CodeStock 2012: Buffer Overflow Attack

We had a great time at CodeStock a few days ago discussing buffer overflow attacks, showing developers how they are discovered and exploited and a bit about how to avoid creating software that is vulnerable to these types of attacks. Below are the slides and video from the session:

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CodeStock 2012: You Think Your WiFi is Safe?

This past Friday I had the privilege of speaking on WiFi security at CodeStock 2012. I had a blast both preparing for the talk and delivering it and I hope it was beneficial to some of those who attended.

As promised (although a bit late), the following are the slides and video from the session:

Posted by rgillen with 15 comment(s)

External File Upload Optimizations for Windows Azure

[Cross posted from here:]

I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting.

Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards).

Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately).

We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here.

We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here.

The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post.

For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results.

Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows:


We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post.

This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here.

Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB.


(click chart for full size image)

The chart above illustrates some interesting points about the results:

  • When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size)
  • For some of the moderately-sized source files, small blocks (256KB) are best
  • As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs).
  • Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant
  • The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.


RateImprovement2 (click chart for full size image)

The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes.


This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.



What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.


Related Resources

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Time to do some digging…

I’ve been getting my test harness and reporting tools setup for some performance baselining that I’m doing relative to cloud computing providers and when I left the office on Friday I set off a test that was uploading a collection of binary files (NetCDF files if you care) to an Azure container. I was doing nothing fancy… looping through a directory, for each file found, upload to the container using the defaults for BlobBlock and then record the duration (start/finish) for that file and the file size. The source directory contained 144 files representing roughly 58 GB of data. 32 of the files were roughly 1.5 GB each and the remainder were about 92.5 MB.

I came in this morning expecting to find the script long finished with some numbers to start looking at. Instead, what I found is that, after uploading some 70 files (almost 15 GB), every subsequent upload attempt failed with a timeout error – stating that the operation couldn’t be completed in the default 90-second time window. I started doing some digging into what was happening and so far have uncovered the following:

  • By default, the Storage Client that ships with the November CTP breaks your file up into 4 MB blocks (assuming you are using BlobBlock – which you should if your file is over the 64 MB limit.
  • The client then manages 4 concurrent threads uploading the data. as each thread completes, another is started – keeping four active most the entire time.
  • At some point Saturday afternoon (just after 12 noon UTC), the client could no longer successfully upload a 4 MB file (block) in the 90 second window, and all subsequent attempts failed.
  • I initially assumed that my computer had simply tripped up or that a local networking event caused the problem so I restarted the tool – only to find every request continuing to fail.
  • I then began to wonder if the problem was the new storage client library (not sure why) so I pulled out a tool to manage  Azure storage – Cloud Storage Studio ( and noticed that I was able to successfully upload a file. I remembered that CSS (by default) splits the file into fairly small blocks, so I cracked open Fiddler and began monitoring what was going on. I learned that it was using 256 KB blocks (this is configurable via settings in the app).
  • I then adjusted my upload script to set the ServiceClient.WriteBlockSizeInBytes property (ServiceClient is a property of the CloudBlockBlob object) to 256k and re-ran the script. This time, I had no troubles at all (other than a painfully slow experience).
  • So, I can upload data (not a service outage) but while 256K blocks work, the 4 MB blocks that worked on Friday no longer work – I’m assuming that there’s a networking issue on my end, or something in the Azure platform. To provide more clarity, I adjusted the tool again, this time using a WriteBlockSizeInBytes value of 1MB and re-ran the tool – again, seeing successful uploads.


While this last step was running, I thought it might be good to go back and do some crunching on the data I had so far. The following chart represents the uploads rate from the files that successfully were uploaded on Friday/Saturday followed by the a chart showing the probability density. The mean rate was 2.74 mbits/sec with a standard deviation of 0.1968. It is interesting to note that there was no upward drift at the end of the collection of successful runs, indicating that more than likely, the “fault” was likely caused by something specific rather than being the result of a gradual shift or failure based on usage (imagine a scenario wherein as more data is populated in a container, indexes slow down, causing upload speeds to trail off).


Upload Speeds [click image for full size]


Probability Density [click image for full size]


I then ran similar reports against the data I from this morning’s runs. I’m still in the process of generating a full report on the data, but a representative sample shows the following: The mean upload rate was 0.15 mbits/sec with a standard deviation rate of 0.0375. This is over 17x slower than Friday. This data points represented below are for three batches – the first batch used a WriteBlockSizeInBytes of 256K, the second used 1MB, and the third used 2MB (10 points per size). The file upload did not succeed with the 2MB size – only finished about 1/4th of the full file.



Upload Speeds [click image for full size]


Probability Density [click image for full size]

I’ve seen a few comments from others today that indicate the slow down may be widespread – My next course of action is to attempt to run the tests from a few different locations to hopefully eliminate my local network as the problem set and have more data with which to address the issue.

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Windows Azure, Climate Data, and Microsoft Surface

I’ve been working on moving a large collection data to, from, and around Azure as we are testing the data profile for scientific computing and large-scale experiment post-processing and, in order to verify the data we uploaded and processed turned out as we wanted tit to, I built a simple visualization app that does a real-time query against the data in Azure and displays it. Originally the app was built as a simple WPF desktop application, but I got to thinking that it would be particularly interesting on the Surface and therefore took a day or two to port it over. The video below is a walkthrough of the app – the dialog is a bit cheesy but the app is interesting as it provides a very tactile means of interacting with otherwise stale data.

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Automated Chart Generation

It’s late on the Friday afternoon before Christmas week which means things are pretty quiet around the office. This quiet has the net-effect of allowing me to get quite a bit done. The last few days have been very productive with respect to our research project and Azure work (more on that coming soon) which is now in full swing. We are currently working on collecting performance data from our codes running in Azure (and soon in the Amazon cloud) and are also doing some testing of transfer speeds of data both to/from the cloud as well as between compute and storage in the cloud.

I’ve been working to automate much of this testing so we can do things in a repeatable fashion as well has have something that others could run (both other users like ourselves as well as possibly vendors should we come across something that requires a repro scenario). So far, running tests and generating data in CSV or XML format is pretty simple, but I found myself wanting to automatically generate charts/graphs of the data as part of the test process to allow a quick visualization of how the test performed. I spent a good bit of the day looking at old tools for command-line generation of charts (i.e. RDTool, etc.) and none of them were exactly what I was looking for – not to mention my proclivity to using C# and VS.NET tools and my desire to have something that looked refined/polished and not overly raw.

Thankfully, I stumbled upon something I should have remembered existed but simply hadn’t had the need to use before – the System.Windows.Forms.DataVisualization.Charting class. If you aren’t familiar with this assembly, it was released at PDC08 and has a companion Web class for performing similar operations in ASP.NET applications. In my basic testing I was able to build a console application that would ingest the CSV output from my testing harness and then generate some fairly nice looking charts based on that data. The following shows a chart (click the chart to see it full size) generated from ~1800 data points, and automatically generates a 50% band and 90% band allowing the viewer to very easily ascertain the averages and data points. This was generated using a combination of the FastPoint and BoxPlot chart types.


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“Live” Monitoring Of Your Worker Roles in Azure

[Cross-posted from here]


I’ve been working for a bit on some larger-scale jobs targeting the Windows Azure platform and early last week had assembled a collection of worker roles that were supposed to be processing my datasets for a number of days moving forward. Unfortunately, they wouldn’t stay running. As always, they “worked on my machine”, so I naturally assumed that the problem was with the Azure platform :). I then proceeded to do what I thought was the correct action… go to the Azure portal and request that the logs be transferred to my storage account so I could review them and fix the problem. What I learned, is that there were two problems with this solution:

  1. The time delay between requesting the logs and actually being able to review them is prohibitive for productive use. In my experience, the minimum turn around was 20 minutes and was most often 30 or longer. I’m not sure why this was happening – is this by design, or a temporary bug, or an artifact of the actual problem with my code, or what, but I know it was too long.
  2. Logs appear to get “lost”. In my scenario, my worker roles were throwing an exception that was un-caught my by code. Near as I can tell, when this happens, the Azure health monitoring platform assumes that the world has come to an end, shuts down that instance, and spins a new instance. While this (health monitoring and auto-recovery) is a good thing, one side effect (caveat is the fact that this is my experience and may not be reality) is that the logs were stored locally and, when the instance was shutdown/recycled, those log files went to the great bit-bucket in the sky. I was stuck in a failure mode with no visibility as to what was going wrong nor how to fix it.

After pounding my head for a bit, I came up with the following solution – trap every exception possible and use queues. The first aspect allowed my worker roles to stay running. This may not always be the right answer, but for my use case, I adapted my code to handle the error cases and trapping / suppressing all exceptions proved to be a good answer. Further, doing so allowed me to grab the error message and do something interesting with it.

The second step (using queues) solved the (my) impatience problem. I created a local method called WriteToLog that did two things: write to the regular Azure log, and write to a queue I created called status (or something similarly brilliant). I replaced all of my “RoleManager.WriteToLog()” calls with calls to the local method and I then wrote a console app that would periodically (every few seconds) pop as many messages as it could (API-limited to 32) off of the status queue, dump the data as a local csv for logging and write the data to the screen. This allowed me to drastically reduce the feedback loop between my app and me, enabling me to fix the problems quickly.

There are certainly some downsides to this approach (do queues hit a max?, what is the overhead introduced by logging to a queue, once a message is dequeued, it is not available for other clients to read, etc), but it was a nice spot fix. A better implementation would have a flag in the config file or something similar that would control the queue-logging.

As you can see from the image above, I also wrote a little winform app to display the approximate queue length so I’d have an idea of the progress and how much work remained.

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SilverLight and Paging with Azure Data

[Cross-posted from here]

If you’ve been watching by blog at all lately, you know that I’ve been playing with some larger data sets and Azure storage, specifically Azure table storage. Last week I found myself working with a SilverLight application to visualize the resulting data and display it to the user, however I did not want to use the ADO.NET Data Services client (ATOM) due to the size of data in transmission. Consequently, I set up a web role that proxied the data calls and fed them back to the caller as JSON. Due to the limitation on Azure table storage of only returning 1,000 rows at a time, I needed to access the response headers in my SilverLight client to determine after each request if there were more rows waiting… and that was the rub… every time I tried to access the response headers collection (tried both with a WebClient and HttpWebRequest), I received a System.NotImplementedException.


I pounded my head on this for a few days with no success until a helpful twitterer (@silverfighter) provided me a link that got me rolling. The root of the problem was my ignorance of how SilverLight’s networking stack functioned. As I (now) understand it, by default any networking calls (WebClient or HttpWebRequest) are actually handled by the browser and not .NET. This results in you getting access to only what the browser object hands you, which in my case, did not include the response headers.


The key here is that SilverLight 3 provides you the ability to tell the browser that you’d rather handle those requests yourself. By simply registering the http protocol (you can actually do it as granular as a site level) as handled by the Silverlight client, “magic” happens and you suddenly have access to the properties of the WebClient (ResponseHeaders) and HttpWebRequest (Response.Headers) objects that you would have expected to. The magic line you need to add prior to issuing any calls is as follows:


bool httpResult = WebRequest.RegisterPrefix("http://", WebRequestCreator.ClientHttp);


(yes… that’s it…)


The links to the appropriate articles are as follows:

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AtomPub, JSON, Azure and Large Datasets, Part 2

[Cross-posted from here]

Last Friday I posted some initial results from some simplistic testing I had done comparing pulling data from Azure via ATOM (the ADO.NET data services client) and JSON. I was surprised at the significant difference in payload and time to completion. A little later, Steve Marx questioned my methodology based on the fact that Azure storage doesn’t support JSON. Steve wasn’t being contrary, but rather pushing for clarification to the methodology of my testing as well as a desire to keep people from attempting to exploit the JSON interface of Azure storage when none exists. This post is a follow up to that one and attempts to clarify things a bit and highlight some expanded findings.


The platform I’m working against is an Azure account with a storage account hosting the data (Azure Tables), and a web role providing multiple interaction points to the data, as well as making the interaction point anonymous. Essentially, this web role serves as a “proxy” to the data and reformats it as necessary. After Steve’s question last week, I got to wondering particularly about the overhead (if any) the web role/proxy was introducing and if, esp. in the case of the ATOM data, it was drastically affecting the results. I also got to wondering if the delays I was experiencing in data transmission were, in some part, caused by the fact of having to issue 9 serial requests in order to retrieve the entire 8100 rows that satisfied my query.


To address these issues, I made the following adjustments:

  1. Tweaked my test harness for ATOM to optionally hit the storage platform directly (bypassing the proxy data service).
  2. Tweaked the data service to allow an extra query string parameter to indicate that the proxy service should make as many calls to the data service as necessary to gather the complete result set and then return the results as a single batch to the caller. This allowed me to eliminate the 1000 row limit as well as to issue only a single HTTP request from the client.
  3. I increased the test runs from 10 to 20 – still not scientifically accurate by any means, but a bit longer to provide a little better sense of the average lengths for each request batch.

The results I received as follows and not altogether different than one might expect:



As you can see from the charts above, the JSON FULL option was the fastest with an average time to completion of 14.4 seconds. When compared to the regular JSON approach, you can infer that the overhead introduced from multiple calls is roughly 4 seconds (18.55 average time to completion).


In the ATOM category, I find it interesting that the difference between the ATOM Direct (directly to the storage service) was only marginally faster (0.2 of a second on average) than the ATOM FULL approach. This would indicate that the network calls between the web role and the storage role are almost a non-factor (hinting at rather good network speeds). Remember, in the case of ATOM Full, the web role is doing the exact same thing as the test client is doing in Atom Direct, but additionally bundling the XML response into a single blob (rather than 9) and then sending it back to the client.


The following chart shows the average payload per request between the test harness and Azure. Atom Full is different then Atom and Atom Direct in that the former is all 8,100 rows whereas the later two represent a single batch of 1000. It is interesting to note that the JSON representation of all 8,100 records is only marginally larger than the ATOM representation of 1,000 records (1,326,298 bytes compared to 1,118,892 bytes).


At the end of the day, none of this is too surprising. JSON is less verbose in markup than ATOM and would logically be smaller on the wire and therefore complete sooner (although I wouldn’t have imagined it was a factor of 9 difference). What is interesting, is that the transfer of data b/t the data layer and the web role is almost trivially fast (remember, that 9 MB of XML moved between the layers and was then reformatted as JSON and shoved down back to the client in 14 seconds). It further makes you wonder what the performance improvement would/could be if Azure storage exposed a native JSON interface…

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AtomPub, JSON, Azure, and Large Datasets

UPDATE 8/20/2009, 15:29 EST: There is some confusing content in this post (i.e. Azure storage doesn’t support JSON). A follow up to this post with further explanation/detail is available here


UPDATE 8/14/2009, 17:16 EST: @smarx pointed out that this post is a bit misleading (my word) in that Azure storage doesn’t support JSON. I have a web role in place that serves the data, which, upon reflection could be introducing some time delays into the Atom feed. I will test further and update this post.


[Cross-posted from here]

I’m just really beginning to scratch the surface on my work on cloud computing and scientific computing but it seems that nearly every day I’m able to spend time on this I come away with something at least moderately novel. Today’s observation is, on reflection, a bit of a no-brainer but it wasn’t immediately obvious to me.

I’m kicking around some scientific data and have a single collection of data, with somewhere north of 40,000 subsets of data, with each subset containing roughly 8,100 rows. I’ve had an interesting time getting this data into Azure tables, but that’s not the point of this post. Once the data resided in Azure, I built a little ADO.NET client to pull the data down based on various queries (in my case, a single “subset” at a time, or 8,100 rows). In case you are wondering, the data is partitioned in Azure based on the key representing each subset, so I know that each query is only hitting a single partition. I proceeded to follow the examples for paging and data calls (checking for continuation tokens, etc.) and it wasn’t long before I had a client that would query for a particular slice of data, and then make however many individual data calls necessary until the complete result set was downloaded and ready for processing. I was, however, disappointed in the time it took to pull down a single slice of data… averages were around 55 seconds. Pulling down a number of slices of the dataset, at nearly a minute each, was a bit slow.

I spent some time poking around with Fiddler and some other tools and discovered that I was suffering from XML bloat. The “convenience” factor of having a common, easy-to-consume format was killing me. Each response coming back from the server (1000 rows) was averaging over 1MB of XML.

After a while of kicking around my options (frankly, too long), I decided to try pulling the data as JSON. I hadn’t used JSON previously, but had heard it touted as being very lightweight. I also found some nice libraries on CodePlex for de-serializing the response so I could use it as I had the results of the Atom feed. Once I made this change, I was shocked to see the amount of improvement (I expected some, but what I saw was much more than anticipated). My average time dropped to around 14 seconds for the entire batch and the average size of the response body dropped to about 163k.


I’ve included some charts below showing the results of my tests. I ran the tests 10 times for each of the approaches. Code base for each test harness is identical with the exception of the protocol-specific text handling. Time measurements are from the start of the query through the point that each response has been de-serialized into an identical .NET object (actually, a List<T> objects).


These first two charts show the time required to retrieve the entire slice of data. The unit of measure is seconds.






These charts show the average payload returned per request for the two different methods. In both cases, the unit of measurement is bytes.




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