Mobile network performance from user devices: A longitudinal, multidimensional analysis

Abstract. In the cellular environment, operators, researchers and end users have poor visibility into network performance for devices. Improving visibility is challenging because this performance depends factors that include carrier, access technology, signal strength, geographic location and time. Addressing this requires longitudinal, continuous and large-scale measurements from a diverse set of mobile devices and networks. This paper takes a first look at cellular network performance from this perspective, using 17 months of data collected from devices located throughout the world. We show that (i) there is significant variance in key performance metrics both within and across carriers; (ii) this variance is at best only partially explained by regional and time-of-day patterns; (iii) the stability of network performance varies substantially among carriers. Further, we use the dataset to diagnose the causes behind observed performance problems and identify additional measurements that will improve our ability to reason about mobile network behavior

Low vision assistance with mobile devices

Low vision affects many people, both young and old. Low vision conditions can range from near- and far-sightedness to conditions such as blind spots and tunnel vision. With the growing popularity of mobile devices such as smartphones, there is large opportunity for use of these multipurpose devices to provide low vision assistance. Furthermore, Google\u27s Android operating system provides a robust environment for applications in various fields, including low vision assistance. The objective of this thesis research is to develop a system for low vision assistance that displays important information at the preferred location of the user\u27s visual field. To that end, a first release of a prototype blind spot/tunnel vision assistance system was created and demonstrated on an Android smartphone. Various algorithms for face detection and face tracking were implemented on the Android platform and their performance was assessed with regards to metrics such as throughput and battery usage. Specifically, Viola-Jones, Support Vector Machines, and a color-based method from Pai et al were used for face detection. Template matching, CAMShift, and Lucas-Kanade methods were used for face tracking. It was found that face detection and tracking could be successfully executed within acceptable bounds of time and battery usage, and in some cases performed faster than it would take a comparable cloud-based system for offloading algorithm usage to complete execution

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

A comparison of IP vs 3G network performance indicators

Telecommunication networks of mobile operators are evolving to use an underlying packet- based IP (Internet Protocol) network using Multi Protocol Label Switching (MPLS) as their core technology. The key performance indicators (KPIs) for monitoring the performance of the 3G mobile network’s voice and data services are well established, as are the key performance indicators for interfaces and nodes on an IP network. For this research report an investigation was done on the correlation between the IP KPIs and 3G KPIs through analysis of packet level traces to obtain the IP KPIs as well as reports on KPIs collected on the nodes of the 3G data network. The study was done on MTN South Africa’s operational network at two sites for 2 observation periods of 30 days, with specific focus on the busy hour performance. In addition to the well-known IP KPIs, two extra measurements that were found during a literature survey (SRTO - Spurious Retransmission Timeout and ISR - Invalid Sample Ratio) were calculated based on the packet level traces of IP traffic. The 3G KPIs were chosen from industry standard network quality benchmark reports. The correlation study found no strong linear relationships between the sets of IP and 3G KPIs. This was due to certain limitations in the experimental setup and the observed behaviour of the network (few instances of degradation of behaviour). Further study with modifications to the experimental setup and packet-trace analysis and possibly artificial introduction of negative network conditions will be necessary to verify if correlations exist between the IP and 3G KPIs

Optimizing Mobile Application Performance through Network Infrastructure Aware Adaptation.

Encouraged by the fast adoption of mobile devices and the widespread deployment of mobile networks, mobile applications are becoming the preferred “gateways” connecting users to networking services. Although the CPU capability of mobile devices is approaching that of off-the-shelf PCs, the performance of mobile networking applications is still far behind. One of the fundamental reasons is that most mobile applications are unaware of the mobile network specific characteristics, leading to inefficient network and device resource utilization. Thus, in order to improve the user experience for most mobile applications, it is essential to dive into the critical network components along network connections including mobile networks, smartphone platforms, mobile applications, and content partners. We aim to optimize the performance of mobile network applications through network-aware resource adaptation approaches. Our techniques consist of the following four aspects: (i) revealing the fundamental infrastructure characteristics of cellular networks that are distinctive from wireline networks; (ii) isolating the impact of important factors on user perceived performance in mobile network applications; (iii) determining the particular usage patterns of mobile applications; and (iv) improving the performance of mobile applications through network aware adaptations.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99829/1/qiangxu_1.pd

Performance and Power Characterization of Cellular Networks and Mobile Application Optimizations.

Smartphones with cellular data access have become increasingly popular with the wide variety of mobile applications. However, the performance and power footprint of these mobile applications are not well-understood, and due to the unawareness of the cellular specific characteristics, many of these applications are causing inefficient radio resource and device energy usage. In this dissertation, we aim at providing a suite of systematic methodology and tools to better understand the performance and power characteristics of cellular networks (3G and the new LTE 4G networks) and the mobile applications relying upon, and to optimize the mobile application design based on this understanding. We have built the MobiPerf tool to understand the characteristics of cellular networks. With this knowledge, we make detailed analysis on smartphone application performance via controlled experiments and via a large-scale data set from one major U.S. cellular carrier. To understand the power footprint of mobile applications, we have derived comprehensive power models for different network types and characterize radio energy usage of various smartphone applications via both controlled experiments and 7-month-long traces collected from 20 real users. Specifically, we characterize the radio and energy impact of the network traffic generated when the phone screen is off and propose the screen-aware traffic optimization. In addition to shedding light to the mobile application design throughout our characterization analysis, we further design and implement a real optimization system RadioProphet, which uses historical traffic features to make predictions and intelligently deallocate radio resource for improved radio and energy efficiency.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99905/1/hjx_1.pd