Loose Screws

The rantings and musings of a pseudo-erudite lunatic

Add-on Performance Testing via Power Usage

We’ve struggled for a long time to programmatically measure the objective performance impact of add-ons on Firefox. While a perfect solution is still far off, we’ve recently started down an interesting new avenue: automatically measuring the impact of add-ons on the computer power consumption during test runs. This turns out to be a surprisingly useful proxy for performance overhead, since power consumption is directly affected by the amount of real work a CPU and GPU have to do, as well as total application runtime. Thanks to hardware donations from Intel, we’ve begun a preliminary set of automated test runs against a subset of the most popular Firefox add-ons. The results so far have been promising, with numbers generally well in line with our expectations.

Methodology

Our initial methodology involves running Firefox, via a simple test harness, while collecting power usage data via Intel’s Power Gadget software. During the test runs, the test harness performs a number of common tasks, including loading a series of URLs in tabs, closing all tabs, and finally performing garbage and cycle collection to clean up any unused memory. For our initial series of tests, we’ve done 5 runs per add-on, graphing the results with the median total power consumption, in order to minimize the impact from uncontrolled factors.

We’ve taken a few precautions in order to prevent tainted data, including:

  • Running Firefox once after the initial add-on install and throwing away the results, in order to prevent interference from first-run initialization tasks.
  • Disabling Firefox update and reporting services.
  • Disabling Windows system services such as the search indexer, screen saver, and system updates.
  • Disabling power management features such as adaptive screen brightness, screen dimming, CPU scaling, and system suspend.
  • Disabling the Adobe Flash plugin.

For future runs, we’d like to take a number of other precautions to tighten the margin of error, including:

  • More aggressive disabling of Windows services and background tasks.
  • Monitoring of CPU usage of other processes during the run, to weed out outliers.
  • Using a more standardized set of web pages for the test runs, in order to prevent interference due to network issues and content which changes throughout the runs (such as some runs using animated advertisements and others using static images).
  • Performing a larger number of runs per add-on, and interspersing single runs for each add-on with those of others, rather than performing all runs for a single add-on in series.
  • Mark garbage and cycle collection pauses in output graphs.
  • Use custom initialization code to ensure add-ons are in a state similar to what we would expect in the wild.

Results

Please note: These results are preliminary and do not necessarilly indicate that any of the add-ons in question should be used or avoided.

An overview of a subset of the first results is pretty telling:

Power test results

There’s a fairly wide margin of error, but even so there are some clear outliers, with NoScript clearly improving performance, and FastestFox and AnonymoX clearly hurting it. A detailed overview of the results gives some more insight:

Baseline

Let’s start with an overview of the baseline results:

Baseline results

Here you see a chart of the current and cumulative power consumption, where the x access is seconds since the start of the process. The colored bands indicate the time from when we start to load a URL until the load event fires, for each of the following URLs:

  1. http://ruby-doc.org/stdlib-2.0.0/
  2. https://www.google.com/search?num=50&hl=en&site=&tbm=isch&source=hp&biw=1918&bih=978&q=mozilla&oq=&gs_l=
  3. https://en.wikipedia.org/wiki/Mahler
  4. http://www.youtube.com/
  5. http://www.smbc-comics.com/
  6. http://slashdot.org/

The three plot lines at the end of the graph indicate the time from when we begin removing tabs, the time when we begin garbage and cycle collection, and the time when we ask the browser to quit, respectively.

The power use spike between the load of URLs 3 and 4 is consistent across all add-ons tested, and most likely represents a garbage collection cycle.

NoScript

We see a subtle, but substantial, difference in the NoScript results:

NoScript results

Not only are load times for pages substantially shorter, but power consumption during and shortly after page load is also markedly lower. The lack of page scripts, in this case, essentially negates the performance impact they would normally cause. In many cases this makes for significantly less reflow during page load, less external media loads such as sharing widgets, and less time away from the main event loop while page code is running. While this doesn’t tell us much about the overhead of NoScript code itself, it does suggest that it’s more than balanced by its impact on overall performance, in the absence of user whitelisting. In ordinary use, most of these benefits are likely lost due to the whitelisting of scripts from trusted sites.

AnonymoX

In the case of AnonymoX, the primary impact seems to be from drawn-out load time:

AnonymoX results

Since the primary function of the add-on is to route all traffic through an anonymizing proxy, we expect load times to suffer, and therefore overall power consumption to increase as well. While this may not be avoidable by the add-on, the raw numbers mirror the very real performance impact of the add-on. This suggests that even for unusual cases, the results of these tests may be useful in providing us data that we can use to warn users that the add-on may impact performance, so that they can make an informed decision about whether to install it.

AutoPager

AutoPager seems to tell a different story still:

AutoPager results

Here we don’t see a significant effect on load time, but we do see a noticeable power usage increase after the load event fires. This suggests that the add-on is doing a considerable amount of work after page load, which is a fairly common pattern in add-ons, and may suggest avenues for improvement.

Conclusions

The results are still preliminary, and the testing procedure needs to be refined and broadened, but the clear and consistent outliers in the results strongly suggest that this is a fruitful avenue for assessing the performance impact of our top add-ons. With expanded testing, this process may be an important tool to find and address the most significant add-on-related performance impacts to our user base.

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