Ashkan Nikravesh

Mobilyzer: An Open Platform for Controllable Mobile Network Measurements

Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways. In this work we propose Mobilyzer, a platform for conducting mobile network measurement experiments in a principled manner. Our system is designed around three key principles: network measurements from mobile devices require tightly controlled access to the network interface to provide isolation; these measurements can be performed efficiently using a global view of available device resources and experiments; and distributing the platform as a library to existing apps provides the incentives and low barrier to adoption necessary for large-scale deployments. We describe our current design and implementation, and illustrate how it provides measurement isolation for applications, efficiently manages measurement experiments and enables a new class of experiments for the mobile environment.

Mobile Network Performance from User Devices: A Longitudinal, Multidimensional Analysis

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.

An in-depth understanding of multipath TCP on mobile devices: measurement and system design

Todays mobile devices are usually equipped with multiple wireless network interfaces that provide new opportunities for improving application performance. In this paper, we conduct an in-depth study of multipath for mobile settings, focusing on MPTCP, with the goal of developing key insights for evolving the mobile multipath design. First, we conduct to our knowledge the most in-depth and the longest user trial of mobile multipath that focuses not only on MPTCP performance, but also on cross-layer interactions. Second, we identify a new research problem of multipath-aware CDN server selection. We demonstrate its real-world importance and provide recommendations. Third, our measurement findings lead us to design and implement a flexible software architecture for mobile multipath called MPFlex, which strategically employs multiplexing to improve multipath performance (by up to 63% for short-lived flows). MPFlex decouples the high-level scheduling algorithm and the low-level OS protocol implementation, and enables developers to flexibly plug-in new multipath features. MPFlex also provides an ideal vantage point for flexibly realizing user-specified multipath policies and is friendly to middleboxes.

Revisiting Network Energy Efficiency of Mobile Apps: Performance in the Wild

Energy consumption due to network traffic on mobile devices continues to be a significant concern. We examine a range of excessive energy consumption problems caused by background network traffic through a two-year user study, and also validate these findings through in-lab testing of the most recent versions of major mobile apps. We discover a new energy consumption problem where foreground network traffic persists after switching from the foreground to the background, leading to unnecessary energy and data drain. Furthermore, while we find some apps have taken steps to improve the energy impact of periodic background traffic, energy consumption differences of up to an order of magnitude exist between apps with near-identical functionality. Finally, by examining how apps are used in the wild, we find that some apps continue to generate unneeded traffic for days when the app is not being used, and in some cases this wasted traffic is responsible for a majority of the apps network energy overhead. We propose that these persistent, widespread and varied sources of excessive energy consumption in popular apps should be addressed through new app management tools that tailor network activity to user interaction patterns.

Demo: Mapping global mobile performance trends with mobilyzer and mobiPerf

Mobilyzer is an open-source network measurement library that coordinates network measurement tasks among different applications, facilitates measurement task design, and allows for more effective measurement task management than in existing standalone approaches. Unifying various network tasks into one framework greatly simplifies the problem of developing, deploying and managing measurement tasks which may otherwise interfere with one another. An intelligent scheduler, coordinated by a central server, dynamically schedules tasks to run in the background, preserving the users battery life and respecting limits set by the user on task frequency and data consumption. We will demo MobiPerf, an open-source mobile network measurement tool built using the Mobilyzer library. MobiPerf collects a wide range of network performance data, ranging from the latency and throughput measurements common in existing client-based measurement frameworks, to HTTP loading times for specific URLs, to inferring RRC state configuration parameters and their impact on performance. We will also demo an interface for viewing a large, open dataset of performance data from around the world collected by MobiPerf.

Accelerating Multipath Transport Through Balanced Subflow Completion

Simultaneously using multiple network paths (e.g., WiFi and cellular) is an attractive feature on mobile devices. A key component in a multipath system such as MPTCP is the scheduler, which determines how to distribute the traffic over multiple paths. In this paper, we propose DEMS, a new multipath scheduler aiming at reducing the data chunk download time. DEMS consists of three key design decisions: (1) being aware of the chunk boundary and strategically decoupling the paths for chunk delivery, (2) ensuring simultaneous subflow completion at the receiver side, and (3) allowing a path to trade a small amount of redundant data for performance. We have implemented DEMS on smartphones and evaluated it over both emulated and real cellular/WiFi networks. DEMS is robust to diverse network conditions and brings significant performance boost compared to the default MPTCP scheduler (e.g., median download time reduction of 33%--48% for fetching files and median loading time reduction of 6%--43% for fetching web pages), and even more benefits compared to other state-of-the-art schedulers.

Demo: DEMS: DEcoupled Multipath Scheduler for Accelerating Multipath Transport

We present the demonstration of DEMS, a new multipath scheduler aiming at reducing the data chunk download time. DEMS consists of three key design decisions: (1) being aware of the chunk boundary and strategically decoupling the paths for chunk delivery, (2) ensuring simultaneous subflow completion at the receiver side, and (3) allowing a path to trade a small amount of redundant data for performance. We integrate the DEMS components into a holistic system and implement it on commodity mobile devices, where unmodified mobile applications can use DEMS to transmit data over multipath. We demonstrate the simple configuration of using DEMS over multipath, visualization of multipath scheduling, download time reduction of data chunks with DEMS over both emulated and real cellular/WiFi networks compared to default MinRTT scheduler, and application QoE improvement on mobile phones from DEMS.

Poster: Context-Triggered Mobile Network Measurement

While the availability and accessibility of cellular network connectivity have improved in recent years, our ability to diagnose and debug network problems in this environment has not. One key challenge is that many of the network problems occur near the edge of the network where only mobile devices can perceive them, but network and battery resources to conduct measurements from these mobile devices are scarce. Traditional network measurement approaches that use continuous, periodic, or random measurements are either infeasible or ineffective in this environment. In this work, we propose an alternative approach: triggering measurements based on relevant device context such as signal strength and historical performance data, which can inform decisions for when to measure current network performance. This context can be collected locally on the device as well as aggregated at a global scale to schedule measurement based on data collected from multiple devices. By carefully selecting when to conduct a measurement, and using prediction to improve the likelihood that triggered measurements will succeed, we can more reliably measure important network phenomena with less overhead. Using Mobilyzer [3] as a platform for evaluation, we propose an architecture that is sufficiently general to support a wide range of triggered measurement experiments. We demonstrate the use of this framework for measurements on mobile platforms that are traditionally difficult to capture, e.g., handoff measurement. Further, we can use the global scheduler to predict which devices will likely satisfy the preconditions for the triggered measurement to improve the measurement success rate. Compared to previous work [2, 1, 4], ours is the first to propose a general framework to enable context-triggered mobile measurement, leveraging both local and global visibility into context while ensuring low overhead.