-----Original Message----- From: herron.philip@xxxxxxxxxxxxxx [mailto:herron.philip@xxxxxxxxxxxxxx] On Behalf Of Philip Herron Sent: Monday, August 29, 2011 9:58 PM To: Niphadkar, Sameer Subject: Re: Memory Trace project Help On 29 August 2011 12:07, Niphadkar, Sameer <Sameer.Niphadkar@xxxxxxxxxxxx> wrote: > 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 > system. > > 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) http://www.cs.umd.edu/~mount/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 Maybe i missed something but i am not sure why you posted this to gcc-help. And i not quite sure what the reason behind doing this really is. If your looking at what pages are being accessed at different intervals during the day are you looking up specific addresses or looking for specific data within these? Are you in the end looking to find something like: computers x through z run program A generally at this time interval but computers f through i run program B at the same interval. but then you can just look up process list. Or is it your looking for specific addresses in memory which are being accessed more frequently. But this will change from computer to computer. And even if you say find with operating system A you find that these addresses are being most frequently accessed can you really make any assumptions about it I'm sorry to have posted it to the wrong list - I intended to post this message to the gcc-developer list as I believe it to have some good experts on memory analytics and management. As pointed out there are two aspects to this project. 1. Find out about the processes running most frequently at a particular time interval on different systems (this may be an easier option) 2. Go deeper to the physical memory(PM) trace and find the relationship between the PM addresses and most frequent access per universal time clock per system. I understand that with address space randomized mappings and with different systems running different processes it might be very hard to find any suitable pattern emerging from this study. But as most of us know that identical systems belonging in a particular network and during a time frame might end up accessing similar PM blocks. (A block here being groups of pages) I intend to find if there is any kind of correlation between this time frame and the access. According to the working set model of a system, there exits a temporal and spatial locality of memory page access and hence we end up using the appropriate page replacement algorithms. Now I intent to see if this same analogy can be applied to the entire memory address space for access. I mean if there exists some sort of a pattern emerging for physical memory access based on time and space. I hope to know if there has been any similar work done before with memory traces or if there are any other areas which I need to look into before I can begin this study.
