, and the third study is described for the first time in this article. These studies reveal how users view the ranked results on a search engine results page (SERP), the relationship between the search result abstracts viewed and those clicked on, and whether gender, search task, or search engine influence these behaviors. In addition, we discuss a key challenge that arose in all three studies that applies to the use of eye tracking in studying online behaviors which is due to the limited support for analyzing scanpaths, or sequences of eye fixations. To meet this challenge, we present a preliminary approach that involves a graphical visualization to compare a path with a group of paths. We conclude by summarizing our findings and discussing future work in further understanding online search behavior with the help of eye tracking.

The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for global Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day with high probability. Data from Google's in-production operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.

Peer-to-peer (P2P) dissemination systems are vulnerable to attacks that may impede nodes from receiving data in which they are interested. The same properties that lead P2P systems to be scalable and efficient also lead to security problems and lack of guarantees. Within this context, live-streaming protocols deserve special attention since their time sensitive nature makes them more susceptible to the packet loss rates induced by malicious behavior. While protocols based on dissemination trees often present obvious points of attack, more recent protocols based on pulling packets from a number of different neighbors present a better chance of standing attacks. We explore this in SecureStream, a P2P live-streaming system built to tolerate malicious behavior at the end level. SecureStream is built upon Fireflies, an intrusion-tolerant membership protocol, and employs a pull-based approach for streaming data. We present the main components of SecureStream and present simulation and experimental results on the Emulab testbed that demonstrate the good resilience properties of pull-based streaming in the face of attacks. This and other techniques allow our system to be tolerant to a variety of intrusions, gracefully degrading even in the presence of a large percentage of malicious peers.

We describe a practical auditing approach designed to encourage fairness in peer-to-peer streaming. Auditing is employed to ensure that correct nodes are able to receive streams even in the presence of nodes that do not upload enough data (opportunistic nodes), and scales well when compared to previous solutions that rely on tit-for-tat style of data exchange. Auditing involves two roles: local and global. Untrusted local auditors run on all nodes in the system, and are responsible for collecting and maintaining accountable information regarding data sent and received by each node. Meanwhile, one or more trusted global auditors periodically sample the state of participating nodes, estimate whether the streaming quality is satisfactory, and decide whether any actions are required. We demonstrate through simulation that our approach can successfully detect and react to the presence of opportunistic nodes in streaming sessions. Furthermore, it incurs low network and computational overheads, which remain fixed as the system scales.

The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day. Data from operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.

Application-level multicast systems are vulnerable to attacks that impede nodes from receiving desired data. Live streaming protocols are especially susceptible to packet loss induced by malicious behavior. We describe SecureStream, an application-level live streaming system built using a pull-based architecture that results in improved tolerance of malicious behavior. SecureStream is implemented as a layer running over Fireflies, an intrusion-tolerant membership protocol. Our paper describes the SecureStream system and offers simulation and experimental results confirming its resilience to attack.

We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network. Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples. In this work, we consider the multi-track map matching, where the location data comes from different trips on the same route, each with very sparse samples. This captures the realistic scenario where users repeatedly travel on regular routes and samples are sparsely collected, either due to energy consumption constraints or because samples are only collected when the user actively uses a service. In the multi-track problem, the total set of combined locations is only partially ordered, rather than globally ordered as required by previous map-matching algorithms. We propose two methods, the iterative projection scheme and the graph Laplacian scheme, to solve the multi-track problem by using a single-track map-matching subroutine. We also propose a boosting technique which may be applied to either approach to improve the accuracy of the estimated paths. In addition, in order to deal with variable sampling rates in single-track map matching, we propose a method based on a particular regularized cost function that can be adapted for different sampling rates and measurement errors. We evaluate the effectiveness of our techniques for reconstructing tracks under several different configurations of sampling error and sampling rate.

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