Cloud Computing at the Tactical Edge

Cloud Computing Add comments

By Grace Lewis,
Senior Member of the Technical Staff
Research Technology & System Solutions

Grace Lewis Cloudlets, which are lightweight servers running one or more virtual machines (VMs), allow soldiers in the field to offload resource-consumptive and battery-draining computations from their handheld devices to nearby cloudlets. This architecture decreases latency by using a single-hop network and potentially lowers battery consumption by using WiFi instead of broadband wireless. This posting extends our original post by describing how we are using cloudlets to help soldiers perform various mission capabilities more effectively, including facial, speech, and imaging recognition, as well as decision making and mission planning.

An initial goal of our research was to create a prototype application that located cloudlets within close proximity of handheld devices using them. We initially focused on offloading computations to cloudlets to extend device battery life. In addition to this benefit, we also found cloudlets significantly reduce the amount of time needed to deploy applications to handheld devices because clients are not tied to a specific server that can take a long time to provision in tactical environments.

Our work together with Mahadev “Satya” Satyanarayanan (the creator of the cloudlet concept and a faculty member at Carnegie Mellon's School of Computer Science) originally focused on face recognition applications as an example of a computation-intensive mission capability. Thus far we have created an Android-based facial recognition application that

  • locates a cloudlet via a discovery protocol,
  • sends the application overlay to the cloudlet, where dynamic VM synthesis is performed,
  • captures the images and sends them to the facial recognition server code that now resides in the cloudlet.

In the context of cloudlets, the application overlay corresponds to the computation-intensive code invoked by the client, which in this case is the face recognition server written in C++ and processes images from a handheld device client for training or recognition purposes. On execution, the overlay is sent to the cloudlet and applied to one of the VMs running in the cloudlet, which is called dynamic VM synthesis. The application overlay is pre-generated by calculating the difference between a base VM and the base VM with the computation-intensive code installed.

The first version of the cloudlet we created is a simple HTTP server.  When this server receives the application overlay from the client it decrypts and decompresses the overlay and performs VM synthesis to configure the cloudlet dynamically. It subsequently returns coordinates for the faces it recognizes, as along with a measure of confidence to the client device.

Constructing the Cloudlet Prototype

The original cloudlet prototype built by Satya’s team used a simple Virtual Network Computer (VNC) client,  to see what was executing inside the VM. Our cloudlet prototype extended Satya’s work to use a thick mobile client that provides a better user experience for users at the edge and allows incorporation of sensor information that would not be possible with the original VNC cloudlet approach.  We constructed this prototype in the RTSS Concept lab.

Our design was tricky because the face recognition client needs to know the IP address and the port on which the face recognition server is listening so that it can connect to it. The client uses an HTTP request to start the cloudlet setup expects an HTTP response from the cloudlet server that includes the face recognition server IP address and port. Since the IP address is assigned by the DHCP server because the VM is executing in bridged mode, however, the host server has no visibility into that assignment, so there was no simple way to obtain the IP address and port.

To solve this problem, we included a Windows service in the VM and run on startup. The Windows service invokes a Python script that performs the following three tasks:

  1. start the face recognition server executable in a separate thread inside a Python script,
  2. read the face recognition server configuration file that contains the IP address and port that the face recognition server is listening on, and
  3. write this information to a file that is accessible by the cloudlet

Although the Windows service creates additional complexity on the cloudlet server, it reduces the complexity cloudlet setup in the field. During field operation, servers residing within Tactical Operation Center (TOCs) and Humvees are provisioned with a set of pre-packaged cloudlets to support a range of applications and versions to avoid provisioning servers for each supported application platform and version. The handheld devices of soldiers participating in the mission are then loaded with application overlays that are necessary for a particular mission. A soldier running a computation-expensive application can discover a compatible cloudlet within minutes and offload the expensive computation to the cloudlet running on a server.

What We’ve Learned

Our research has identified the following two types of applications that can be deployed in a cloudlet setting:

  • Data-source-reliant applications that rely on a particular data source to work. For example, if soldiers need to launch the facial recognition application, they need a database of faces to match images against. Another example would be if the soldier wanted to compare fingerprints and needed a database of fingerprints to match against. In this setting the cloudlet must be configured to connect the cloudlet to a particular data source.
  • Non-data-source-reliant applications that are computationally intensive but don’t require a large data source to work. For example, imagine soldiers encountering a sign with characters they don’t understand. They can take a picture of the sign and submit it to a cloudlet to determine the language in which the sign is written. In this case the computationally-intensive code residing on the cloudlet relies on complex character recognition algorithms instead of a large database.

As expected, our experiments demonstrated that the size of the overlay increases overlay transmission time (which in turn consumes more battery) as well as VM synthesis time. If the data source is included inside the overlay this would create a large overlay, which indicates that the cloudlet concept is better fit for non-data-source-reliant applications. We overcame this problem by specifying the location of the data source in a configuration file. The location could be the local server or a server accessible over a network or the Internet. Although this approach requires additional configuration, it is only done once (when the cloudlet is packaged by IT experts), rather than doing it each time a server is configured in the field (potentially by non-IT experts).

Future Work

When testing the cloudlet prototype in the RTSS Concept Lab, we discovered that a reduced deployment time makes it easier to deploy an application in a tactical environment. We are working to capture those measurements and are developing the following applications to support our findings

  • fingerprint recognition — fingerprints are captured using a fingerprint scanner connected to a handheld device and sent to the cloudlet for processing,
  • character recognition — pictures of a written sign are taken with a camera on the handheld device and sent to the cloudlet for character identification and translation,
  • speech recognition — voice of a person speaking a foreign language is captured using the voice recorder on the handheld device and sent to the cloudlet for translation; the same application can be used to translate a response back to the  identified foreign language, and
  • model checking —An app is generated on the handheld on-the-fly using end-user programming capabilities and sent to a model checker in a cloudlet to ensure it does not violate any security (or other) policies and constraints.

We will use these new applications to gather measurements related to bandwidth consumption of overlay transfer and VM synthesis to focus on optimization of cloudlet setup time.

Our future research and collaboration will position cloudlets to both reduce battery consumption and simplify application deployment in the field. For example, our goal is to use dynamic VM synthesis to slash the time needed to deploy applications, thereby shielding operators from unnecessary technical details, while also communicating and responding to mission-critical information at an accelerated operational tempo.

Additional Resources:

This is the second post in a series exploring the  SEI’s research in Cloud Computing in partnership with Satya. To read the initial post, Cloud Computing for the Battlefield, please visit
http://blog.sei.cmu.edu/post.cfm/cloud-computing-for-the-battlefield.

Share this

Share on Facebook Send to your Twitter page  Save to del.ico.us  Save to LinkedIn  Digg this  Stumble this page.  Save this page on your Google Home Page 

1 response to “Cloud Computing at the Tactical Edge”

  1. Daniel Thomas Says:
    Brilliant article. When reading I was wondering how this technology could move into a commercial environment?

    For instance with tighter banking regulations in the EU and the need for 'silos' within banking departments (referred to as Chinese walls) could the "cloudlets" deliver computational processing whilst allowing trades and treasury applications to be kept very lightweight.

    This could almost be an extension of traditional thin client technologies.

    For third world countries with a lack of telecommunications infrastructure this could possibly offer a way to roll-out vastly cheaper networks, and create Cloud Computing communities.

    Thanks for a great post!

Add Comment


Leave this field empty: