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.

Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.

Recent studies have shown that web browsing is one of the most prominent cellular applications. It is therefore important for cellular network operators to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' web browsing Quality-of-Experience (web QoE). Understanding the relationship between web QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact web QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure web QoE metrics, such as abandonment rate and session length. In this paper, we first devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. We then present a large-scale study characterizing the impact of network characteristics on web QoE using a month-long anonymized dataset collected from a major cellular network provider. Our results show that improving signal-to-noise ratio, decreasing load and reducing handovers can improve user experience. We find that web QoE is very sensitive to inter-radio-access-technology (IRAT) handovers. We further find that higher radio data link rate does not necessarily lead to better web QoE. Since many network characteristics are interrelated, we also use machine learning to accurately model the influence of radio network characteristics on user experience metrics. This model can be used by cellular network operators to prioritize the improvement of network factors that most influence web QoE.

Mobile network operators have a significant interest in the performance of streaming video on their networks because network dynamics directly influence the Quality of Experience (QoE). However, unlike video service providers, network operators are not privy to the client- or server-side logs typically used to measure key video performance metrics, such as user engagement. To address this limitation, this paper presents the first large-scale study characterizing the impact of cellular network performance on mobile video user engagement from the perspective of a network operator. Our study on a month-long anonymized data set from a major cellular network makes two main contributions. First, we quantify the effect that 31 different network factors have on user behavior in mobile video. Our results provide network operators direct guidance on how to improve user engagement --- for example, improving mean signal-to-interference ratio by 1 dB reduces the likelihood of video abandonment by 2%. Second, we model the complex relationships between these factors and video abandonment, enabling operators to monitor mobile video user engagement in real-time. Our model can predict whether a user completely downloads a video with more than 87% accuracy by observing only the initial 10 seconds of video streaming sessions. Moreover, our model achieves significantly better accuracy than prior models that require client- or server-side logs, yet we only use standard radio network statistics and/or TCP/IP headers available to network operators.

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 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 towards this end by characterizing the operational performance of a tier-1 cellular network in the United States 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 seconds shorter RRC timeouts as compared to 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.

Cellular network-based machine-to-machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it to traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the US. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance. Our experimental results show that M2M traffic exhibits significantly different patterns than smartphone traffic in multiple aspects. For instance, M2M devices have a much larger ratio of uplink-to-downlink traffic volume, their traffic typically exhibits different diurnal patterns, they are more likely to generate synchronized traffic resulting in bursty aggregate traffic volumes, and are less mobile compared to smartphones. On the other hand, we also find that M2M devices are generally competing with smartphones for network resources in co-located geographical regions. These and other findings suggest that better protocol design, more careful spectrum allocation, and modified pricing schemes may be needed to accommodate the rise of M2M devices.

Network service providers, and other parties, require an accurate understanding of the performance cellular networks deliver to users. In particular, they often seek a measure of the network performance users experience solely when they are interacting with their device---a measure we call in-context. Acquiring such measures is challenging due to the many factors, including time and physical context, that influence cellular network performance. This paper makes two contributions. First, we conduct a large scale measurement study, based on data collected from a large cellular provider and from hundreds of controlled experiments, to shed light on the issues underlying in-context measurements. Our novel observations show that measurements must be conducted on devices which (i) recently used the network as a result of user interaction with the device, (ii) remain in the same macro-environment (e.g., indoors and stationary), and in some cases the same micro-environment (e.g., in the user's hand), during the period between normal usage and a subsequent measurement, and (iii) are currently sending/ receiving little or no user-generated traffic. Second, we design and deploy a prototype active measurement service for Android phones based on these key insights. Our analysis of 1650 measurements gathered from 12 volunteer devices shows that the system is able to obtain average throughput measurements that accurately quantify the performance experienced during times of active device and network usage.

Cellular network operators have a compelling interest to monitor HTTP transaction latency because it is an important component of the user experience. Existing techniques to monitor latency require active probing or use passive analysis to estimate round trip time (RTT). Unfortunately, it is impractical to use active probing to monitor entire cellular networks, and RTT is only one component of HTTP latency in cellular networks. This paper presents a new passive technique to estimate HTTP transaction latency that overcomes the scaling and completeness limitations of prior approaches. We validate our technique in an operational cellular network and present results for traffic in the wild.

Recent studies on cellular network measurement have provided the evidence that significant geospatial correlations, in terms of traffic volume and application access, exist in cellular network usage. Such geospatial correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained characterization of the geospatial dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings.

Cellular network based Machine-to-Machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it with traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the United States. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance.

Smartphone users are increasingly shifting to using apps as "gateways" to Internet services rather than traditional web browsers. App marketplaces for iOS, Android, and Windows Phone platforms have made it attractive for developers to deploy apps and easy for users to discover and start using many network-enabled apps quickly. For example, it was recently reported that the iOS AppStore has more than 350K apps and more than 10 billion downloads. Furthermore, the appearance of tablets and mobile devices with other form factors, which also use these marketplaces, has increased the diversity in apps and their user population. Despite the increasing importance of apps as gateways to network services, we have a much sparser understanding of how, where, and when they are used compared to traditional web services, particularly at scale. This paper takes a first step in addressing this knowledge gap by presenting results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. We identify traffic from distinct marketplace apps based on HTTP signatures and present aggregate results on their spatial and temporal prevalence, locality, and correlation.

The end to end system data performance over a 3G cellular network depends on many factors such as the number of users, interference, multipath propagation, radio resource management techniques as well as the interaction between these mechanisms and the transport protocol's flow and congestion mechanisms. Using controlled experiments in a public cell site, we investigate the interaction between TCP and the 3G UMTS/HSPA network's resource allocation, and its effect on fairness in the throughput achieved across multiple (up to 26) TCP flows in a loaded cell sector. Our field measurement results indicate that TCP fairness fluctuates significantly when the air interface (radio link) is the bottleneck. We also observe that TCP fairness is substantially better when the backhaul link (a fixed wired link) is the bottleneck, instead of the air interface. We speculate that the fairness of TCP flows is adversely impacted by the mismatch between the resource allocation mechanisms of TCP's flow and congestion control and that of the Radio Access Network (RAN).

Operators of 3G data networks need to distinguish the performance of each geographic area in their 3G networks to detect and resolve local network problems. This is because the quality of the last mile radio link between 3G base stations and end-user devices is a crucial factor in the end-to-end performance that each user experiences. It is relatively straightforward to measure the performance of all IP traffic in the 3G network from a small number of vantage points in the core network. However, the location information available about each mobile device (e.g., the cell sector/site that it is in) is often too stale to be accurate because of user mobility. Moreover, very costly infrastructure deployment and maintenance of custom equipment would be required to collect fine-grained location information about all mobile devices on an on-going basis in large 3G networks. Thus, it is a challenge to accurately assign IP performance measurements to fine-grained geographic regions of the 3G network using existing standard network components. Fortunately, previous studies have observed that human mobility patterns are very predictable. In this paper, we exploit this predictability to develop a novel clustering algorithm grouping related cell sectors that accurately assigns IP performance measurements to fine-grained geographic regions. We present results from a prototype in a real 3G network that shows our approach provides more accurate performance localization than existing approaches. Eventually, we can either narrow down individual IP performance measurements into only 4 candidate cell sectors consistently with the accuracy of 70% over one week based on a one-day snapshot of fine-grained 3GPP events, or increase the accuracy 20% comparing with site-level accuracy through lightweight handover statistics hourly collected at RNCs. Using our approach, we improve anomaly detection based on IP performance measurements by reducing the number of false positives and false negatives. Our study also sheds light on the mobility patterns of 3G devices.

How fast is the network? The speed at which real users can download content at different locations and at different times is an important metric for service providers. Knowledge of this speed helps determine where to provision more capacity and helps detect network problems. However, most network-level estimates of these speeds today are obtained using active "speed tests" that place substantial load on the network and are not necessarily representative of actual user experiences due to limited vantage points. These problems are exacerbated in wireless networks where the physical locations of users play an important role in performance. To redress these problems, this paper presents a new technique to estimate achievable download speed using only flow records collected passively. Estimating achievable speed passively is non-trivial because the measured throughput of real flows is often not comparable to the achievable steady-state TCP rate. This can be because, for example, flows are small and never exit TCP slow start or are rate-limited by the content-provider. Our technique addresses these issues by constructing a ThroughputIndex, a list of flow types that accurately estimate achievable speed. We show that our technique estimates achievable speed more accurately than other techniques in a large 3G wireless network.

Wi-Fi clients can obtain much better performance at some commercial hot spots than others. Unfortunately, there is currently no way for users to determine which hot spot access points (APs) will be sufficient to run their applications before purchasing access. To address this problem, this paper presents Wifi-Reports, a collaborative service that provides Wi-Fi clients with historical information about AP performance and application support. The key research challenge in Wifi-Reports is to obtain accurate user-submitted reports. This is challenging because two conflicting goals must be addressed in a practical system: preserving the privacy of users' reports and limiting fraudulent reports. We introduce a practical cryptographic protocol that achieves both goals, and address the important engineering challenges in building Wifi-Reports. Using a measurement study of APs in a busy commercial district, we show that Wifi-Reports would improve the performance over previous AP selection approaches in 30-60 percent of locations.

Wi-Fi clients can obtain much better performance at some commercial hotspots than at others. Unfortunately, there is currently no way for users to determine which hotspot access points (APs) will be sufficient to run their applications before purchasing access. To address this problem, this paper presents Wifi-Reports, a collaborative service that provides Wi-Fi clients with historical information about AP performance and application support. The key research challenge in Wifi-Reports is to obtain accurate user-submitted reports. This is challenging because two conflicting goals must be addressed in a practical system: preserving the privacy of users' reports and limiting fraudulent reports. We introduce a practical cryptographic protocol that achieves both goals, and we address the important engineering challenges in building Wifi-Reports. Using a measurement study of commercial APs in Seattle, we show that Wifi-Reports would improve performance over previous AP selection approaches in 30%-60% of locations.

We present the design and evaluation of an 802.11-like wireless link layer protocol that obfuscates all transmitted bits to increase privacy. This includes explicit identifiers such as MAC addresses, the contents of management messages, and other protocol fields that the existing 802.11 protocol relies on to be sent in the clear. By obscuring these fields, we greatly increase the difficulty of identifying or profiling users from their transmissions in ways that are otherwise straightforward. Our design, called SlyFi, is nearly as efficient as existing schemes such as WPA for discovery, link setup, and data delivery despite its heightened protections; transmission requires only symmetric key encryption and reception requires a table lookup followed by symmetric key decryption. Experiments using our implementation on Atheros 802.11 drivers show that SlyFi can discover and associate with networks faster than 802.11 using WPA-PSK. The overhead SlyFi introduces in packet delivery is only slightly higher than that added by WPA-CCMP encryption (10% vs. 3% decrease in throughput).

Without well-provisioned dedicated servers, modern fast-paced action games limit the number of players who can interact simultaneously to 16-32. This is because interacting players must frequently exchange state updates, and high player counts would exceed the bandwidth available to participating machines. In this paper, we describe Donnybrook, a system that enables epic-scale battles without dedicated server resources, even in a fast-paced game with tight latency bounds. It achieves this scalability through two novel components. First, it reduces bandwidth demand by estimating what players are paying attention to, thereby enabling it to reduce the frequency of sending less important state updates. Second, it overcomes resource and interest heterogeneity by disseminating updates via a multicast system designed for the special requirements of games: that they have multiple sources, are latency-sensitive, and have frequent group membership changes. We present user study results using a prototype implementation based on Quake III that show our approach provides a desirable user experience. We also present simulation results that demonstrate Donnybrook's efficacy in enabling battles of up to 900 players.

Existing DHT-based file systems use consistent hashing to assign file blocks to random machines. As a result, a user task accessing an entire file or multiple files needs to retrieve blocks from many different machines. This paper demonstrates that significant availability and performance gains can be achieved if instead, users are able to retrieve all the data needed for a given task from only a few DHT nodes. We explore the design and implications of such a "defragmented" DHT-based distributed file system, called D2, that also maintains important DHT properties like storage load balance. We show using real-world file system traces that a simple key encoding scheme is sufficient to maintain good defragmentation for most user tasks. Using both simulation and an actual 1,000 node deployment, we show that D2 increases availability by over an order of magnitude and improves user-perceived latency by 30- 100% compared to a traditional design.

The ubiquity of 802.11 devices and networks enables anyone to track our every move with alarming ease. Each 802.11 device transmits a globally unique and persistent MAC address and thus is trivially identifiable. In response, recent research has proposed replacing such identifiers with pseudonyms (i.e., temporary, unlinkable names). In this paper, we demonstrate that pseudonyms are insufficient to prevent tracking of 802.11 devices because implicit identifiers, or identifying characteristics of 802.11 traffic, can identify many users with high accuracy. For example, even without unique names and addresses, we estimate that an adversary can identify 64 % of users with 90 % accuracy when they spend a day at a busy hot spot. We present an automated procedure based on four previously unrecognized implicit identifiers that can identify users in three real 802.11 traces even when pseudonyms and encryption are employed. We find that the majority of users can be identified using our techniques, but our ability to identify users is not uniform; some users are not easily identifiable. Nonetheless, we show that even a single implicit identifier is sufficient to distinguish many users. Therefore, we argue that design considerations beyond eliminating explicit identifiers (i.e., unique names and addresses), must be addressed in order to prevent user tracking in wireless networks. Categories and Subject Descriptors:

For the last few years, large Web content providers interested in improving their scalability and availability have increasingly turned to three techniques: mirroring, content distribution, and ISP multihoming. The Domain Name System (DNS) has gained a prominent role in the way each of these techniques directs client requests to achieve the goals of scalability and availability. The DNS is thought to offer the transparent and agile control necessary to react quickly to ISP link failures or phenomenon such as flash crowds.

The Domain Name System (DNS) is a critical part of the Internet's infrastructure, and is one of the few examples of a robust, highlyscalable, and operational distributed system. Although a few studies have been devoted to characterizing its properties, such as its workload and the stability of the top-level servers, many key components of DNS have not yet been examined. Based on large-scale measurements taken from servers in a large content distribution network, we present a detailed study of key characteristics of the DNS infrastructure, such as load distribution, availability, and deployment patterns of DNS servers. Our analysis includes both local DNS servers and servers in the authoritative hierarchy. We find that (1) the vast majority of users use a small fraction of deployed name servers, (2) the availability of most name servers is high, and (3) there exists a larger degree of diversity in local DNS server deployment and usage than for authoritative servers. Furthermore, we use our DNS measurements to draw conclusions about federated infrastructures in general. We evaluate and discuss the impact of federated deployment models on future systems, such as Distributed Hash Tables.

The limitations of BGP routing in the Internet are often blamed for poor end-to-end performance and prolonged connectivity interruptions. Recent work advocates using overlays to effectively bypass BGP's path selection in order to improve performance and fault tolerance. In this paper, we explore the possibility that intelligent control of BGP routes, coupled with ISP multihoming, can provide competitive end-to-end performance and reliability. Using extensive measurements of paths between nodes in a large content distribution network, we compare the relative benefits of overlay routing and multihoming route control in terms of round-trip latency, TCP connection throughput, and path availability. We observe that the performance achieved by route control together with multihoming to three ISPs (3-multihoming), is within 5-15% of overlay routing employed in conjunction 3-multihoming, in terms of both end-to-end RTT and throughput. We also show that while multihoming cannot offer the nearly perfect resilience of overlays, it can eliminate almost all failures experienced by a singly-homed end-network. Our results demonstrate that, by leveraging the capability of multihoming route control, it is not necessary to circumvent BGP routing to extract good wide-area performance and availability from the existing routing system.