Sarat Puthenpura

Reflection: Automated test location selection for cellular network upgrades

Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, dynamic applications and associated trafflc mixes. Network upgrades should be performed with extreme caution since millions of users heavily depend on the cellular networks for a wide range of day to day tasks, including emergency and alert notifications. Before upgrading the entire network, it is important to conduct field evaluation of upgrades. Field evaluations are typically cumbersome and can be time consuming; however if done correctly they can help alleviate a lot of the deployment issues in terms of service quality degradation. The choice and number of field test locations have significant impacts on the time-to-market as well as confidence in how well various network upgrades will work out in the rest of the network. In this paper, we propose a novel approach — Reflection to automatically determine where to conduct the upgrade field tests in order to accurately identify important features that affect the upgrade. We demonstrate the effectiveness of Reflection using extensive evaluation based on real traces collected from a major US cellular network as well as synthetic traces.

Automated Test Location Selection For Cellular Network Upgrades

Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, applications, and traffic. Network upgrades should be performed with extreme caution since millions of users heavily depend on the cellular networks. Before upgrading the entire network, it is important to conduct field evaluation of upgrades.The choice and number of field test locations have significant impact on the time-to-market and confidence in how well various network upgrades will work out in the rest of the network. We propose a novel approach -- Reflection to automatically determine where to conduct the upgrade field tests to accurately identify important features that affect the upgrade and predict for the performance of untested locations. We demonstrate its effectiveness using real traces collected from a major US cellular network as well as synthetic traces.

Approximation algorithm for optimal location of concentrators and design of Capacitated Survivable Backbone Networks

We focus on the problem of selecting concentrator locations to minimise the access network costs. This falls under the general class of Uncapacitated Facility Location (UFL) problem. We present an approximation algorithm along with some computational results. We also study the problem of designing a Capacitated Survivable Backbone Network (CSBN) with the property that there is at least one route present between all the communicating node pairs in the case of a single link failure. Mathematical formulation to efficiently solve the survivable backbone network design problem is presented. The methodologies developed are applied to a real world setting obtaining optimal or near-optimal results.