Man VS Machine Learning Based Optimizations | Instart Logic

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MAN VS. MACHINE LEARNING-BASED OPTIMIZATIONS
BY DAWN PARZYCH
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In the previous post I introduced the application
optimization quadrant, rating some popular FEO
techniques in terms of performance impact,
reliability, and time to implement. The challenge
with many of these optimizations is that they
rely on humans to write the code and determine
the optimal settings, but humans are prone to
error. This is why Instart Logic realized that in
order to optimize an application, a different
approach was needed  one that harnessed the
power of machine learning to deliver the most
reliable, high performance optimizations in the
least amount of time. Today I will be re-visiting
that quadrant to add Instart Logics application
optimizations, which utilize machine-based
learning techniques. First, a brief introduction
to what is meant by machine-based learning.
Computers are programmed by humans, so doesnt
that mean a computer is only as smart as the
human that programmed it? If that means using
static programmatic instructions, like "convert
all images to WebP" or "combine all external
JavaScript into a single file," then yes.
Programs that automate these tasks are useful,
but it doesnt address the fact that there is no
one-size-fits-all answer, and that implementing
an optimization may cause an application to break
or perform worse. Machine-based learning doesn't
mean following static program instructions, but
instead means creating algorithms that have the
ability to learn from and make predictions based
on various types of data. Machine learning is
ideal in situations where you cannot code the
rules, you cannot scale, or the environment is
continually changing. When rules become too
complex due to the number of variables and they
need to be finely tuned, it becomes difficult for
a human to accurately perform the task. Take the
case of image optimization. To optimize an image,
the following factors need to be considered what
browser is being used, what the dimensions of the
image are, at what point does image quality begin
to degrade, what image format provides the best
image in terms of quality and size... the list
goes on. For a human to determine the ideal
settings for an image could take days as
different permutations are applied and examined.
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Most applications have more than one image, and
new images can be added on a daily or hourly
basis, so going through the painstaking process
of determining the ideal image settings manually
is essentially impossible. This is why most image
optimization solutions set a single best-guess
compression level for all images and call it
good. Machine learning is all around you in
the suggestions that Amazon and Netflix provide,
in the suggested friends and posts visible in
your Facebook feed, and the facial recognition
capabilities of your camera. And if you are an
Instart Logic customer, it is in your application
optimization. As traffic flows through the
Instart Logic system, our machine-based learning
system works in conjunction with feedback
received from the Nanovisor, our client-side web
application virtualization layer, to determine
which pieces of code are being executed, and
the similarities and differences between the
images being served and other images we have
previously seen. As the feedback from the client
is fed back to our algorithms in the cloud, they
learn how to better optimize content across
devices and users. The information obtained from
the client-cloud architecture of the Nanovisor
and the machine learning algorithms determine how
to best optimize content at run-time and deliver
content to end users without running the risk of
breaking the application.
THE APPLICATION OPTIMIZATION QUADRANT REVISITED
The application optimization quadrant below
includes traditional front end optimization (FEO)
techniques plotted along with some of Instart
Logics machine-based learning application
optimization techniques. The purple dots
represent traditional FEO techniques, while the
blue dots represent Instart Logic features. The
y-axis is the measurement of the performance
impact, the x-axis is the measurement of the
reliability of the implementation, and the size
of the dot reflects the amount of time required
for implementation (the larger the dot, the more
time consuming).
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HIGH PERFORMANCE, HIGH RELIABILITY
You may quickly notice that there are two dots
for Image Optimization. No ,this is not a
mistake. Image optimization is a set of
techniques available through traditional FEO and
also through Instart Logic that include image
transcoding and image adaptation. Image
optimization performed by Instart Logic is less
time-consuming, more reliable and scales to
millions of images as the work is being done by
computers. It provides a better performance
improvement, as each image is tuned with the best
settings for that specific image and end user. No
more leaving bytes on the table. As HTML and
JavaScript pass through the Instart Logic
service, the Nanovisor works to discover how the
code is being executed which parts of the code
are dynamic and which are static, or which
JavaScript functions are used most frequently.
The HTML content that is common across all users
is delivered first while the personalized or
dynamic content is stitched in later. This tiered
delivery improves start render times and the
user's perception that the page loaded
quickly. For JavaScript, the Nanovisor determines
which functions are used frequently and which
ones are rarely used delivering only the most
frequently-used functions first and delivering
the remaining functions on demand. Manually going
through code to prioritize what should be sent
first would be too time-consuming to be a
worthwhile optimization, but with the benefit of
machine-based learning, this can be easily
achieved.
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LOW PERFORMANCE, HIGH RELIABILITY
JavaScript interception is classified as a lower
performance optimization as it is narrower in
focus, applying only to third-party JavaScript
resources. Optimizations with a narrow focus will
have a lower performance impact. If a page only
has 1 external JavaScript resource, performance
gains would be much smaller than if there were 10
or more external JavaScript resources. JavaScript
interception allows the Instart Logic service to
intercept and re-route requests for 3rd party
content from a single source, ensuring high
reliability. When a 3rd party component is
taking an excessive amount of time to load, the
Instart Logic service will bypass it and deliver
all other content first, eliminating the chance
for users to blame your application for sluggish
behavior.
OPTIMIZATIONS IN ACTION
In the previous post, I shared some examples of
how minification and concatenation can impact
page load time and page weights. I tested the
same page with Instart Logic optimizations in
place. Applying concatenation and minification
results in an 8 improvement in start render time
from the origin. Using Instart Logic yields a 24
improvement from the origin.
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Humans are prone to error. We are influenced by
outside forces such as personal biases, and have
limited amounts of time as we need to do things
like eat and sleep. Computers can operate 24
hours a day, 7 days a week, allowing them to
process much more information than a mere mortal
can. Using machines to help us optimize
applications based on the low reliability and
high implementation costs associated with
traditional FEO techniques takes the headache
away. As end users demands and expectations
increase in terms of response times and
customizations, traditional methods of
application delivery and optimization need to be
revisited. To get pages to load in under 3
seconds for any user on any device requires
in-depth knowledge of the application and the
best possible optimization settings. This can
only be achieved by utilizing the intelligence
available from machines.
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