Andrew C. Rice

Predicting the Performance of Virtual Machine Migration

With the ability to move virtual machines between physical hosts, live migration is a core feature of virtualisation. However for migration to be useful, deployable feature on a large (datacentre) scale, we need to predict migration times with accuracy. In this paper, we characterise the parameters affecting live migration with particular emphasis on the Xen virtualisation platform. We discuss the relationships between the important parameters that affect migration and highlight how migration performance can vary considerably depending on the workload. We further provide 2 simulation models that are able to predict migration times to within 90% accuracy for both synthetic and real-world benchmarks.

Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

Despite the tremendous market penetration of smartphones, their utility has been and will remain severely limited by their battery life. A major source of smartphone battery drain is accessing the Internet over cellular or WiFi connection when running various apps and services. Despite much anecdotal evidence of smartphone users experiencing quicker battery drain in poor signal strength, there has been limited understanding of how often smartphone users experience poor signal strength and the quantitative impact of poor signal strength on the phone battery drain. The answers to such questions are essential for diagnosing and improving cellular network services and smartphone battery life and help to build more accurate online power models for smartphones, which are building blocks for energy profiling and optimization of smartphone apps. In this paper, we conduct the first measurement and modeling study of the impact of wireless signal strength on smartphone energy consumption. Our study makes four contributions. First, through analyzing traces collected on 3785 smartphones for at least one month, we show that poor signal strength of both 3G and WiFi is routinely experienced by smartphone users, both spatially and temporally. Second, we quantify the extra energy consumption on data transfer induced by poor wireless signal strength. Third, we develop a new power model for WiFi and 3G that incorporates the signal strength factor and significantly improves the modeling accuracy over the previous state of the art. Finally, we perform what-if analysis to quantify the potential energy savings from opportunistically delaying network traffic by exploring the dynamics of signal strength experienced by users.

Device Analyzer: Understanding Smartphone Usage

We describe Device Analyzer, a robust data collection tool which is able to reliably collect information on Android smartphone usage from an open community of contributors. We collected the largest, most detailed dataset of Android phone use publicly available to date. In this paper we systematically evaluate smartphones as a platform for mobile ubiquitous computing by quantifying access to critical resources in the wild. Our analysis of the dataset demonstrates considerable diversity in behaviour between users but also over time. We further demonstrate the value of handset-centric data collection by presenting case-study analyses of human mobility, interaction patterns, and energy management and identify notable differences between our results and those found by other studies.

Decomposing power measurements for mobile devices

Modern mobile phones are an appealing platform for pervasive computing applications. However, the complexity of these devices makes it difficult for developers to understand the power consumption of their applications. Our measurement framework is the first we have seen which can produce fine-grained, annotated traces of a phones power consumption and is designed to develop an understanding of how particular aspects of an application drive energy use. We are using our framework to analyse the power consumption of Android-based G1 and Magic handsets and show that particular choices of message size and send buffer can alter the energy required to send data by an order of magnitude in certain cases.

Measuring mobile phone energy consumption for 802.11 wireless networking

The complexity of modern mobile phones makes it difficult for developers to understand the power consumption of their applications. Our measurement framework produces fine-grained, annotated traces of a phones power consumption which we are using to develop an understanding of how particular aspects of an application drive energy use. We ran a large number of automated tests using Google Android G1, Magic, Hero and Nexus handsets and present results for the average energy consumption of connection and data transmission over 802.11 wireless networks. Our results show that the optimal choice of data transmission strategy is different between handsets, operating systems, and device context.

Device analyzer: large-scale mobile data collection

We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.

An Analysis of Hard Drive Energy Consumption

The increasing storage capacity and necessary redundancy of data centers and other large-scale IT facilities has drawn attention to the issue of reducing the power consumption of hard drives. This work comprehensively investigates the power consumption of hard drives to determine typical runtime power profiles. We have instrumented at a fine-grained level and present our findings which show that (i) the energy consumed by the electronics of a drive is just as important as the mechanical energy consumption; (ii) the energy required to access data is affected by physical location on a drive; and (iii) the size of data transfers has measurable effect on power consumption.

The case for apportionment

Apportioning the total energy consumption of a building or organisation to individual users may provide incentives to make reductions. We explore how sensor systems installed in many buildings today can be used to apportion energy consumption between users. We investigate the differences between a number of possible policies to evaluate the case for apportionment based on energy and usage data collected over the course of a year. We also study the additional possibilities offered by more fine-grained data with reference to case studies for specific shared resources, and discuss the potential and challenges for future sensor systems in this area.

Computing for the Future of the Planet

Digital technology is becoming an indispensable and crucial component of our lives, society and the environment. We present a framework for computing in the context of problems facing the planet. The framework has a number of goals: an optimal digital infrastructure, sensing and optimizing with a global world model, reliably predicting and reacting to our environment and providing digital alternatives to physical activities. This paper describes our vision in which data centres can scale power consumption in line with performance, run closer to the wire with reduced redundancy and behave as a ‘virtual battery’ dynamically using spare, or otherwise unusable, generation capacity from renewable sources. On a broader scale, we consider how global sensing might allow us to optimize our daily activities and lives. We highlight the issues and dilemmas inherent in the deployment of global sensing infrastructure and work towards our challenge of a personal energy meter as a tool for informing decisions and providing impetus for reducing the ecological footprint of our society.

Security Metrics for the Android Ecosystem

The security of Android depends on the timely delivery of updates to fix critical vulnerabilities. In this paper we map the complex network of players in the Android ecosystem who must collaborate to provide updates, and determine that inaction by some manufacturers and network operators means many handsets are vulnerable to critical vulnerabilities. We define the FUM security metric to rank the performance of device manufacturers and network operators, based on their provision of updates and exposure to critical vulnerabilities. Using a corpus of 20 400 devices we show that there is significant variability in the timely delivery of security updates across different device manufacturers and network operators. This provides a comparison point for purchasers and regulators to determine which device manufacturers and network operators provide security updates and which do not. We find that on average 87.7% of Android devices are exposed to at least one of 11 known critical vulnerabilities and, across the ecosystem as a whole, assign a FUM security score of 2.87 out of 10. In our data, Nexus devices do considerably better than average with a score of 5.17; and LG is the best manufacturer with a score of 3.97.