Browsing by Author "Liang, Xu"
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Item Accelerating complex modeling workflows in CyberWater using on-demand HPC/Cloud resources(IEEE, 2021-09) Li, Feng; Chen, Ranran; Fu, Yuankun; Song, Fengguang; Liang, Yao; Ranawaka, Isuru; Pamidighantam, Sudhakar; Luna, Daniel; Liang, Xu; Computer Information and Graphics Technology, School of Engineering and TechnologyWorkflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present an on-demand access mechanism to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor and analyze scientific workflows in the CyberWater project. Cyber-Water is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent library, which can be integrated into the local desktop environment to allow on-demand usage of remote resources for hydrology-related workflows. LaunchAgent manages authentication to remote resources, prepares the computationally-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors the quality of services for the users. LaunchAgent interacts seamlessly with other existing components in CyberWater, which is now able to provide advantages of both feature-rich desktop software experience and increased computation power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrology workflow that consists of both local and remote tasks can be constructed and show that the added on-demand HPC/Cloud usage helps speeding up hydrology workflows while allowing intuitive workflow configurations and execution using a desktop graphical user interface.Item CyberWater: An Open Framework for Data and Model Integration in Water Science and Engineering(ACM, 2022-10-17) Chen, Ranran; Li, Feng; Bieger, Drew; Song, Fengguang; Liang, Yao; Luna, Daniel; Young, Ryan; Liang, Xu; Pamidighantam, Sudhakar; Computer and Information Science, School of ScienceThe CyberWater project is to build an open-data open-model framework for easy and incremental integration of heterogeneous data sources and diverse scientific models across disciplines in the broad water domain. The CyberWater framework extends the open-data open-model framework called Meta-Scientific-Modeling (MSM) that provides a system-wide data and model integration platform. On top of MSM, the CyberWater framework provides a set of toolkits, and external system integration engines, to further facilitate users' scientific modeling and collaboration across disciplines. For example, the developed generic model agent toolkit enables users to integrate their computational models into CyberWater via graphical user interface configuration without coding, which further simplifies the data and model integration and model coupling. CyberWater adopts a graphical scientific workflow system, VisTrails, ensuring data provenance and reproducible computing. CyberWater supports novel access to high-performance computing resources on demand for users' computational expensive model tasks. We demonstrate merits of CyberWater by a use case of hydrologic modeling workflow.Item A Networked Sensor System for the Analysis of Plot-Scale Hydrology(MDPI, 2017-03-20) Villalba, German; Plaza, Fernando; Zhong, Xiaoyang; Davis, Tyler W.; Navarro, Miguel; Li, Yimei; Slater, Thomas A.; Liang, Yao; Liang, Xu; Computer and Information Science, School of ScienceThis study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.Item Open data and model integration through generic model agent toolkit in CyberWater framework(Elsevier, 2022-06) Chen, Ranran; Luna, Daniel; Cao, Yuan; Liang, Yao; Liang, Xu; Computer and Information Science, School of ScienceThe CyberWater project is created to develop an open data and open model integration framework for studying complex environmental and water problems, where diverse online data sources can be directly accessed by diverse models without any need of users’ extra effort on the tedious tasks of data preparation for their models. We present our design and development of a novel generic model agent toolkit in the context of CyberWater, which enables users to integrate their models into the CyberWater system without writing any new code, significantly simplifying the data and model integration task. CyberWater adopts a visual scientific workflow system, VisTrails, which also supports provenance and reproducible computing. Our approach and the developed generic model agent toolkit are demonstrated, via CyberWater framework, with automated and flexible workflows through integrating data and models using real-world use cases. Two popular hydrological models, VIC and DHSVM, are used for illustrations.Item An open-data open-model framework for hydrological models’ integration, evaluation and application(Elsevier, 2020-04) Salas, Daniel; Liang, Xu; Navarro, Miguel; Liang, Yao; Luna, Daniel; Computer and Information Science, School of ScienceTo tackle fundamental scientific questions regarding health, resilience and sustainability of water resources which encompass multiple disciplines, researchers need to be able to easily access diverse data sources and to also effectively incorporate these data into heterogeneous models. To address these cyberinfrastructure challenges, a new sustainable and easy-to-use Open Data and Open Modeling framework called Meta-Scientific-Modeling (MSM) is developed. MSM addresses the challenges of accessing heterogeneous data sources via the Open Data architecture which facilitates integration of various external data sources. Data Agents are used to handle remote data access protocols, metadata standards, and source-specific implementations. The Open Modeling architecture allows different models to be easily integrated into MSM via Model Agents, enabling direct heterogeneous model coupling. MSM adopts a graphical scientific workflow system (VisTrails) and does not require re-compiling or adding interface codes for any diverse model integration. A study case is presented to illustrate the merit of MSM.Item Selection of Multiple Donor Gauges via Graphical Lasso for Estimation of Daily Streamflow Time Series(Wiley, 2021-05) Villalba, German A.; Liang, Xu; Liang, Yao; Computer and Information Science, School of ScienceA fundamental challenge in estimations of daily streamflow time series at sites with incomplete records is how to effectively and efficiently select reference/donor gauges from an existing gauge network to infer the missing data. While research on estimating missing streamflow time series is not new, the existing approaches either use a single reference streamflow gauge or employ a set of “ad hoc” reference gauges, leaving a systematic selection of reference gauges as a long-standing open question. In this work, a novel method is introduced that facilitates a systematic selection of multiple reference gauges from any given streamflow network. The idea is to mathematically characterize the network-wise correlation structure of a streamflow network via graphical Markov modeling and to further transform a dense network into a sparsely connected one. The resulted underlying sparse graph from the graphical model encodes conditional independence conditions among all reference gauges from the streamflow network, allowing determination of an optimum subset of the donor gauges. The sparsity is discovered by using the Graphical Lasso algorithm with an L1 norm regularization parameter and a thresholding parameter. These two parameters are determined by a multi-objective optimization process. Furthermore, the graphical modeling approach is employed to solve another open problem in gauge removal planning decision (e.g., due to operation budget constraints): which gauges to remove would statistically guarantee the least loss of information by estimations from the remaining gauges? Our graphical model-based method is demonstrated with daily streamflow data from a network of 34 gauges over the Ohio River basin region.Item Smart Phone Based Mobile Code Dissemination for Heterogeneous Wireless Sensor Networks(IEEE, 2019-10) Faruk, Omor; Zhong, Xiaoyang; Liang, Yao; Liang, Xu; Computer and Information Science, School of ScienceLow-power Wireless Sensor Networks (WSNs) are being widely used in various outdoor applications including environmental monitoring, precision agriculture, and smart cities. WSN is a distributed network of sensor devices where the software running on the sensor devices defines how the devices should operate. In real-world WSN deployments, sensor node's software update is required to fix bugs and maintain optimal operation. In this paper, we present a novel mobile code dissemination tool based on smart phone running on Android Operating System for heterogeneous WSN reprogramming. Our implementation builds upon Mobile Deluge with new enhancements and more convenient mobile code dissemination tool in practice. We have evaluated our application performance on Android platform, and validated our mobile tool with a real-world outdoor low-power heterogeneous WSN deployment, demonstrating its practical merit.Item Towards Long-Term Multi-Hop WSN Deployments for Environmental Monitoring: An Experimental Network Evaluation(MDPI, 2014-12-05) Navarro, Miguel; Davis, Tyler W.; Villalba, German; Li, Yimei; Zhong, Xiaoyang; Erratt, Newlyn; Liang, Xu; Liang, Yao; Computer and Information Science, School of ScienceThis paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four years for environmental data collection. The WSN performance is studied over selected time periods during the network deployment time, based on two different TinyOS-based WSN routing protocols: commercial XMesh and the open-source Collection Tree Protocol (CTP). Empirical results show that the network performance is improved with CTP (i.e., 79% packet reception rate, 96% packet success rate and 0.2% duplicate packets), versus using XMesh (i.e., 36% packet reception rate and 46% packet success rate, with 3%–4% duplicate packets). The deployment cost of the 52-node, 253-sensor WSN is $31,500 with an additional $600 per month in labor and maintenance resulting in a cost of $184 m−2·y−1 of sensed area. Network maintenance during the first four years of operation was performed on average every 12 days, costing approximately $187 for each field visit.