Memory Trace project Help

Niphadkar, Sameer
Mon Aug 29 11:08:00 GMT 2011

Hi guys, 

I hope to get your valuable inputs to this pet project of mine, please
do feel free to mention your ideas, suggestions and recommendations for
the same. 

I've collected a huge number of memory traces almost 10 GB of data.
These memory traces were gathered from a set of servers, desktops, and
laptops in a university CS Department. Each trace file contains a list
of hashes representing the contents of the machine's memory, as well as
some meta information about the running processes and OS type. 

The traces have been grouped by type and date. Traces were recorded
approximately every 30 minutes, although if machines were turned off or
away from an internet connection for a long period, no traces were
acquired. Each trace file is split into two portions. The top segment is
ASCII text containing the system meta data about operating system type
and a list of running processes. This is followed by binary data
containing the list of hashes generated for each page in the system.
Hashes are stored as consecutive 32bit values. There is a simple tool
called "traceReader" for extracting the hashes from a trace file. This
takes as an argument the file to be parsed, and will output the hash
list as a series of integer values. If you would like to compare to
traces to estimate the amount of sharing between them, you could run: 

./traceReader trace-x.dat > trace-all 
./traceReader trace-y.dat >> trace-all 
cat trace-all | sort | uniq -c 

This will tell you the number of times that each hash occurs in the

Now my idea is to take the trace for every interval (every 30 mins) for
each of the systems and find the frequency of each memory hash. I then
plan to collect the highest frequencies (hashes maximally occurring) of
the entire hour (60 mins) and then divide the memory into 'k' different
patterns based on the count of these frequencies. Like for instance say
hashes 14F430C8 ,1550068, 15AD480A, 161384B6, 16985213, 17CA274B,
18E5F038 and 1A3329 have the highest frequencies then I might divide the
memory into 8 patterns (k=8). I plan to use the Approximate Nearest
neighbor algorithm (ANN) for this
division. In ANN one needs to provide a set of query points, data points
and dimensions. I guess in my case my query points can be all the
remaining hashes other than the highest frequency ones, the data points
are all the hashes for the hour and dimension can be 1. I can thus
formulate the memory patterns for every hour, I then plan to formulate
memory patterns for every 3 hrs, 6 hrs, 12 hrs and finally all the 24
hrs. Armed with these statistics, I plan to compare the patterns based
on the time of the day. I hope to provide certain overlap with the
patterns and create what I call as "heat zones" for memory based on the
time of the day and finally come up with a suitable report for the same.

The entire objective of this project is to provide a sort of relation
between the memory page access and the interval of time of the day. So
for specific intervals there are certain memory "heat zones". I
understand that these "heat zones" might change and may not be
consistent with every system and user. The study here intends to only
establish this relationship and doesn't do any kind of qualitative or
quantitative analysis of these heat zones per system and user. The above
can be considered to be an extension of this work. 

Please feel free to comment and suggest for any new insights


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