Olaf David

A software engineering perspective on environmental modeling framework design: The Object Modeling System

The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to address this problem, but much work remains before EMFs are adopted as mainstream modeling tools. Environmental model development requires both scientific understanding of environmental phenomena and software developer proficiency. EMFs support the modeling process through streamlining model code development, allowing seamless access to data, and supporting data analysis and visualization. EMFs also support aggregation of model components into functional units, component interaction and communication, temporal-spatial stepping, scaling of spatial data, multi-threading/multi-processor support, and cross-language interoperability. Some EMFs additionally focus on high-performance computing and are tailored for particular modeling domains such as ecosystem, socio-economic, or climate change research. The Object Modeling System Version 3 (OMS3) EMF employs new advances in software framework design to better support the environmental model development process. This paper discusses key EMF design goals/constraints and addresses software engineering aspects that have made OMS3 framework development efficacious and its application practical, as demonstrated by leveraging software engineering efforts outside of the modeling community and lessons learned from over a decade of EMF development. Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability. The key advancements in EMF design presented herein may be applicable and beneficial for other EMF developers seeking to better support environmental model development through improved framework design.

Hydrological modelling with components: A GIS-based open-source framework

This paper describes the structure of JGrass-NewAge: a system for hydrological forecasting and modelling of water resources at the basin scale. It has been designed and implemented to emphasize the comparison of modelling solutions and reproduce hydrological modelling results in a straightforward manner. It is composed of two parts: (i) the data and result visualization system, based on the Geographic Information System uDig and (ii) the component-based modelling system, built on top of the Object Modelling System v3. Modelling components can be selected, adapted, and connected according to the needs of the modeller and then executed within the uDig spatial toolbox. Hence, the system provides an ideal and modern integration of models and GIS without invalidating existing solutions. Compared to traditional hydrological models, which are built upon monolithic code, JGrass-NewAge allows for multiple modelling solutions for the same physical process, provided they share similar input and output constraints. Modelling components are connected by means of a concise scripting language. Furthermore, the system utilizes the Pfafstetter numbering scheme to represent the digital watershed model; the adaption of this topological classification of a basin with respect to NewAge is explained in this paper. Finally, the system application for the Fort Cobb watershed and its results are presented. A system for hydrological modelling of water resources at the basin scale is presented.The system built on top of the Object Modelling System v3.Data and results visualization system is based on the GIS uDig.Modelling solutions can be tested by linking several components.The whole system is applied for the Fort Cobb river basin.

Performance implications of multi-tier application deployments on Infrastructure-as-a-Service clouds: Towards performance modeling

Hosting a multi-tier application using an Infrastructure-as-a-Service (IaaS) cloud requires deploying components of the application stack across virtual machines (VMs) to provide the applications infrastructure while considering factors such as scalability, fault tolerance, performance and deployment costs (# of VMs). This paper presents results from an empirical study which investigates implications for application performance and resource requirements (CPU, disk and network) resulting from how multi-tier applications are deployed to IaaS clouds. We investigate the implications of: (1) component placement across VMs, (2) VM memory size, (3) VM hypervisor type (KVM vs. Xen), and (4) VM placement across physical hosts (provisioning variation). All possible deployment configurations for two multi-tier application variants are tested. One application variant was computationally bound by the application middleware, the other bound by geospatial queries. The best performing deployments required as few as 2 VMs, half the number required for VM-level service isolation, demonstrating potential cost savings when components can be consolidated. Resource utilization (CPU time, disk I/O, and network I/O) varied with component deployment location, VM memory allocation, and the hypervisor used (Xen or KVM) demonstrating how application deployment decisions impact required resources. Isolating application components using separate VMs produced performance overhead of ~1%-2%. Provisioning variation of VMs across physical hosts produced overhead up to 3%. Relationships between resource utilization and performance were assessed using multiple linear regression to develop a model to predict application deployment performance. Our model explained over 84% of the variance and predicted application performance with mean absolute error of only ~0.3 s with CPU time, disk sector reads, and disk sector writes serving as the most powerful predictors of application performance.

Environmental modeling framework invasiveness: Analysis and implications

Environmental modeling frameworks support scientific model development by providing model developers with domain specific software libraries which are used to aid model implementation. This paper presents an investigation on the framework invasiveness of environmental modeling frameworks. Invasiveness, similar to object-oriented coupling, is defined as the quantity of dependencies between model code and a modeling framework. We investigated relationships between invasiveness and the quality of modeling code, and also the utility of using a lightweight framework design approach in an environmental modeling framework. Five metrics to measure framework invasiveness were proposed and applied to measure dependencies between model and framework code of several implementations of Thornthwaite and the Precipitation-Runoff Modeling System (PRMS), two well-known hydrological models. Framework invasiveness measures were compared with existing common software metrics including size (lines of code), cyclomatic complexity, and object-oriented coupling. Models with lower framework invasiveness tended to be smaller, less complex, and have less coupling. In addition, the lightweight framework implementations of the Thornthwaite and PRMS models were less invasive than the traditional framework model implementations. Our results show that model implementations with higher degrees of framework invasiveness also had structural characteristics which previously have been shown to predict poor maintainability, a non-functional code quality attribute of concern. We conclude that using a framework with a lightweight framework design shows promise in helping to improve the quality of model code and that the lightweight framework design approach merits further attention by environmental modeling framework developers.

Migration of Multi-tier Applications to Infrastructure-as-a-Service Clouds: An Investigation Using Kernel-Based Virtual Machines

To investigate challenges of multi-tier application migration to Infrastructure-as-a-Service (IaaS) clouds we performed an experimental investigation by deploying a processor bound and input-output bound variant of the RUSLE2 erosion model to an IaaS based private cloud. Scaling the applications to achieve optimal system throughput is complex and involves much more than simply increasing the number of allotted virtual machines (VMs). While scaling the application variants a series of bottlenecks were encountered unique to an applications processing, I/O, and memory requirements, herein referred to as an applications profile. To investigate the impact of provisioning variation for hosting multi-tier applications we tested four schemes of VM deployments across the physical nodes of our cloud. Performance degradation was more pronounced when multiple I/O or CPU resource intensive application components were co-located on the same physical hardware. We investigated the virtualization overhead incurred using Kernel-based virtual machines (KVM) by deploying our application variants to both physical and virtual machines. Overhead varied based on the unique characteristics of each applications profile. We observed ~112% overhead for the input/output bound application and just ~ 10% overhead for the processor bound application. Understanding an applications profile was found to be important for optimal IaaS-based cloud migration and scaling.

Performance Modeling to Support Multi-tier Application Deployment to Infrastructure-as-a-Service Clouds

Infrastructure-as-a-service (IaaS) clouds support migration of multi-tier applications through virtualization of diverse application stack(s) of components which may require various operating systems and environments. To maximize performance of applications deployed to IaaS clouds while minimizing deployment costs, it is necessary to create virtual machine images to host application components with consideration for component dependencies that may affect load balancing of physical resources of VM hosts including CPU time, disk and network bandwidth. This paper presents results of an investigation utilizing physical machine (PM) and virtual machine (VM) resource utilization statistics to build performance models to predict application performance and rank performance of application component deployment configurations deployed across VMs. Our objective was to predict which component compositions provide best performance while requiring the fewest number of VMs. Eighteen individual resource utilization statistics were investigated for use as independent variables to predict service execution time using four different modeling approaches. Overall CPU time was the strongest predictor of execution time. The strength of individual predictors varied with respect to the resource utilization profiles of the applications. CPU statistics including idle time and number of context switches were good predictors when the test application was more disk I/O bound, while disk I/O statistics were better predictors when the application was more CPU bound. All performance models built were effective at determining the best performing service composition deployments validating the utility of our approach.

Assessing the Potential of the Object Modeling System (OMS) for Erosion Prediction Modeling

Current challenges in soil erosion research have created demand for integrated, flexible, and easily parameterized sediment transport models. Most of the existing monolithic erosion models (e.g., WEPP and WEPS) are not modular, thus modifications require considerable time, effort, and expense. In this paper, the feasibility and challenges of using the Object Modeling System (OMS) for soil erosion model development will be explored. The OMS is a Java-based modeling framework that facilitates simulation model development, evaluation, and deployment. We present application of a fully restructured and modularized core WEPP hillslope erosion component functioning within the OMS as a single compartmentalized erosion module. In addition, we discuss specific features of the OMS related to soil erosion modeling including: 1) how to reduce duplication of effort in wind and water erosion modeling; 2) how to make soil erosion models easier to build, apply, and evaluate, 3) how to facilitate long-term maintainability of soil erosion models; and 4) how to improve the quality of soil erosion model code and ensure credibility of model implementations.

Demystifying the Clouds: Harnessing Resource Utilization Models for Cost Effective Infrastructure Alternatives

Deployment of service oriented applications (SOAs) to public infrastructure-as-a-service (IaaS) clouds presents challenges to system analysts. Public clouds offer an increasing array of virtual machine types with qualitatively defined CPU, disk, and network I/O capabilities. Determining cost effective application deployments requires selecting both the quantity and type of virtual machine (VM) resources for hosting SOA workloads of interest. Hosting decisions must utilize sufficient infrastructure to meet service level objectives and cope with service demand. To support these decisions, analysts must: (1) understand how their SOA behaves in the cloud; (2) quantify representative workload(s) for execution; and (3) support service level objectives regardless of the performance limits of the hosting infrastructure. In this paper we introduce a workload cost prediction methodology which harnesses operating system time accounting principles to support equivalent SOA workload performance using alternate virtual machine types. We demonstrate how the use of resource utilization checkpointing supports capturing the total resource utilization profile for SOA workloads executed across a pool of VMs. Given these workload profiles, we develop and evaluate our cost prediction methodology using six SOAs. We demonstrate how our methodology can support finding alternate infrastructures that afford lower hosting costs while offering equal or better performance using any VM type on Amazons public elastic compute cloud.

Development of a Hillslope Erosion Module for the Object Modeling System

A recent high priority need item of the USDA – Natural Resources Conservation Service (NRCS) was development by the USDA - Agricultural Research Service (ARS) of a combined water and wind process erosion model. This new model would ultimately replace individual erosion prediction software tools (e.g. RUSLE, WEPS, WEPP, etc.), and would provide consistent results in estimating soil moisture, runoff, plant biomass development, and residue cover, decomposition and burial by tillage. The new tool would take the best science available from existing models, and when necessary or appropriate new scientific relationships would be utilized. As a possible platform for development of the combined erosion model, the Object Modeling System (OMS) currently being developed by USDA-ARS GPSRU and Colorado State University in Fort Collins, CO was utilized. This presentation describes the process of creating a standalone hillslope erosion program (originally based upon the Water Erosion Prediction Project (WEPP) model code), testing of that program, then incorporation of the stand-alone erosion program within the OMS system. Evaluation of the new modular component, and work on creation of additional components and a complete functional erosion model within OMS will also be discussed.

IAEA Isotope-enabled coupled catchment–lake water balance model, IWBMIso: description and validation†

ABSTRACT The International Atomic Energy Agency (IAEA) Water Balance Model with Isotopes (IWBMIso) is a spatially distributed monthly water balance model that considers water fluxes and storages and their associated isotopic compositions. It is composed of a lake water balance model that is tightly coupled with a catchment water balance model. Measured isotope compositions of precipitation, rivers, lakes, and groundwater provide data that can be used to make an improved estimate of the magnitude of the fluxes among the model components. The model has been developed using the Object Modelling System (OMS). A variety of open source geographic information systems and web-based tools have been combined to provide user support for (1) basin delineation, characterization, and parameterization; (2) data pre-processing; (3) model calibration and application; and (4) visualization and analysis of model results. In regions where measured data are limited, the model can use freely available global data sets of climate, isotopic composition of precipitation, and soils and vegetation characteristics to create input data files and estimate spatially distributed model parameters. The OMS model engine and support functions, and the spatial and web-based tool set are integrated using the Colorado State University Environmental Risk Assessment and Management System (eRAMS) framework. The IWBMIso can be used to assess the spatial and temporal variability of annual and monthly water balance components for input to water planning and management.