Edward T.-H. Chu

A Near-optimal Solution for the Heterogeneous Multi-processor Single-level Voltage Setup Problem

A heterogeneous multi-processor (HeMP) system consists of several heterogeneous processors, each of which is specially designed to deliver the best energy-saving performance for a particular category of applications. A low-power real-time scheduling algorithm is required to schedule tasks on such a system to minimize its energy consumption and complete all tasks by their deadline. The problem of determining the optimal speed for each processor to minimize the total energy consumption is called the voltage setup problem. This paper provides a near-optimal solution for the HeMP single-level voltage setup problem. To our best knowledge, we are the first work that addresses this problem. Initially, each task is assigned to a processor in a local-optimal manner. We next propose a couple of solutions to reduce energy by migrating tasks between processors. Finally, we determine each processors speed by its final workload and the deadline. We conducted a series of simulations to evaluate our algorithms. The results show that the local-optimal partition leads to a considerably better energy-saving schedule than a commonly-used homogeneous multi-processor scheduling algorithm. Furthermore, at all measurable configurations, our energy consumption is at most 3% more than the optimal value obtained by an exhaustive iteration of all possible task-to-processor assignments. In summary, our work is shown to provide a near-optimal solution at its polynomial-time complexity.

Time-and-Energy-Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds

Running sophisticated software on smart phones could result in poor performance and shortened battery lifetime because of their limited resources. Recently, offloading computation workload to the cloud has become a promising solution to enhance both performance and battery life of smart phones. However, it also consumes both time and energy to upload data or programs to the cloud and retrieve the results from the cloud. In this paper, we develop an offloading framework, named Ternary Decision Maker (TDM), which aims to shorten response time and reduce energy consumption at the same time. Unlike previous works, our targets of execution include an on-board CPU, an on-board GPU, and a cloud, all of which combined provide a more flexible execution environment for mobile applications. We conducted a real-world application, i.e., matrix multiplication, in order to evaluate the performance of TDM. According to our experimental results, TDM has less false offloading decision rate than existing methods. In addition, by offloading modules, our method can achieve, at most, 75% savings in execution time and 56% in battery usage.

On the Accuracy, Efficiency, and Reusability of Automated Test Oracles for Android Devices

Automated GUI testing consists of simulating user events and validating the changes in the GUI in order to determine if an Android application meets specifications. Traditional record-replay testing tools mainly focus on facilitating the test case writing process but not the replay and verification process. The accuracy of testing tools degrades significantly when the device under test (DUT) is under heavy load. In order to improve the accuracy, our previous work, SPAG, uses event batching and smart wait function to eliminate the uncertainty of the replay process and adopts GUI layout information to verify the testing results. SPAG maintains an accuracy of up to 99.5 percent and outperforms existing methods. In this work, we propose smart phone automated GUI testing tool with camera (SPAG-C), an extension of SPAG, to test an Android hardware device. Our goal is to further reduce the time required to record test cases and increase reusability of the test oracle without compromising test accuracy. In the record stage, SPAG captures screenshots from devices frame buffer and writes verification commands into the test case. Unlike SPAG, SPAG-C captures the screenshots from an external camera instead of frame buffer. In the replay stage, SPAG-C automatically performs image comparison while SPAG simply performs a string comparison to verify the test results. In order to make SPAG-C reusable for different devices and to allow bettersynchronization at the time of capturing images, we develop a new architecture that uses an external camera and Web services to decouple the test oracle. Our experiments show that recording a test case using SPAG-Cs automatic verification is as fast as SPAGs but more accurate. Moreover, SPAG-C is 50 to 75 percent faster than SPAG in achieving the same test accuracy. With reusability, SPAG-C reduces the testing time from days to hours for heterogeneous devices.

Cyberphysical Elements of Disaster-Prepared Smart Environments

Intelligent Guards against Disasters (iGaDs) could process and respond to alert and warning messages from responsible authorities and thus help in preparing to respond to disasters, but making such smart devices and systems dependable and affordable enough for pervasive use in future smart living environments will require strong standards.

Active Disaster Response System for a Smart Building

In recent years, major natural disasters have made it more challenging for us to protect human lives. Examples include the 2011 Japan Tohoku earthquake and Typhoon Morakot in 2009 in Taiwan. However, modern disaster warning systems cannot automatically respond efficiently and effectively to disasters. As a result, it is necessary to develop an automatic response system to prevent, diminish or eliminate the damages caused by disasters. In this paper, we develop an active emergency disaster system to automatically process standard warning messages, such as CAP (Common Alerting Protocol) messages. After receiving official warning messages of earthquakes, our system automatically shuts down natural gas lines in order to prevent buildings from fire and opens the building doors for easy evacuation. Our system also stops elevators when they reach the closest floor. In addition, our system can be applied to hospitals to tell surgeons to pause ongoing operations, or to supermarkets to inform consumers of the way to exit the building. According to our experiment results, the proposed system can avoid possible dangers and save human lives during major disasters.

Improving the Accuracy of Automated GUI Testing for Embedded Systems

Automated GUIs test application user interfaces and verify their functionalities. However, due to the uncertainty of runtime execution environments, the device under test (DUT) might not reproduce GUI operations on time, resulting in test failures. The Smart Phone Automated GUI (SPAG) avoids nondeterministic events by batching event sequences and directly reproducing them on the DUT. SPAG dynamically changes the timing of following operations so that all event sequences can be performed on time. Experiments conducted on an Acer Liquid smartphone comparing SPAG to MonkeyRunner showed that SPAG can maintain up to 99.5 percent accuracy.

A Framework for Fusion of Human Sensor and Physical Sensor Data

Many disaster warning and response systems can improve their surveillance coverage of the threatened area by supplementing in situ and remote physical sensor data with crowdsourced human sensor data captured and sent by people in the area. This paper presents fusion methods which enable a crowdsourcing enhanced system to use human sensor data and physical sensor data synergistically to improve its sensor coverage and the quality of its decisions. The methods are built on results of classical statistical detection and estimation theory and use value fusion and decision fusion of human sensor data and physical sensor data in a coherent way. They are the building blocks of a central fusion unit in a crowdsourcing support system for disaster surveillance and early warning applications.

Strategies For Crowdsourcing ForDisaster Situation Information

When existing surveillance sensors used by a disaster warning and response system cannot provide adequate data for situation assessment purposes, crowdsourcing information collection can be an effective solution: People armed with wireless devices and social network services can be used as mobile human sensors. Eye-witness reports from them can complement data from in-situ physical sensors and provide the system with more extensive and detailed sensor coverage. The crowdsourcing strategy used by the system can be random, relying solely on mobility of individuals for coverage of the threatened area; or crowddriven, with the system providing situation updates as feedbacks to aid the crowd; or system-driven with individuals moving in response to directives from the system. The relative merits of the strategies clearly depend on the disaster scenario and the characteristics of the crowd. This paper presents a general crowd model for characterizing individuals within a crowd and the crowd as a whole and an abstract mobility model of crowd movements in the threatened area. The models can be specialized to characterize different disaster scenarios and crowds and used in simulation of the crowdsourcing strategies for evaluation purposes. Data on relative performance of different strategies for two types of disasters were thus obtained.

WRR-SCAN: a rate-based real-time disk-scheduling algorithm

Traditional real-time disk-scheduling algorithms service real-time tasks according to their deadlines. Such a priority-based algorithm, although satisfying real-time constraints, yields low disk utilization due to the excessive disk-seek time. Furthermore, it results in prolonged response time or even starvation for aperiodic tasks. In this paper, we propose a novel rate-based real-time disk-scheduling algorithm called WRR-SCAN (Weighted-Round-Robin SCAN). WRR-SCAN guarantees to meet the deadline of a real-time task by reserving disk bandwidth according to its real-time constraints. WRR-SCAN services scheduled tasks in scan order to minimize the disk-seek time. In addition, WRR-SCAN delivers better response time for aperiodic tasks which are served in best-effort manner by priority based algorithms. We conducted a set of extensive experiments to compare WRR-SCAN and SCAN-EDF, a priority-based algorithm studied extensively in literature. The experimental results show that WRR-SCAN reduces non-transmission overhead significantly and produces a guaranteed minimum data rate for aperiodic tasks while keeping the deadlines of real-time tasks.

Ubiquitous Smart Devices and Applications for Disaster Preparedness

Recent advances in disaster prediction and detection technologies and ICT support infrastructures have enabled the generation and reliable deliveries of machine-readable early disaster alerts over all communication pathways. The emergence of ubiquitous smart devices and applications that can receive, authenticate and process standard-conforming disaster alert messages and respond by taking appropriate actions to help us to be better prepared for nature disasters is a natural next step in the advancement of disaster management technologies. We call such smart devices and applications iGaDs (intelligent Guards against Disasters). This paper describes reference architecture, key components and design of iGaDs in general and an ASIC enhancement of battery-powered iGaDs.