As smart vehicles have become more ubiquitous, the capability now exists to detect environmental road features (e.g., potholes, road incline angle, etc.) from their embedded sensor data. By aggregating data from multiple vehicles, crowdsourcing can be leveraged to detect environmental information with improved accuracy. We focus on using such data to detect and localize potholes on multi-lane roads. Extracting information from aggregated vehicle data is challenging due to undersampling sensors, sensor mobility, asynchronous sensor operation, sensor noise, vehicle and road heterogeneity, and GPS position error. GPS position error is particularly problematic in multi-lane environments since the position error is generally larger than standard lane widths. In this paper, we investigate these issues and develop a crowdsourced system to detect and localize potholes in multi-lane environments using accelerometer data from embedded vehicle sensors. Our crowdsourced system reduces the required network bandwidth by determining road incline and bank angle information in each vehicle to filter acceleration components that do not correspond to pothole conditions. We evaluate our system on simulated and real-world data, analyze tradeoffs in the number of vehicles and the amount of bandwidth required for accurate detection, and compare the results to the simpler single lane detection scenario.

Crowdsourcing data from multiple sensing agents has become a fundamental mechanism for extracting information in large-scale cyber-physical systems (CPS). However, there has been little attention paid to the difficulties crowdsourcing presents when the desired information is represented by continuous signals. Crowdsourcing can result in unique types of noise in the aggregated data sample set. Errors from clock synchronization, GPS location determination, or sensor heterogeneity result in biased and correlated errors in the sampled data from each individual sensor. Furthermore, in CPS sensors are often mobile and operate asynchronously. This affects the spatial sampling rates among sensors and results in nonuniform sample spacing. We refer to these sampling issues as the noisy multi-source, variable-rate (MSVR) sampling problem. This work introduces and investigates signal reconstruction algorithms given MSVR sampling conditions. These signal reconstructions are investigated for vehicular applications, using a joint road inclination and bank angle signal estimation algorithm that has access to MSVR sampled vehicle GPS and accelerometer data. The signal reconstruction and angle determination algorithms are validated on simulated and real-world vehicle data, where we reconstruct a road elevation signal with 0.89 m root-mean-square error.

The increased availability of embedded vehicle sensors allows for the detection of road features such as potholes. Despite being a promising approach, current vehicle embedded sensors operate at low frequencies and undersample sensor signals, thus degrading detection accuracy. One emerging solution is to crowdsource such undersampled sensor data from multiple vehicles to increase the detection accuracy. Aggregating sensor data from multiple vehicles, nonetheless, is a challenging task given the heterogeneity among vehicles, asynchronous sensor operation, GPS error, and sensor noise. Additionally, there may be bandwidth restrictions in vehicular networks which limit the amount of data available for aggregation. We investigate these issues by focusing on the problem of pothole detection. To quantify the detection accuracies and effects of real-world limitations, we design and evaluate three crowdsourcing pothole detection schemes involving vehicles and the Cloud. We also address the issue of lack of extensive model training data by demonstrating that a detection model applicable to real-world systems can be derived using simulated data. We validate our pothole detection methods using 38.1 km of real-world data collected from driving on roads in Warren, Michigan.

Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.

Consolidating Virtual Machines (VMs) in data centers is desirable as it reduces hardware and power costs. However the performances of VMs on shared physical servers are not isolated from each other as they contend for the same server resources. This contention degrades the performance of delay sensitive applications and can increase response times by three orders of magnitude at high contention levels. In order to achieve Service Level Agreements (SLAs) under VM consolidation, resources must be allocated by considering the performance effects of contention. We therefore present VARACO, a contention-aware VM management system to achieve Quality-of-Service (QoS) targets for multi-tier web applications. VARACO models applications' performances online using 13 server resource utilization and contention metrics. Resources are dynamically allocated using these models to achieve QoS targets. Our results show that application- level performance can be modeled 130% more accurately when resource contention is considered. We demonstrate VARACO by achieving a 90thpercentile response time target of a sample application under VM consolidation.