It is crucial for cellular data network operators to understand the service quality perceived by its customers. The state-of-art systems deployed in cellular networks mostly report service quality aggregated on cell site level, which is typically an aggregation of tens or hundreds of customers depending on the locations of the cell sites. In this paper, we propose to enhance the measurement of customer-perceived service quality by leveraging M2M devices as sensors in the field, which provide an unprecedented opportunity for cellular network operators to measure what end-users experience with better accuracy and coverage. Our approach is to identify a set of M2M devices which are stationary and communicate continuously over the cellular network over an indefinite period of time. We use these M2M devices to estimate the customer-perceived service quality during cell site outages. We implement our methodology as a system called M2MScan and evaluate M2MScan with both synthetic outages and real outages from a large-scale operational cellular network. To the best of our knowledge, this is the first work that employs M2M devices to measure the service quality perceived by customers in operational cellular networks at a large scale.

Providing high end-to-end (E2E) performance experienced by users is critical for cellular service providers to best serve their customers. This paper focuses on the detection and localization of E2E performance degradation (such as slow webpage page loading and unsmooth video playing) at cellular service providers. 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, the 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. Our approach consists of three steps: modeling, detection, and localization. First, we use training data to build models that can capture the normal performance of every E2E instance, which means the 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.

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.

With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Cellular operators still seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions among a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership, all of which we consider in this work. We determine the optimal allocation of wireless resources for users to maximize proportional fair utility. To handle the heterogeneity of user channel conditions, we efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength. Numerical simulations are performed to compare our scheme to practical heuristics and state-of-the-art schemes. We demonstrate the tradeoff between improving unicast user rates and improving spectrum efficiency through multicast. Finally, we analyze the interaction between the globally fair solution and individual user's desire to maximize its rate. We show that even if the user deviates from the global solution in a number of scenarios, we can bound the number of selfish users that will choose to deviate.

Network function virtualization (NFV) promises significant cost savings, flexibility and ease of deployment. However, potential challenges in implementing virtualized network elements that can support real-world performance requirements are still an open question. For example, traditional telecom networks have a lot of complex interdependencies that can affect performance. In this paper, we study the potential bottlenecks in virtualizing cellular core network functions. Using a combination of analysis and experimentation, we quantify the impact of software-based EPC elements on various metrics including physical processing, memory, IO, and bandwidth resource requirements. We use production grade, software-based cellular network elements running on general purpose Linux servers, driven by a variety of realistic workloads derived from a realworld cellular network, to examine the combined effects of control and data planes on an LTE enhanced packet core (EPC). In particular, we discover that the SGW handles about 33% of the control plane transactions and is a potential source for performance bottlenecks as a result of the interdependencies between control and data plane processing. Our results indicate that simply replacing existing EPC elements with virtualized equivalents can have severe performance bottlenecks and that virtualized EPC elements need to be carefully designed.

Nowadays mobile device (e.g., smartphone) users not only have a high expectation on the availability of the cellular data service, but also increasingly depend on the high end-to-end (E2E) performance of their applications. Since the E2E performance of individual application sessions may vary greatly, depending on factors such as the cellular network condition, the content provider, the type/model of the mobile devices, and the application software, detecting and localizing service performance degradations in a timely manner at large scale is of great value to cellular service providers. In this paper, we build a holistic measurement system that tracks session-level E2E performance metrics along with the service attributes for these factors. Using data collected from a major cellular service provider, we first model the expected E2E service performance with a regression based approach, detect performance degradation conditions based on the time series of fine-grained measurement data, and finally localize the service degradation using association-rule-mining techniques. Our deployment experience reveals that in 80% of the detected problem instances, performance degradation can be attributed to non-network-location specific factors, such as a common content provider, or a set of applications running on certain models of devices.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). In this paper, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over 4 months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on Web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify negative interactions between the protocols used for Web access (HTTP/SPDY over TCP) and cellular radio resource management as the underlying cause. Overall performance suffers when devices go through a cellular radio state promotion after an idle period, and the consequent increase in latency. This impacts SPDY more because of the use of a single TCP connection. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved Web performance over cellular networks.

Characterizing the flow of Internet traffic is important in a wide range of contexts, from network engineering and application design to understanding the network impact of consumer demand and business relationships. Despite the growing interest, the nearly impossible task of collecting large-scale, Internet-wide traffic data has severely constrained the focus of traffic-related studies.

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.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). With cellular networks fast becoming the communication channel of choice, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over four months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify, as the underlying cause, a lack of harmony between how TCP and cellular networks interact. In particular, the performance of most TCP implementations is impacted by their implicit assumption that the network round-trip latency does not change after an idle period, which is typically not the case in cellular networks. This causes spurious retransmissions and degraded throughput for both HTTP and SPDY. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved performance over cellular networks.

Large events like the Super Bowl, where almost 75K attendees congegrate for several hours, poses a significant challenge in the planning, design and deployment of wireless networks. This was one of the first events where the LTE cellular network was available widely, in addition to almost 700 WiFi free hotspots. The Super Bowl in 2013 was also unprecedented because of a stadium-wide power outage for over half an hour. This study is the first to look in-depth at the user behaviours and traffic demand of a large ISP's celluar network at such an unique event.

Web caching in mobile networks is critical due to the unprecedented cellular traffic growth that far exceeds the deployment of cellular infrastructures. Caching on handsets is particularly important as it eliminates all network-related overheads. We perform the first network-wide study of the redundant transfers caused by inefficient web caching on handsets, using a dataset collected from 3 million smartphone users of a large commercial cellular carrier, as well as another five-month-long trace contributed by 20 smartphone users. Our findings suggest that redundant transfers contribute 18% and 20% of the total HTTP traffic volume in the two datasets. Also they are responsible for 17% of the bytes, 7% of the radio energy consumption, 6% of the signaling load, and 9% of the radio resource utilization of all cellular data traffic in the second dataset. Most of such redundant transfers are caused by the smartphone web caching implementation that does not fully support or strictly follow the protocol specification, or by developers not fully utilizing the caching support provided by the libraries. This is further confirmed by our caching tests of 10 popular HTTP libraries and mobile browsers. Improving the cache implementation will bring considerable reduction of network traffic volume, cellular resource consumption, handset energy consumption, and user-perceived latency, benefiting both cellular carriers and customers.

The ability to accurately and scalably classify network traffic is of critical importance to a wide range of management tasks of large networks, such as tier-1 ISP networks and global enterprise networks. Guided by the practical constraints and requirements of traffic classification in large networks, in this article, we explore the design of an accurate and scalable machine learning based flow-level traffic classification system, which is trained on a dataset of flow-level data that has been annotated with application protocol labels by a packet-level classifier. Our system employs a lightweight modular architecture , which combines a series of simple linear binary classifiers, each of which can be efficiently implemented and trained on vast amounts of flow data in parallel, and embraces three key innovative mechanisms, weighted threshold sampling, logistic calibration , and intelligent data partitioning , to achieve scalability while attaining high accuracy. Evaluations using real traffic data from multiple locations in a large ISP show that our system accurately reproduces the labels of the packet level classifier when runs on (unlabeled) flow records, while meeting the scalability and stability requirements of large ISP networks. Using training and test datasets that are two months apart and collected from two different locations, the flow error rates are only 3% for TCP flows and 0.4% for UDP flows. We further show that such error rates can be reduced by combining the information of spatial distributions of flows, or collective traffic statistics , during classification. We propose a novel two-step model, which seamlessly integrates these collective traffic statistics into the existing traffic classification system. Experimental results display performance improvement on all traffic classes and an overall error rate reduction by 15%. In addition to a high accuracy, at runtime, our implementation easily scales to classify traffic on 10Gbps links.

Cellular networks have witnessed tremendous traffic growth recently, fueled by smartphones, tablets and new high speed broadband cellular access technologies. A key application driving that growth is video streaming. Yet very little is known about the characteristics of this traffic class. In this paper, we examine video traffic generated by three million users across one of the world's largest 3G cellular networks. This first deep dive into cellular video streaming shows that HLS, an adaptive bitrate streaming protocol, accounts for one third of the streaming video traffic and that it is common to see changes in encoding bitrates within a session. We also observe that most of the content is streamed at less than 255 Kbps and that only 40% of the videos are fully downloaded. Another key finding is that there exists significant potential for caching to deliver this content.

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.

This paper proposes and evaluates a Network Aware Forward Caching approach for determining the optimal deployment strategy of forward caches to a network. A key advantage of this approach is that we can reduce the network costs associated with forward caching to maximize the benefit obtained from their deployment. We show in our simulation that a 37% increase to net benefits could be achieved over the standard method of full cache deployment to cache all POPs traffic. In addition, we show that this maximal point occurs when only 68% of the total traffic is cached.

No abstract available.

Traffic classification is the ability to identify and categorize network traffic by application type. In this paper, we consider the problem of traffic classification in the network core.Classification at the core is challenging because only partial information about the flows and their contributors is available. We address this problem by developing a framework that can classify a flow using only unidirectional flow information. We evaluated this approach using recent packet traces that we collected and pre-classified to establish a "base truth". From our evaluation, we find that flow statistics for the server-to-client direction of a TCP connection provide greater classification accuracy than the flow statistics for the client-to-server direction. Because collection of the server-to-client flow statistics may not always be feasible, we developed and validated an algorithm that can estimate the missing statistics froma unidirectional packet trace.

We apply an unsupervised machine learning approach for Internet traffic identification and compare the results with that of a previously applied supervised machine learning approach. Our unsupervised approach uses an expectation maximization (EM) based clustering algorithm and the supervised approach uses the naive Bayes classifier. We find the unsupervised clustering technique has an accuracy up to 91% and outperform the supervised technique by up to 9%. We also find that the unsupervised technique can be used to discover traffic from previously unknown applications and has the potential to become an excellent tool for exploring Internet traffic.