Alexandre Gerber

A close examination of performance and power characteristics of 4G LTE networks

With the recent advent of 4G LTE networks, there has been increasing interest to better understand the performance and power characteristics, compared with 3G/WiFi networks. In this paper, we take one of the first steps in this direction. Using a publicly deployed tool we designed for Android called 4GTest attracting more than 3000 users within 2 months and extensive local experiments, we study the network performance of LTE networks and compare with other types of mobile networks. We observe LTE generally has significantly higher downlink and uplink throughput than 3G and even WiFi, with a median value of 13Mbps and 6Mbps, respectively. We develop the first empirically derived comprehensive power model of a commercial LTE network with less than 6% error rate and state transitions matching the specifications. Using a comprehensive data set consisting of 5-month traces of 20 smartphone users, we carefully investigate the energy usage in 3G, LTE, and WiFi networks and evaluate the impact of configuring LTE-related parameters. Despite several new power saving improvements, we find that LTE is as much as 23 times less power efficient compared with WiFi, and even less power efficient than 3G, based on the user traces and the long high power tail is found to be a key contributor. In addition, we perform case studies of several popular applications on Android in LTE and identify that the performance bottleneck for web-based applications lies less in the network, compared to our previous study in 3G [24]. Instead, the devices processing power, despite the significant improvement compared to our analysis two years ago, becomes more of a bottleneck.

Characterizing radio resource allocation for 3G networks

3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.

Profiling resource usage for mobile applications: a cross-layer approach

Despite the popularity of mobile applications, their performance and energy bottlenecks remain hidden due to a lack of visibility into the resource-constrained mobile execution environment with potentially complex interaction with the application behavior. We design and implement ARO, the mobile Application Resource Optimizer, the first tool that efficiently and accurately exposes the cross-layer interaction among various layers including radio resource channel state, transport layer, application layer, and the user interaction layer to enable the discovery of inefficient resource usage for smartphone applications. To realize this, ARO provides three key novel analyses: (i) accurate inference of lower-layer radio resource control states, (ii) quantification of the resource impact of application traffic patterns, and (iii) detection of energy and radio resource bottlenecks by jointly analyzing cross-layer information. We have implemented ARO and demonstrated its benefit on several essential categories of popular Android applications to detect radio resource and energy inefficiencies, such as unacceptably high (46%) energy overhead of periodic audience measurements and inefficient content prefetching behavior.

TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation

In 3G cellular networks, the release of radio resources is controlled by inactivity timers. However, the timeout value itself, also known as the tail time, can last up to 15 seconds due to the necessity of trading off resource utilization efficiency for low management overhead and good stability, thus wasting considerable amount of radio resources and battery energy at user handsets. In this paper, we propose Tail Optimization Protocol (TOP), which enables cooperation between the phone and the radio access network to eliminate the tail whenever possible. Intuitively, applications can often accurately predict a long idle time. Therefore the phone can notify the cellular network on such an imminent tail, allowing the latter to immediately release radio resources. To realize TOP, we utilize a recent proposal of 3GPP specification called fast dormancy, a mechanism for a handset to notify the cellular network for immediate radio resource release. TOP thus requires no change to the cellular infrastructure and only minimal changes to smartphone applications. Our experimental results based on real traces show that with a reasonable prediction accuracy, TOP saves the overall radio energy (up to 17%) and radio resources (up to 14%) by reducing tail times by up to 60%. For applications such as multimedia streaming, TOP can achieve even more significant savings of radio energy (up to 60%) and radio resources (up to 50%).

Over the top video: the gorilla in cellular networks

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 worlds 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.

To Cache or Not to Cache: The 3G Case

Recent studies have shown that in the wired broadband world, caching of HTTP objects results in substantial savings in network resources. What about cellular networks? This article examines the characteristics of HTTP traffic generated by millions of wireless users across one of the worlds largest 3G cellular networks and explores the potential of forward caching. You can easily use the simple cost model provided here to determine the cost-benefit trade-offs for your own cellular network settings. This is the first large-scale caching analysis for cellular networks.

Network-aware forward caching

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. Another contribution of this paper is the analysis we use to motivate and evaluate this problem. We characterize the Internet traffic of 100K subscribers of a US residential broadband provider. We use both layer 4 and layer 7 analysis to investigate the traffic volumes of the flows as well as study the general characteristics of the applications used. We show that HTTP is a dominant protocol and account for 68% of the total downstream traffic and that 34% of that traffic is multimedia. In addition, we show that multimedia content using HTTP exhibits a 83% annualized growth rate and other HTTP traffic has a 53% growth rate versus the 26% over all annual growth rate of broadband traffic. This shows that HTTP traffic will become ever more dominent and increase the potential caching opportunities. Furthermore, we characterize the core backbone traffic of this broadband provider to measure the distance travelled by content and traffic. We find that CDN traffic is much more efficient than P2P content and that there is large skew in the Air Miles between POP in a typical network. Our findings show that there are many opportunties in broadband provider networks to optimize how traffic is delivered and cached.

Periodic transfers in mobile applications: network-wide origin, impact, and optimization

Cellular networks employ a specific radio resource management policy distinguishing them from wired and Wi-Fi networks. A lack of awareness of this important mechanism potentially leads to resource-inefficient mobile applications. We perform the first network-wide, large-scale investigation of a particular type of application traffic pattern called periodic transfers where a handset periodically exchanges some data with a remote server every t seconds. Using packet traces containing 1.5 billion packets collected from a commercial cellular carrier, we found that periodic transfers are very prevalent in todays smartphone traffic. However, they are extremely resource-inefficient for both the network and end-user devices even though they predominantly generate very little traffic. This somewhat counter-intuitive behavior is a direct consequence of the adverse interaction between such periodic transfer patterns and the cellular network radio resource management policy. For example, for popular smartphone applications such as Facebook, periodic transfers account for only 1.7% of the overall traffic volume but contribute to 30% of the total handset radio energy consumption. We found periodic transfers are generated for various reasons such as keep-alive, polling, and user behavior measurements. We further investigate the potential of various traffic shaping and resource control algorithms. Depending on their traffic patterns, applications exhibit disparate responses to optimization strategies. Jointly using several strategies with moderate aggressiveness can eliminate almost all energy impact of periodic transfers for popular applications such as Facebook and Pandora.

Web caching on smartphones: ideal vs. reality

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.

NEVERMIND, the problem is already fixed: proactively detecting and troubleshooting customer DSL problems

Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.