Share |

Research Report: FARE-SHARE for mobile grid computing

Two diverging trends characterizing the mobile computing revolution. Image courtesy of Dario Pompili.

The mobile computing revolution is characterized by two clearly visible trends. The first trend is the increase in mobile devices’ computational capabilities aimed at improving user experience. The second trend is the growing popularity of the cloud computing paradigm, which pushes processing and storage (required for running applications) to remote servers on the Internet while retaining only a light front-end on the local device. The former is market- and demand-driven and is dictated primarily by users’ purchasing behavior as well as by recent innovations in human-computer interaction, while the latter can be attributed to the gains that cloud computing provides in terms of lower infrastructure costs and reduced time-to-market for innovative applications (by offering infrastructure and platform as a service). These diverging trends have rendered these powerful mobile devices heavily under-utilized.

Working with Rutgers doctoral students Hariharasudhan Viswanathan and Eun Kyung Lee, we are designing a system called FARE-SHARE, which will take advantage of those under-utilized resources. FARE-SHARE is aimed at organizing these powerful smart hand-held devices into a mobile wireless grid, so that their communication and collective computational capabilities can be harnessed to enable innovative data- and compute-intensive mobile applications. The response time, quality, and relevance of data- and compute-intensive mobile applications can be drastically improved through mobile grid computing.

Applications that will benefit from mobile grid computing include distributed rainfall and flood-risk estimation (which you can read about at this link), distributed wireless channel estimation, distributed target detection and tracking, estimation of pollution level using real-time air-quality measurements, and content-based distributed multimedia search and sharing (which you can read about here), just to name a few.

Unfortunately, the communication cost involved in enabling these applications using the conventional approach—aggregating large amounts of sensor data at a server for centralized computation, as exemplified at this link—is prohibitive. The resource capabilities of a single device may also be insufficient to process all the data and produce meaningful results in realistic time bounds.

FARE-SHARE is an efficient autonomic resource provisioning framework that addresses the major research challenges associated with mobile grid computing: namely, discovery and provisioning of computing resources. FARE-SHARE strives to minimize computational load on individual mobile devices by exploiting parallelism while incurring the minimal communication cost and, hence, energy expenditure for supporting parallelism among multiple devices.

In order to accomplish that goal, FARE-SHARE identifies two entities in a mobile grid, namely a broker and mobile devices, which may play the role of 1) consumers, which generate service requests, 2) data providers, which provide sensor data, or 3) resource providers, which provide computational, storage, and communication resources for processing the data. The broker is a remote entity whose role is to process the requests from the consumers, to determine the set of resource providers that will process the data (aided by a resource provisioning engine), and to distribute the workload among resource providers.

Example application—estimating pollution levels using real-time air-quality data.

As the roles played by the mobile devices in a mobile grid can change, FARE-SHARE relies on a credit-based system to ensure that a consumer does not gain an undue advantage by heavily consuming without providing any service. Also, such a system allows for credit transfers between multiple applications, thus widening the applicability of the framework. As there will be concurrent application requests generated by a number of consumers, FARE-SHARE will multiplex these requests and "share the fare fairly" among the consumers.

There are many potential applications of this technology; at the moment we are working on two demonstrations. The first is a healthcare application, and the second is for distributed communication channel estimation; we anticipate that these will be ready in one to two months.

At present, several papers and grant proposals related to FARE-SHARE are under review or pending. The papers will either appear in peer-reviewed journals or be presented at conferences.

Your rating: None Average: 4.8 (36 votes)


Post new comment

By submitting this form, you accept the Mollom privacy policy.