Cloud Computing for the Battlefield
The Department of Defense (DoD) is increasingly interested in having soldiers carry handheld mobile computing devicesto support their mission needs. Soldiers can use handheld devices to help with various tasks, such as speech and image recognition, natural language processing, decision-making and mission planning. Three challenges, however, present obstacles to achieving these capabilities. The first challenge is that mobile devices offer less computational power than a conventional desktop or server computer. A second challenge is that computation-intensive tasks, such as image recognition or even global positioning system (GPS), take a heavy toll on battery power. The third challenge is dealing with unreliable networks and bandwidth. This post explores our research to overcome these challenges by using cloudlets, which are localized, lightweight servers running one or more virtual machines (VMs) on which soldiers can offload expensive computations from their handheld mobile devices, thereby providing greater processing capacity and helping conserve battery power.
This leverage of external resources to augment the capabilities of resource-limited mobile devices is a technique commonly known as cyber-foraging. The use of VM technology provides greater flexibility in the type and platform of applications and also reduces setup and administration time, which is critical for systems at the tactical edge. The term tactical edge refers to systems used by soldiers or first responders that are close to a mission or emergency executing in environments characterized by limited resources in terms of computation, power and network bandwidth, as well as changes in the status of the mission or emergency.
Cloudlets are located within proximity of handheld devices that use them, thereby decreasing latency by using a single-hop network and potentially lowering battery consumption by using WiFi instead of broadband wireless which consumes more energy. For example, a cloudlet might run in a Tactical Operations Center (TOC) or a Humvee. From a security perspective, cloudlets can use WiFi networks to take advantage of existing security policies, including access from only specific handheld devices and encryption techniques.
Related work on offloading computation to conserve battery power in mobile devices relies on the conventional Internet or environments that tightly couple applications running on handheld devices and servers on which computations are offloaded. In contrast, cloudlets decouple mobile applications from the servers. Each mobile app has a client portion and an application overlay corresponding to the computation-intensive code invoked by the client. On execution, the overlay is sent to the cloudlet and applied to one of the virtual machines 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 only coupling that exists between the mobile app and the cloudlet is that the same version of the VM software on which the overlay was created must be used. Since no application-specific software is installed on the server, there is no need to synchronize release cycles between the client and server portions of apps, which simplifies the deployment and configuration management of apps in the field.
Dynamic VM synthesis is particularly useful in tactical environments characterized by unreliable networks and bandwidth, unplanned loss of cyber foraging platforms, and a need for rapid deployment. For example, imagine a scenario where a soldier needs to execute a computation-intensive app configured to work with cloudlets. At runtime, the app discovers a nearby cloudlet located on a Humvee and offloads the computation-intensive portion of code to it. Due to enemy attacks, network connectivity, or exhaustion of energy sources on the cloudlet, however, the mobile app is disconnected from the cloudlet. The mobile app can then locate a different cloudlet (e.g., in a TOC) and--due to dynamic VM synthesis--can have the app running in a short amount of time, with no need for any configuration on the app or the cloudlet. This flexibility enables the use of whatever resources become opportunistically available, as well as replacement of lost cyber-foraging resources and dynamic customization of newly-acquired cyber foraging resources.
As part of our research, we are focusing on face recognition applications. Thus far we have created an Android-based facial recognition app that performs the following actions:
- It locates a cloudlet via a discovery protocol
- It sends the application overlay to the cloudlet where dynamic VM synthesis is performed.
- It captures images and sends them to the facial recognition server code that now resides in the cloudlet.
- The application overlay is a facial recognition server written in C++ that processes images from a client for training or recognition purposes. When in recognition mode, it returns coordinates for the faces it recognizes, as well as a measure of confidence. The first version of the cloudlet is a simple HTTP server that receives the application overlay from the client, decrypts the overlay, decompresses the overlay, and performs VM synthesis to dynamically set up the cloudlet.
The first phase of our work has focused on creating the cloudlet prototype described above. In the second phase we will conduct measurements to see if computations in a cloudlet provide significant reductions in device battery power. In addition, we will gather measurements related to bandwidth consumption of overlay transfer and VM synthesis to focus on optimization of cloudlet setup time. Assuming we are successful, our third phase will create a cloudlet in the RTSS Concept Lab to explore other ways to take computation to the tactical edge.
As part of our research, we are collaborating with Mahadev Satyanarayanan, the creator of the cloudlet concept and a faculty member at Carnegie Mellon University's School of Computer Science. We will be blogging about the progress of our research in future posts.
To read more about the cloud computing research conducted by the SEI's System of Systems team, please visit
To view an SEI Webinar on cloud computing, please visit
More By The Author
Software Engineering for Machine Learning: Characterizing and Detecting Mismatch in Machine-Learning Systems
More In Cloud Computing
Deep Learning, Agile-DevOps, and Cloud Security: The Top 10 Blog Posts of 2018
This post has been shared 0 times.
Get updates on our latest work.
Sign up to have the latest post sent to your inbox weekly.