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Update to paper
Additions of UITS system information along with predictions about what the captured data will look like
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paw10003 committed Jan 17, 2015
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24 changes: 11 additions & 13 deletions TracingPaper.aux
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\bibcite{Dabir2008}{6}
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\bibcite{Skopko2012}{8}
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22 changes: 16 additions & 6 deletions TracingPaper.tex
Expand Up @@ -162,12 +162,6 @@ All comands sent over the network are coupled to an identifying MID/PID/TID/UID
\\Following these process IDs is as a way to check for intercommunication between two or more processes. In particular, we examine the compute time \& I/O (input/output) time (i.e. time spent in communication; between information arrivals). This is done by examining the inter-arrival times (IAT) between the server \& the client. This is interesting because this information will give us a realistic sense of the data transit time of the network connections being used (e.g. ethernet, firewire, fibre, etc.). Other pertinent information would be how often the client makes requests \& how often this event occurs per client process ID, identifiable by their PID/MID tuple. One could also track the amount of sharing that is occurring between users. The PID is the process identifier and the MID is the multiplex identifier, which is set by the client and is to be used for identifying groups of commands belonging to the same logical thread of operation on the client node.
\\The per client process ID can be used to map the activity of given programs, thus allowing for finer granularity in the produced benchmark (e.g. control down to process types ran by individual client levels). Other features of interest are the time between an open \& close, or how many opens/closes occurred in a window (e.g. a period of time). This information could be used as a gauge of current day trends in filesystem usage \& its consequent taxation on the surrounding network. It would also allow for greater insight on the r/w habits of users on a network along with a rough comparison between other registered events that occur on the network. Lastly, though no less important, it would allow us to look at how many occurrences there are of shared files between different users, though one must note that there is some issue (though hopefully rare) of resource locking (e.g. shared files) that needs to be taken into account. This is initially addressed by monitoring any oplock flags that are sent for read \& writes. This information also helps provide a preliminary mapping of how the network is used and what sort of traffic populates the communication.

\subsection{Run Patterns}
\label{Run Patterns}

\subsection{Locating Performance Bottlenecks}
\label{Locating Performance Bottlenecks}

%\subsection{Other (e.g. HTML)}
%\label{Other (e.g. HTML)}
%
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%\label{event_data Structure Tracking}
%The purpose of the event\_data structure is to maintain a list of the interesting information associated with each PID/MID/TID/UID tuple seen on the network. It is through this structure that the read \& write times, IATs, and even number of occurances are tracked, along with the request/response IAT pairings. In this manner each tuple has the following information tracked, and both the packet processing is performed and the meaningful data is output from the AnalysisModule code. \textit{\textbf{ADD LIST OF event\_data INFORMATION HERE}}. Although there is a large number of aspects that can be examined when dealing with all of this network information, the current focus of this paper is to examine the possible read/write commands that can occur in via SMB protcols and the IAT times of the request and response packets for these commands. \textit{\textbf{Note:}} Eventually the addition of resource locks WILL be included because it is through this information that we can gain any sort of idea as to the interaction between users/other programs with the resources on the network.

\subsection{System Information and Predictions}
\label{System Information and Predictions}
The following is an explination the UITS system from which trace1 pulls it's packet information along with predicitions of how the data will look.

The UITS system consisnts of five Microsoft file server cluster nodes. These blade servers are used to host home directories for all UConn users within the list of 88 departments. These home directories are used to provide personal drive share space to facultiy, staff and students, along with at lest one small group of users. Each server is capable of handling 1Gb of traffic in each direction (e.g. outbound and inbound traffic). All together the five blade server system can in theory handle 10Gb of recieving and tranmitting data. Some of these blade servers have local storage but the majority do not have any. To the understanding of this paper, the blade servers are purposed purely for dealing with incoming traffic to the SAN storage that sits beihnd them. This system does not currently implement load balancing, instead the servers are set up to spead the traffic load among four of the active cluster nodes while the fifth node is passive and purposed to take over in the case that any of the other nodes goes down. \\

The following are my predictions about what the data will tell me about the system. First are the predictions based on what was learned from talking to people within UITS, after that are my general predictions.

From this paper's understanding of the file server system there are spikes of traffic that tend to happen during the night time. The assumption is that the majority of this traffic will occur between 2am and 6am because this is when backups occur to the SAN system. The point of note is that, however, it is not expected that we would see any of this traffic as the protocol used is not the SMB/CIFS protocol that is being analyzed by this paper. The reasoning for this is that this traffic would be encrypted, therefore this traffic would appear as some other protocol. Further more, any traffic that does occur during the duration of "day time hours" (i.e. 9am to 5pm) would be soley due to the actions taken by the users of this system (e.g facutly, staff, students). \\
Assumptions:
\begin{itemize}
\item Some backup traffic will be seen because traffic will be generated as the data being stored using this "oneline storage" is backed up to the SAN system. Note: Any traffic past moving the data to the SAN system will \textbf{not} be seen.
\item All backup will be performed late night/early morning (e.g. 11pm-5am)
\item One general assumption is that these blade servers are "rock solid" and therefore should \textbf{not} ever go down. If this is the case then the expectation is that we should see at most a transfer rate of 8Gb since the fifth server will not be in operation. If we do find that there is a greater rate of transfer of data, then this means that the fifth server is actually helping with the traffic, not just acting as a backup in the case that any other blade server crashes or "goes down".
\end{itemize}

\subsection{Run Patterns}
\label{Run Patterns}

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