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Cellular service providers continuously upgrade their network software on base stations to introduce new service features, fix software bugs, enhance quality of experience to users, or patch security vulnerabilities. A software upgrade typically requires the network element to be taken out of service, which can potentially degrade the service to users. Thus, the new software is deployed across the network using a rolling upgrade model such that the service impact during the roll-out is minimized. A sequential roll-out guarantees minimal impact but increases the deployment time thereby incurring a significant human cost and time in monitoring the upgrade. A network-wide concurrent roll-out guarantees minimal deployment time but can result in a significant service impact. The goal is to strike a balance between deployment time and service impact during the upgrade. In this paper, we first present our findings from analyzing upgrades in operational networks and discussions with network operators and exposing the challenges in rolling software upgrades. We propose a new framework Concord to effectively coordinate software upgrades across the network that balances the deployment time and service impact. We evaluate Concord using real-world data collected from a large operational cellular network and demonstrate the benefits and tradeoffs. We also present a prototype deployment of Concord using a small-scale LTE testbed deployed indoors in a corporate building. 

Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, dynamic applications and associated traffic 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.