

python execute.py --task terasort --vary-both-latency-bw --iter 10

This will run terasort with different bandwith (10Gbps - 100Gbps) and latency(1us - 40us) combinations for 10 times. (This will take a long time. You may want to reduce that to 1 times for initial test To run other experiment such as "fix latency at 5us and vary bandwidth", "fix bandwidth at 40g and vary latency", "vary percentage of local memory", and "application degradation in a network" run

python execute.py --task terasort --vary-latency --iter 10
python execute.py --task terasort --vary-bw --iter 10
python execute.py --task terasort --vary-remote-mem --iter 10
python execute.py --task terasort --slowdown-cdf-exp 100g --iter 10

You can run other application by replacing "terasort" with "wordcount" (Spark Wordcount), "wordcount-hadoop" (Hadoop Wordcount), "terasort-spark" (Spark Terasort), "graphlab", "memcached", "bdb" (Spark SQL), "memcached", "timely" (Timely Dataflow).

##Folders##

apps The applications to run on ddc emulator

ec2 scripts to launch ec2 clusters

result result plots

rmem ddc emulator

srmem ddc emulator with partitioned memory space

turbo-sub pHost simulator