Steve Goddard

A Self-Calibrating Palmer Drought Severity Index

The Palmer Drought Severity Index (PDSI) has been used for more than 30 years to quantify the long-term drought conditions for a given location and time. However, a common critique of the PDSI is that the behavior of the index at various locations is inconsistent, making spatial comparisons of PDSI values difficult, if not meaningless. A self-calibrating Palmer Drought Severity Index (SC-PDSI) is presented and evaluated. The SC-PDSI automatically calibrates the behavior of the index at any location by replacing empirical constants in the index computation with dynamically calculated values. An evaluation of the SC-PDSI at 761 sites within Nebraska, Kansas, Colorado, Wyoming, Montana, North Dakota, and South Dakota, as well as at all 344 climate divisions shows that it is more spatially comparable than the PDSI, and reports extreme wet and dry conditions with frequencies that would be expected for rare conditions.

Scalable Web server clustering technologies

The exponential growth of the Internet, coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. Server clustering has emerged as a promising technique to build scalable Web servers. We examine the seminal work, early products, and a sample of contemporary commercial offerings in the field of transparent Web server clustering. We broadly classify transparent server clustering into three categories.

Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. However, designing real-time scheduling and communication solutions for these networks is challenging since the characteristics of QoS metrics in WSNs are not well known yet. Due to the nature of wireless connectivity, it is infeasible to satisfy worst-case QoS requirements in WSNs. Instead, probabilistic QoS guarantees should be provided, which requires the definition of probabilistic QoS metrics. To provide an analytical tool for the development of real-time solutions, in this paper, the distribution of end-to-end delay in multi-hop WSNs is investigated. Accordingly, a comprehensive and accurate cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework captures the heterogeneity in WSNs in terms of channel quality, transmit power, queue length, and communication protocols. A case study with the TinyOS CSMA/CA MAC protocol is conducted to show how the developed framework can analytically predict the distribution of end-to-end delay. Testbed experiments are provided to validate the developed model. The cross-layer framework can be used to identify the relationships between network parameters and the distribution of end-to-end delay and accordingly, to design real-time solutions for WSNs. Our ongoing work suggests that this framework can be easily extended to model additional QoS metrics such as energy consumption distribution. To the best of our knowledge, this is the first work to investigate probabilistic QoS guarantees in WSNs.

A theory of rate-based execution

We present a task model for the real-time execution of event-driven tasks in which no a priori characterization of the actual arrival rates of events is known; only the expected arrival rates of events is known. The model, called rate-bared execution (RBE), is a generalization of Moks sporadic task model. The RBE model is motivated naturally by distributed multimedia and digital signal processing applications. We derive necessary and sufficient conditions for determining the feasibility of an RBE task set and demonstrate that earliest deadline first (EDF) scheduling is an optimal scheduling algorithm for both preemptive and nonpreemptive execution environments, as well as hybrid environments wherein RBE tasks access shared resources. Our analysis of RBE tasks demonstrates a fundamental distinction between deadline based scheduling methods and static priority based methods. We show that for deadline-based scheduling methods, feasibility is solely a function of the distribution of task deadlines in time. This is contrasted with static priority schedulers where feasibility is a function of the actual arrival rates of work for tasks. Thus whereas the feasibility of static priority schedulers is a function of the periodicity of tasks, the feasibility of deadline schedulers is independent of task arrival processes and hence deadline schedulers are more suitable for use in distributed, event-driven, real-time systems.

Localization and follow-the-leader control of a heterogeneous group of mobile robots

This paper investigates the control and localization of a heterogeneous (e.g., different sensing, mechanical, computational capabilities) group of mobile robots. The group considered here has several inexpensive sensor-limited and computationally limited robots, which follow a leader robot in a desired formation over long distances. This situation is similar to a search, demining, or planetary exploration situation where there are several deployable/disposable robots led by a more sophisticated leader. Specifically, the robots in this paper are designed for highway safety applications where they automatically deploy and maneuver safety barrels commonly used to control traffic in highway work zones. Complex sensing and computation are performed by the leader, while the followers perform simple operations under the leaders guidance. This architecture allows followers to be simple, inexpensive, and have minimal sensors. Theoretical and statistical analysis of a tracking-based localization method is provided. A simple follow-the-leader control method is also presented, including a method for changing followers configuration. Experimental results of localization and follow-the-leader formation-motion are included.

A dynamic voltage scaling algorithm for sporadic tasks

Dynamic voltage scaling (DVS) algorithms save energy by scaling down the processor frequency when the processor is not fully loaded. Many algorithms have been proposed for periodic and aperiodic task models but none support the canonical sporadic task model. A DVS algorithm, called DVSST, is presented that can be used with sporadic tasks in conjunction with preemptive EDF scheduling. The algorithm is proven to guarantee each tasks meets its deadline while saving the maximum amount of energy possible with processor frequency scaling. DVSST was implemented in the /spl mu/C/OS-II real-time operating system for embedded systems and its overhead was measured using a stand-alone Rabbit 2000 test board. Though theoretically optimal, the actual power savings realized with DVSST is a function of the sporadic task set and the processors DVS support. It is shown that the DVSST algorithm achieves 83% of the theoretical power savings for a robotic highway safety marker real-time application. The difference between the theoretical power savings and the actual power savings is due to the limited number of frequency levels the Rabbit 2000 processor supports.

Spatio-Temporal Event Model for Cyber-Physical Systems

The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close-interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatio-temporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

Performance of Quality Assurance Procedures for an Applied Climate Information System

Abstract Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of ±1°C for temperature, not too different from the sensor field accuracy. The study...

Online energy-aware I/O device scheduling for hard real-time systems

Much research has focused on power conservation for the processor, while power conservation for I/O devices has received little attention. In this paper, we analyze the problem of online energy-aware I/O scheduling for hard real-time systems based on the preemptive periodic task model. We propose an online energy-aware I/O device scheduling algorithm: energy-efficient device scheduling (EEDS). The EEDS algorithm utilizes device slack to perform device power state transitions to save energy, without jeopardizing temporal correctness. An evaluation of the approach shows that it yields significant energy savings with respect to no dynamic power management (DPM) techniques

Real-Time Divisible Load Scheduling for Cluster Computing

Cluster computing has emerged as a new paradigm for solving large-scale problems. To enhance QoS and provide performance guarantees in cluster computing environments, various real-time scheduling algorithms and workload models have been investigated. Computational loads that can be arbitrarily divided into independent pieces represent many real-world applications. Divisible load theory (DLT) provides insight into distribution strategies for such computations. However, the problem of providing performance guarantees to divisible load applications has not yet been systematically studied. This paper investigates such algorithms for a cluster environment. Design parameters that affect the performance of these algorithms and scenarios when the choice of these parameters have significant effects are studied. A novel algorithmic approach integrating DLT and EDF (earliest deadline first) scheduling is proposed. For comparison, we also propose a heuristic algorithm. Intensive experimental results show that the application of DLT to real-time cluster-based scheduling leads to significantly better scheduling approaches