Showing papers by "Jia Wang published in 2016"

Characterizing and optimizing cellular network performance during crowded events

Abstract: During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For an effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step toward this end by characterizing the operational performance of a tier-1 cellular network in the U.S. during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 s shorter radio resource control timeouts as compared with routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events, and opportunistic connection sharing can reduce connection failures by 95% when employed by a small number of devices in each cell sector.

Detecting and localizing end-to-end performance degradation for cellular data services

Abstract: Providing high end-to-end (E2E) performance is critical for cellular service providers to best serve their customers. Detecting and localizing E2E performance degradation is crucial for cellular service providers, content providers, device manufactures, and application developers to jointly troubleshoot root causes. To the best of our knowledge, detection and localization of E2E performance degradation at cellular service providers has not been previously studied. In this paper, we propose a holistic approach to detecting and localizing E2E performance degradation at cellular service providers across the four dimensions of user locations, content providers, device types, and application types. First, we use training data to build models that can capture the normal performance of every E2E-instance, which means flows corresponding to a specific location, content provider, device type, and application type. Second, we use our models to detect performance degradation for each E2E-instance on an hourly basis. Third, after each E2E-instance has been labeled as non-degrading or degrading, we use association rule mining techniques to localize the source of performance degradation. Our system detected performance degradation instances over a period of one week. In 80% of the detected degraded instances, content providers, device types, and application types were the only factors of performance degradation.

Libra: Impact assessment of cellular load balancing

Abstract: Load on cellular towers is one of the key metrics that cellular service providers monitor as part of their operational and management tasks. Increased load on the towers can lead to congestion, which in turn can severely degrade quality of service perceived by users. Hence, it is of great interest to cellular service providers to minimize the maximum load at cell towers, and thereby minimize chances of congestion in the event of a sudden increase in load due to user demand changes. This goal can be achieved by proactive load balancing among neighboring cell towers, i.e., proactively identify opportunities to balance the load through re-binding of users from heavily loaded cell towers to lightly loaded neighboring towers. In this paper, we propose a new tool Libra to effectively assess the impact of load balancing related parameter changes. Libra provides an objective measure of the degree of load imbalance across multiple network locations and identifies if the measure improves or degrades after parameter changes. Our evaluation of Libra using real-world data collected from a large cellular provider demonstrates its effectiveness in accurately capturing the degree of imbalance at multiple cell towers.