DSS is a scheduling simulator written in Python designed to gather statistics about the execution of big-data traces on virtual topologies, mimicking the behaviour of frameworks such as Hadoop. It was initially designed to study the impact of scheduling decisions in YARN on job processing times. It has been used to explore new scheduling strategies and their scalability to very large clusters. It is designed to be fast and fully deterministic. All sources of randomness are controlled to ensure full experiment reproducibility.
DSS takes as input an execution trace in JSON format, which describes the most important characteristics:
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the number of jobs submitted to the cluster
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the arrival time of each job
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the number of tasks per each job
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the resource requirements of each class of tasks (currently only memory and CPU cores supported, tasks implicitly use 1 core each)
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the execution time for each class of tasks
DSS works by constructing a discrete timeline of events and parsing each event in order. Such events include job arrivals, containers launched, containers finished, job completion, ResourceManager heartbeats, etc. Each event can impact the remaining timeline. For example, a JOB_ARRIVAL event at t = 5 can indirectly add several CONTAINER_LAUNCHED events at t = 6, t = 8 and so forth.
DSS was written for Python 2, but for best performance we recommend using PyPy. Download and unpack in your home folder, and, optionally, place the pypy binary in your $PATH.
You can use pip to install all the dependencies of DSS.
pip install -r requirements.txtpypy -m ensurepip
$PYPY_HOME/bin/pip install -U pip wheel # Update pip to the latest version
$PYPY_HOME/bin/pip install -r dss/requirements.txtpython dss/setup.py install-
Generate a topology file
dss/samples/topos/generate_topo.py 100 > topo_100 # Generate a 100 node topology
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Generate an input trace file
- 1000 jobs
- Job arrival times: exponential distribution [5, 5000] seconds
- No tasks / job: uniform distribution [1, 500]
- Memory MB / task: exponential distribution [100MB, 20GB]
- Task duration: uniform distribution [10, 1000] seconds
- Random seed: 12345
dss/samples/traces/trace-generator.py 1000 5 5000 exp 1 500 unif 1 200 exp 10 1000 unif 12345 > 1000job.trace -
Run DSS
- Simulate a 100 node cluster with 128GB of RAM and 32 cores each
- Use a 1000-millisecond heartbeat interval between the NodeManagers and the ResourceManager
- Use a 1000-millisecond heartbeat interval between the ApplicationMasters and the ResourceManager
- ApplicationMaster containers use 1000 MB of RAM and 1 core each
- Use the regular YARN scheduler with node reservations (-r)
dss/dss.py -r ./1000job.trace ./topo_100 128000 32 1000 1000 1000 1 regular
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Examine the output files
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job_stats.out- start / end times, job duration
- task stats: no tasks, task memory
job_0, start_ms=1786000, end_ms=53564000, duration_s=51778.00, tasks = (9400MB x 399) job_1, start_ms=260000, end_ms=47652000, duration_s=47392.00, tasks = (6000MB x 361) job_2, start_ms=141000, end_ms=52012000, duration_s=51871.00, tasks = (5000MB x 400) ... -
task_stats.out- task type: ApplicationMaster (MRAM), MAP, REDUCE
- start / end times, task duration
- allocated memory (MB)
- memory allocation ratio (
$allocated MB \over requested MB$ ) - node where task ran
job_0:1, type=MRAM, start_ms=1787000, end_ms=53563000, duration_s=51776.00, given_memory_mb=1000, alloc_ratio=1.00, node=node22 job_0:2, type=REDUCE, start_ms=1789000, end_ms=2279000, duration_s=490.00, given_memory_mb=9400, alloc_ratio=1.00, node=node12 job_0:3, type=REDUCE, start_ms=2281000, end_ms=2771000, duration_s=490.00, given_memory_mb=9400, alloc_ratio=1.00, node=node12 ... -
decision_stats.out- breakdown of scheduling decisions for each job
- TOTAL: no times the scheduler was called for that job
- ACCEPT: no times the scheduler found a node suitable for that job
- REJECT: no times the scheduler could not find a node suitable for that job
- RESERVE: no times the scheduler reserved a node for that job
job_0: TOTAL: 1350, ACCEPT: 400(29.63%), REJECT: 586(43.41%), RESERVE: 364(26.96%) job_1: TOTAL: 1208, ACCEPT: 362(29.97%), REJECT: 518(42.88%), RESERVE: 328(27.15%) job_2: TOTAL: 1177, ACCEPT: 401(34.07%), REJECT: 440(37.38%), RESERVE: 336(28.55%) ... -
reservation_stats.out- breakdown of node reservations by each job
node1: job=job_483, start_ms=38000, end_ms=44000 job=job_458, start_ms=44000, end_ms=151000 ... node2: job=job_861, start_ms=37010, end_ms=38010 job=job_483, start_ms=38010, end_ms=44010 ...
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Listed below are several supported DSS parameters. Any other parameters not listed here are no longer maintained, and as such cannot be guaranteed to function as advertised in the help page.
usage: dss.py [-h] [-r] [-v]
[-p [{TIMELINE_CHECK_CURRENT_JOB,PEEK_EACH_ELASTIC}]]
[--duration-error DURATION_ERROR]
[--duration-error-type {MIXED,POSITIVE,NEGATIVE}]
[--duration-error-mode {CONSTANT,RANDOM}]
[--duration-error-only-elastic][--mem-error MEM_ERROR]
[--mem-error-type {MIXED,POSITIVE,NEGATIVE}]
[--mem-error-mode {CONSTANT,RANDOM}][--ib-error IB_ERROR]
[--ib-error-type {MIXED,POSITIVE,NEGATIVE}]
[--ib-error-mode {CONSTANT,RANDOM}]
[--assign-multiple]
[--mem-overestimate-range MEM_OVERESTIMATE_RANGE]
[--mem-overestimate-assume MEM_OVERESTIMATE_ASSUME][--meganode]
[--occupancy-stats-interval OCCUPANCY_STATS_INTERVAL]
[--occupancy-stats-file OCCUPANCY_STATS_FILE]
sls_trace_file network_topology_file node_mem_mb node_cores
node_hb_ms am_hb_ms am_container_mb am_container_cores
[{REGULAR,GREEDY,SMARTG,SRTF,PEEK}] sls_trace_file Path to SLS trace file (JSON format).
network_topology_file Path to topo file (JSON format).
node_mem_mb Amount of memory in MB for each node.
node_cores Amount of virtual cores for each node.
node_hb_ms Period of the NM heartbeats in milliseconds.
am_hb_ms Period of the AM heartbeats in milliseconds.
am_container_mb Amount of memory in MB for each AM container.
am_container_cores Number of cores taken by each AM container.
{REGULAR,GREEDY,SMARTG,SRTF,PEEK}
Type of scheduler to run.
{NULL,POWER1,POWER2,SQUAREROOT,STEP,SAWTOOTH,SPARKWC}
The performance penalty model to use
ib The initial bump of the penalty model
-r, --use-reservations
Whether an app can place a reservation on a node to
guarantee its priority there.
--assign-multiple Run the simulation as if the assignMultiple flag is
set (multiple assignments per heartbeat are allowed).
-h, --help show this help message and exit
--meganode Pool all node resources together and run simulation on
this one node.
--occupancy-stats-interval OCCUPANCY_STATS_INTERVAL
Gather occupancy stats each interval (in seconds).
--occupancy-stats-file OCCUPANCY_STATS_FILE
File in which to output occupancy stats.
-v, --verbose
-p [{TIMELINE_CHECK_CURRENT_JOB,PEEK_EACH_ELASTIC}], --peek-pushback-strategy [{TIMELINE_CHECK_CURRENT_JOB,PEEK_EACH_ELASTIC}]
Strategy to use to avoid pushback in PEEK.
--duration-error DURATION_ERROR
Percentage by which to mis-estimate the running times
of the containers.
--duration-error-type {MIXED,POSITIVE,NEGATIVE}
Whether duration error is positive, negative or
either.
--duration-error-mode {CONSTANT,RANDOM}
Whether duration error is constant or random.
--duration-error-only-elastic
Inject duration error only for ELASTIC containers
--mem-error MEM_ERROR
Percentage by which to mis-estimate the ideal memory
of the containers.
--mem-error-type {MIXED,POSITIVE,NEGATIVE}
Whether the memory misestimation error is positive,
negative or either.
--mem-error-mode {CONSTANT,RANDOM}
Whether the memory misestimation error is constant or
random.
--ib-error IB_ERROR Percentage by which to mis-estimate the IB of tasks.
--ib-error-type {MIXED,POSITIVE,NEGATIVE}
Whether IB error is positive, negative or either.
--ib-error-mode {CONSTANT,RANDOM}
Whether IB error is constant or random.
The standard (vanilla) YARN scheduler. Will assign a task to a node if the node has sufficient resources (RAM and CPU cores) at the time of assignment.
Node reservations
Using the -r argument, the YARN scheduler will reserve a node for the job at the head of the scheduling queue if the node does not have sufficient resources.
Assign multiple
Using the —assign-multiple argument, the YARN scheduler will try to assign more than the standard 1 task / node heartbeat.
Based on the YARN scheduler, but will assign a task to a node even if less memory (RAM) than required is available. It still imposes a minimum 10% of the task required memory.
Tasks allocated with less memory than required can have a different duration based on their properties (see below). For example, a task that normally uses 2GB of RAM and runs in 10 minutes, given 1.5GB of RAM might run 15 minutes.
Similar to GREEDY, but assigns memory such that the execution time of the container is minimised. Depending on the memory elasticity model of each task, the correlation between allocated memory and task execution time may not be a linear function. Therefore, slightly counter-intuitively, giving less memory can sometimes slightly improve execution time.
Uses same allocation strategy as SMARTG, but allocates a task with fewer resources iff this decision reduces overall job completion time.
Implements a standard Shortest-Remaining-Time-First scheduler, which prioritises jobs based on an estimation of the total remaining duration of their respective containers.
Initial Bump
The maximum degradation factor expected to occur for a task if it is allocated less memory than required. Is expressed as a floating point value.
Example: A task that requires 2GB to run, and takes 10 minutes to complete, with an IB value of 2.1x will take at most 21 minutes to complete if given <2GB.
Memory elasticity model based on STEP that captures the spilling behaviour of Hadoop MapReduce containers, particularly of REDUCE tasks. Requires defining the IB value.
Memory elasticity model based on STEP that captures the spilling behaviour of Spark WordCount containers. Requires defining the IB value.
