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What is it?


Hawk is a Hybrid Data Center Scheduler presented at Usenix ATC 2015

It takes the best of both worlds combining centralized and distributed schedulers. It has the following main features:

  1. Hybrid Scheduling. Schedules Long jobs in a centralized way (better scheduling decisions) and Short jobs in a distributed way (better scheduling latency).

  2. Work stealing. To do better load balance when a node is free it will contact another one and 'steal' the short-latency-sensitive jobs in the queue.

  3. Partitioning. It prevents Long jobs from taking all the resources in the cluster so that Short jobs do not experience head-of-line blocking.


Eagle is currently work in progress. A beta version is available here. Eagle aims to avoid the Head-of-Line blocking that short jobs experience in distributed schedulers by providing and approximate/fast view of the Long jobs.


In order to run Hawk and Eagle you need to have Java JDK 1.7 installed and Maven.

This command installs Hawk/Eagle locally.

$ mvn install -DskipTests 

Getting started

Create a configuration file with the following parameters.


deployment.mode = configbased		# currently only this mode is supported
static.node_monitors =<hostname_1>:20502	# comma sepparated list of nodes where jobs will run = spark			# the application name, this can also be changed as a java opt
system.memory =10240000			# 
system.cpus=1				# currently only one slot per machine is supported
sample.ratio=2				# number of probes per task for distributed schedulers, 'power of two'
cancellation=no				# after a job finishes will cancel the rest of the probes, in practice makes no difference
scheduler.centralized=<centralized_scheduler_ip>	# centralized scheduler IP, if no centralized scheduler set
big.partition=80				# the percentage of nodes where Long jobs can run
small.partition=100				# the percentage of nodes where Short jobs can run
nodemonitor.stealing=yes			# enable Hawk stealing
nodemonitor.stealing_attempts=10		# number of stealing attempts
eagle.piggybacking=no			# enable Eagle
eagle.retry_rounds=0			# number of rounds distributed schedulers should try before going to small partition


To enable Eagle you need to change the following parameters

nodemonitor.stealing=no			# enable Hawk stealing
nodemonitor.stealing_attempts=0		# number of stealing attempts
eagle.piggybacking=yes			# enable Eagle
eagle.retry_rounds=3			# number of rounds distributed schedulers should try before going to small partition

After creating the configuration file you can run Hawk/Eagle daemon with the following command (replace JAVA_DIR, EAGLE_JAR and CONF_FILE with their corresponding paths):

$ JAVA_DIR -XX:+UseConcMarkSweepGC -verbose:gc -XX:+PrintGCTimeStamps -Xmx2046m -XX:+PrintGCDetails -cp EAGLE_JAR ch.epfl.eagle.daemon.EagleDaemon -c CONF_FILE

Now you need to run a front end application, you can test it with a Spark program for example.

Spark plugin

We also have a plugin for Spark, you can find it here.

You can compile it using the following command, provided you installed Eagle first.

$ build/sbt assembly

You can run an example with JavaSleep, for that you need to create a file with the jobs sleeping time. The input file should have the following format:

[Each line a job]
Col1: job arrival time
Col2: number of tasks in job
Col3: estimated job runtime (we use normally the mean)
Col4: (and as many cols as needed) the real duration of each task for the job (for the sleep)

570    2 2722 2722 2722 
  1. Start the driver.

    $ spark/bin/spark-run<driver_hostname>
    org.apache.spark.examples.JavaSleep "eagle@$SCHEDULER:20503" 5 3 hostname $SMALL "<path_to_input_file>"

SMALL can take the values: "small" or "big" depending on if its the centralized or the distributed (centralized --> big)

  1. Start the backends. This should run in each of the nodes

    $ spark/bin/spark-run
    org.apache.spark.scheduler.eagle.EagleExecutorBackend --driver-url spark://EagleSchedulerBackend@<driver_hostname>:60501


Hawk and Eagle are meant to improve job completion times in large clusters, to simulate with tens of thousands of nodes we used a simulator. This simulator is in Python, please refer to its README for further information.



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