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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 postby 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
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:
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:
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).
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
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.
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
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