BOINC client simulator

The BOINC client simulator simulates a single BOINC client interacting with one or more projects. The simulator uses the same source code as the client for the CPU scheduling and work-fetch policies, so it models the BOINC client accurately.

The intended uses of the simulator include:

• Identifying scenarios (combinations of host and project characteristics) where the current scheduling policies don't behave well.
• Studying experimental policies.

However, the simulator is not necessarily perfect - in some cases its results may differ significantly from what the actual client would do. Or its inputs may be inadequate to describe a real-life scenario. If you find such cases, please send email to David Anderson or boinc_dev.

You can use the simulator in either of two ways:

• Through a web interface. This lets you do one simulation at a time, and shows you results graphically.
• Compile it yourself and run from a command line. This provides a more flexible interface.

Input files

The input consists of the following files:

client_state.xml

This describes a set of attached projects. The format is an extension of the state file generated by the client; you can use the state file of a running client as an input to the simulator.

The fields used by the simulator are as follows (fields marked with * are not generated by the client).

host_info
	p_ncpus
	p_fpops
	m_nbytes
        coprocs

These describe the hosts's processing hardware. The simulator doesn't model disk or memory usage.

time_stats
	on_frac
	connected_frac
	active_frac
	gpu_active_frac
        *on_lambda
        *connected_lambda
        *active_lambda
        *gpu_active_lambda

These describe the host's availability:

• on_frac: the fraction of total time this host runs the client
• connected_frac: of the time this host runs the client, the fraction it is connected to the Internet.
• active_frac: of the time this host runs the client, the fraction it is enabled to use CPU
• gpu_active_frac: of the time this host runs the client, the fraction it is enabled to use GPU (always <= active_frac).

For periods of activity and inactivity are exponentially distributed. The mean of the activity periods can be specified with on_lambda etc.; the default is 1 hour.

project
	project_name
	resource_share
	*available
		frac
		lambda
app
	name
	*latency_bound
	*fpops_est
	*fpops_actual
		mean
		stddev
	*weight
app_version
	app_name
	avg_ncpus
	flops
	plan_class
	coproc
		type
		count
	gpu_ram
	*working_set
workunit
	app_name
	rsc_fpops_est
	rsc_fpops_bound
result
	name
	report_deadline
	received_time
active_task
	result_name
	working_set_size

Notes: Each application has a fixed latency bound. It can be specified in app.latency_bound. If not, and there is a result for that app, it is computed as report_deadline - received time for one such result. If there is no result, it is 1 week.

An application has a fixed FLOP count estimate. It can be specified as app.fpops_est. If not, and there is a WU for that app, it is wu.rsc_fpops_est. Otherwise it is 3600*1e9 (i.e., 1 GFLOPS/hr).

An application has a normal distribution of actual FLOP count. It can be specified as app.fpops_actual. Otherwise it is mean app.fpops_est, stddev 0.

An application has an associated weight that determines the fraction of its jobs dispatched by that project. This defaults to 1.

An application version has a fixed working set size. This can be specified as app_version.working_set. If not, and there is an active task for that app version, active_task.working_set_size is used. Otherwise it defaults to 0.

The availability of the projects (i.e. the periods when scheduler RPCs succeed) is modeled with two parameters: the duration of available periods are exponentially distributed with the given mean, and the unavailable periods are exponentially distributed achieving the given available fraction. The availability of a project can be specified as project.available; otherwise it is always available.

The algorithm for simulating a scheduler RPC to project P is:

while need more work
   X = list of P's apps with versions for requested resources
   if X is empty
      break
   choose an app A from X, randomly based on weights
      V = version that uses requested resources and has highest FLOPS
      J = generate job
      if J is feasible
         update request
      else
         infeasible_count++
         if infeasible_count == 10
            break

The available periods (i.e., when BOINC is running) and the idle periods (i.e. when there is no user input) are modeled as above.

global_prefs.xml

format described here.

cc_config.xml

format described here?.

Building and running the simulator

The simulator can be built with 'makefile_sim' on Unix or the 'sim' project on Windows. The usage is:

sim [--duration X] [--delta X] [--server_uses_workload] [--dirs d1 ...]

--duration

simulate this much time.

--delta

time step of simulation.

--server_uses_workload

servers take existing workload into account when deciding whether to send jobs.

--dcf_dont_use

Duration correction factor (DCF) is one.

--dcf_stats

Use formula for DCF based on completion time mean/stdev.

--dirs d1 …

chdir into each of the given directories, and runs a simulation based on the input files there. Prints summaries of each one separately, and a total summary.

Output files

The simulator creates two output files:

sim_log.txt: This is the message log (same as would be generated by the client). Its contents are controlled by cc_config.xml.

sim_out.html: When viewed in a web browser, a 'time line' showing what's running when. The bottom of this file contains four numbers that summarize how well the policies performed:

wasted_frac

Of the total CPU time, the fraction spent computing results that missed their deadline.

idle_frac

Of the total CPU time, the fraction spent not computing.

share_violation

A measure (0 to 1) of how badly resource shares were violated.

monotony

A measure (0 to 1) of how long a single project used all CPUs (so that user would see only that project on their screensaver, and get bored).

In addition, information is printed about the per-project CPU time and waste.