The Departments of Computer Science (CS) and Applied Mathematics and Statistics (AMS) are pleased to announce the award of $449k in funding through the National Science Foundation’s (NSF) NeTS program.

Even for the personal smartphone or home computer user there is no avoiding the use of cloud computing. Cloud computing is low in cost, easily available, and offers access to useful services that would otherwise be out of reach. Services such as Netflix, Amazon Fire, and Expedia are only some of the popular online services being hosted on the cloud. On the backend, dynamic applications in the cloud are more lucrative if their deployments grow through dynamic capacity provisioning. Software deployments must be carefully provisioned to meet their performance requirements without wasting resources.

Most resource provisioning solutions today employ predictions to estimate demand and provide resources, accordingly. At times, this process could be fraught with errors. With a recent NSF Networking Technology and Systems (NeTS) award, researchers Anshul Gandhi and Zhenhua Liu, seek to bridge the gap between predictors and provisioning solutions.

​The goal of their NeTS project, Demystifying the Role of Prediction Models: Bridging Prediction Algorithms and Resource Provisioning, is to develop and leverage error models to fully realize the potential of predictors. According to Gandhi, “Our research will allow businesses to maximize resource utilization despite prediction errors.” Gandhi and Liu will investigate the prediction error model which includes constructing models that capture the structure of prediction errors; developing an algorithmic framework; and designing systems to exploit the new prediction error-aware algorithms.

Joseph Mitchell, chair of the AMS department, described Gandhi and Liu’s work as “embarking on research at the cutting edge of cloud computing, addressing the critical need to deploy scarce resources intelligently, particularly in light of the considerable uncertainty that comes with prediction models.”

Mitchell added, “By explicitly taking into account the prediction errors, using methods from applied probability, statistics, and mathematical modeling, they will develop improved algorithms for resource allocation in cloud computing. This is a compelling project that requires collective expertise from computer science, optimization, probability, and statistics, and represents an ideal collaboration between Computer Science and Applied Mathematics and Statistics, both in research and in educational impact.”

Gandhi and Liu will realize additional benefits of their research through technology transfer opportunities with industrial partners.

NSF identified this NeTS project as “transformative research” related to fundamental scientific and technological advances in networking as well as systems research. NSF funds projects such as these in the hope that it will lead to “the development of future-generation, high-performance networks and future internet architectures”.