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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The First Year of KOKEE12M-WETTZ13S VGOS Intensive Scheduling: Status and Efforts Towards Improvement

The KOKEE12M-WETTZ13S (K2-Ws) Intensive series began on January 4, 2021. Evaluation of the first year of schedules has shown that the series is performing reasonably weel, but the schedules could be improved. For example, the number of scheduled observations in a typical 2021 K2-Ws session is approximately half that of the first few MACGO12M-WETTZ13S Intensive sessions, and scheduling fewer observations negatively impats UT1 results. Also, preliminary analysis of the scheduled and achieve SNRs of the K2-Ws observations and the SNRs' ratios shows examples where the achieved SNRs are either too high, indicating long observations that could have been replaced by a greater number of shorter observations, or too low, indicating a lack of robustness that could lead to observation loss. In this poster, we describe key aspects of how the K2-Ws Intensives have been scheduled in 2021. Then we discuss the areas of concern and the status of efforts to make improvements in these areas.

Karen Baver↗
DOE OSTI2024

An exploration of online-simulation-driven portfolio scheduling in Workflow Management Systems

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use. The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. Here we perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗
NASA NTRS2011

Multi-Objective Scheduling for the Cluster II Constellation

This paper describes the application of the MUSE multiobjecctive scheduling framework to the Cluster II WBD scheduling domain. Cluster II is an ESA four-spacecraft constellation designed to study the plasma environment of the Earth and it's magnetosphere. One of the instruments on each of the four spacecraft is the Wide Band Data (WBD) plasma wave experiment. We have applied the MUSE evolutionary algorithm to the scheduling problem represented by this instrument, and the result has been adopted and utilized by the WBD schedulers for nearly a year. This paper describes the WBD scheduling problem, its representation in MUSE, and some of the visualization elements that provide insight into objective value tradeoffs.

observatory scheduilng↗
NASA NTRS2016

Scheduler Design Criteria: Requirements and Considerations

This presentation covers fundamental requirements and considerations for developing schedulers in airport operations. We first introduce performance and functional requirements for airport surface schedulers. Among various optimization problems in airport operations, we focus on airport surface scheduling problem, including runway and taxiway operations. We then describe a basic methodology for airport surface scheduling such as node-link network model and scheduling algorithms previously developed. Next, we explain how to design a mathematical formulation in more details, which consists of objectives, decision variables, and constraints. Lastly, we review other considerations, including optimization tools, computational performance, and performance metrics for evaluation.

NASA-KAIA/KARI research collaboration↗
NASA NTRS2017

Wheels-Off Time Uncertainty Impact on Benefits of Early Call for Release Scheduling

Arrival traffic scenarios with 808 flights from 173 airports to Houston George Bush International airport are simulated to determine if Call For Release flights can receive a benefit in terms of less delay over other flights by scheduling prior to gate pushback (look-ahead in time) as opposed to at gate pushback. Call for Release flights are departures that require approval from Air Route Traffic Control Center prior to release. Realism is brought to the study by including gate departure delay and taxi-out delay uncertainties for the 77 major U. S. airports. Gate departure delay uncertainty is assumed to increase as a function of look-ahead time. Results show that Call For Release flights from an airport within the freeze horizon (a region surrounding the arrival airport) can get an advantage over other flights to a capacity constrained airport by scheduling prior to gate pushback, provided the wheels-off time uncertainty with respect to schedule is controlled to a small value, such as within a three-minute window. Another finding of the study is that system delay, measured as the sum of arrival delays, is smaller when flights are scheduled in the order of arrival compared to in the order of departure. Because flights from airports within the freeze horizon are scheduled in the order of departure, an increase in the number of internal airports with a larger freeze horizon increases system delay. Delay in the given scenario was found to increase by 126% (from 13.8 hours to 31.2 hours) as freeze horizon was increased from 30-minutes to 2-hours in the baseline scenario.

wheels-off time uncertainty↗
NASA NTRS2018

A Data-Driven Analysis of a Tactical Surface Scheduler

NASA's Airspace Technology Demonstration-2 (ATD-2) integrates arrival, departure, and surface operations to extend integrated traffic sequencing all the way from the gate to the overhead stream and back again for multi-airport, metroplex environments. A key concept of ATD-2 centers on surface scheduling that allows aircraft to taxi, climb, and insert within the overhead stream with minimal interruptions. A core principle is to allow aircraft to absorb delay at the gate prior to engine start in order to reduce overall fuel burn and emissions. To achieve these goals, it is necessary for the scheduler to properly balance the demand at the runway with the available capacity while also predicting accurate takeoff times. This paper provides a data-driven analysis of the runway demand capacity balancing and measures the accuracy of schedules that are generated while running in a live operational environment at the Charlotte Douglas International Airport. We found that using minimum-time wake vortex separation constraints to define runway capacity resulted in scheduling departure operations at a slightly higher rate than the runway was operating and we discovered a surprising relationship between the runway rate and the accuracy of the schedules.

Tactical Surface Scheduler↗
NASA NTRS2018

A Data-Driven Analysis of a Tactical Surface Scheduler

NASA's Airspace Technology Demonstration-2 (ATD-2) integrates arrival, departure, and surface operations to extend integrated traffic sequencing all the way from the gate to the overhead stream and back again for multi-airport, metroplex environments. A key concept of ATD-2 centers on surface scheduling that allows aircraft to taxi, climb, and insert within the overhead stream with minimal interruptions. A core principle is to allow aircraft to absorb delay at the gate prior to engine start in order to reduce overall fuel burn and emissions. To achieve these goals, it is necessary for the scheduler to properly balance the demand at the runway with the available capacity while also predicting accurate takeoff times. This paper provides a data-driven analysis of the runway demand capacity balancing and measures the accuracy of schedules that are generated while running in a live operational environment at the Charlotte Douglas International Airport. We found that using minimum-time wake vortex separation constraints to define runway capacity resulted in scheduling departure operations at a slightly higher rate than the runway was operating and we discovered a surprising relationship between the runway rate and the accuracy of the schedules.

Tactical Surface Scheduler↗
NASA NTRSDate not supplied

Promoting Crew Autonomy in a Human Spaceflight Earth Analog Mission through Self-Scheduling

Deep space exploration missions face the challenge of communication transmission latencies between ground stations and astronaut crews due to increasing distance between the Earth and spacecraft in transit. To address this, research at NASA has aimed toward supporting crew autonomy by enabling astronauts to schedule their own timelines with minimal oversight from Mission Control. While self-scheduling has been shown to be feasible, it is yet to be studied as an integral part of autonomous crew operations. The current paper reviews the operationalization of self-scheduling and a number of related objectives during Campaign 6 of HERA, a Human Exploration Research Analog. Research objectives include studying the effects of phasic autonomy over the course of a 45-day mission, evaluating differences in scheduling performance produced by software interface aids, and deploying a novel measure of crew attitudes toward self-scheduling and plan execution.

crew autonomy↗
NASA NTRSDate not supplied

Promoting Crew Autonomy in a Human Spaceflight Earth Analog Mission through Self-Scheduling

Deep space exploration missions face the challenge of communication transmission latencies between ground stations and astronaut crews due to increasing distance between the Earth and spacecraft in transit. To address this, research at NASA has aimed toward supporting crew autonomy by enabling astronauts to schedule their own timelines with minimal oversight from Mission Control. While self-scheduling has been shown to be feasible, it is yet to be studied as an integral part of autonomous crew operations. The current paper reviews the operationalization of self-scheduling and a number of related objectives during Campaign 6 of HERA, a Human Exploration Research Analog. Research objectives include studying the effects of phasic autonomy over the course of a 45-day mission, evaluating differences in scheduling performance produced by software interface aids, and deploying a novel measure of crew attitudes toward self-scheduling and plan execution.

crew autonomy↗
NASA NTRSDate not supplied

Enhancing Autonomous Satellite Communication Systems with Weather-Aware Scheduling and Reconfiguration

NASA currently provides communication support to over 100 satellite missions, and the agency is driving developments in Ka-band communications and network management automation to support additional future missions. At Ka-band frequencies, rain can degrade a communication link by more than 10 dB, which may be mitigated by agile scheduling and data rate control. We present a weather forecasting module for Ka-band communications that is intended to be used in an autonomous network management service that employs machine-to-machine scheduling systems for dynamic user access opportunities. Our forecasting module (NIMBUS) runs on AWS Cloud, consumes the freely and publicly available NOAA MRMS precipitation rate dataset (1km x 1km x 2-min), produces 30-minute Nowcasts using the pySTEPS algorithm, and publishes high level ground station specific link quality predictions. We evaluate two potential NIMBUS outputs, a binary classifier that predicts rain attenuation greater than 3 dB and a rain attenuation estimator, and we backtest these outputs using one year of power measurement data collected from observations of the geostationary ANIK F2 satellite’s Ka-band beacon. We report, with a 30-minute lead time, a binary classifier accuracy of 84% and an estimator RMSE of 1.67 dB. Additionally, we discuss how the NIMBUS module could be incorporated into a user-initiated service framework to enable weather-aware scheduling and reconfiguration. Operating a static link budget with minimal link margin can increase network operation costs by creating additional scheduling tasks due to failed packets, which may require human intervention and/or lead to inefficient asset utilization. The proposed system aims to increase throughput by reducing link margin, while mitigating increases in network operations costs by leveraging autonomous machine-to-machine scheduling, shortened prediction lead times, and advances in precipitation Nowcasting.

satellite communications↗
DOE OSTI2026

Consumer safety-oriented scheduling of rotating power outages during heat waves

Extreme heat events have widespread effects on power systems, reducing available generation capacity, limiting transmission capabilities, and causing unusual demand patterns on the consumer side. As these combined effects expose bulk transmission systems to potential large-scale blackouts, utilities may be required to schedule and apply rotating outages, by temporarily and alternately disconnecting distribution substations to reduce overload. However, utilities lack mechanisms to inform these events, exacerbating the negative effects of heat waves on affected communities. This paper introduces a novel framework for scheduling rotating outages during heat waves while considering impacts on consumers’ safety. Instead of random sequential load shedding, we propose a methodology to rotate power outages considering a metric that quantifies the indoor overheating risk of groups of consumers during a power outage. The overheating risk is derived from a detailed building simulation using CityBES, where the buildings are modeled based on available data—use type, year built, floor area, number of stories, location—while presence of air conditioning and occupancy are calibrated from smart meter data. Based on the metric, an algorithm to schedule the rotating outages is applied to prioritize feeders for disconnection at each hour according to their overheating risk to meet a utility load reduction target. Applied to two substations and seven feeders in the Portland General Electric territory, the results show that this approach effectively leads to the lowest overheating risk during the resulting outage schedules, with an average 10.1% lower overheating compared to uninformed schedules.

Building thermal simulation↗
DOE OSTI2024

Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tool and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the- art methods.

Guo, Luanzheng [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

IRIS: Exploring Performance Scaling of the Intelligent Runtime System and its Dynamic Scheduling Policies

High-Performance Computing is becoming increasingly heterogeneous, relying on a diverse mix of hardware to achieve good performance. Paradoxically, current drivers and frameworks for these devices typically require separate languages and implementations for each vendor. Furthermore, there are few tools and little support to schedule codes between these devices in a truly heterogeneous manner-partly because of this fragmentation between vendors and the languages each supports. To overcome both limitations, the Intelligent Runtime System (IRIS) was developed. It allows a common task abstraction to automatically be shared among contemporary vendors and is run from a single host-side API. At runtime, IRIS queries the host system and registers which frameworks and drivers are available, these determine which kernels can be used by the scheduler-CPUs via OpenMP, Nvidia GPUs (CUDA), AMD GPUs (HIP), and Intel and Xilinx FPGAs with OpenCL. IRIS enables tasks to be scheduled to any heterogeneous device and resolves to the appropriate kernel binary at runtimeit only uses the devices supported by the system on which it is run. IRIS supports single-task and graph-based expressions of dependencies of tasks. Additionally, IRIS features a range of dynamic scheduling policies, allowing complex chains of tasks and interactions to be executed, relieving the programmer/user from considering the system to assign tasks to devices optimally. This paper presents the peak performance attainable by IRIS over a range of systems-each with different numbers and types of accelerator devices, it highlights the flexibility of IRIS since these devices are truly heterogeneous, relying on different backends (drivers, frameworks, and languages) which historically required unique implementations to utilize them. We then use this peak performance as a baseline to compare increasingly complex chains of tasks (with increasingly complex task dependencies) and evaluate how IRIS copes. Finally, we consider the performance of different IRIS scheduling policies on this range of task graphs.

Johnston, Beau↗
DOE OSTI2025

Evaluating HPC Scheduling Strategies for Urgent Workloads

Scientific computing centers increasingly face workloads with diverse urgency requirements, driven by applications that demand rapid or even immediate execution. Appropriately configured scheduling policies can significantly improve both user satisfaction and overall cluster utilization. In this work, we present a systematic analysis of scheduler configurations under scenarios where a fraction of jobs have urgent computing needs. We evaluate multiple job scheduling simulators, develop a lightweight job-submission emulation framework, and create tools to analyze and visualize the resulting scheduling data. Our study identifies key trade-offs between responsiveness, fairness, and efficiency, and offers a set of practical scheduling configurations (particularly for Slurm) that can be tailored to HPC environments supporting mixed-urgency workloads.

Maheshwari, Ketan [ORNL] (ORCID:000000033800662X)↗
NASA NTRS1976

Aspects of job scheduling

A mathematical model for job scheduling in a specified context is presented. The model uses both linear programming and combinatorial methods. While designed with a view toward optimization of scheduling of facility and plant operations at the Deep Space Communications Complex, the context is sufficiently general to be widely applicable. The general scheduling problem including options for scheduling objectives is discussed and fundamental parameters identified. Mathematical algorithms for partitioning problems germane to scheduling are presented.

Phillips, K.↗
NASA NTRS1982

An assessment of PERT as a technique for schedule planning and control

The PERT technique including the types of reports which can be computer generated using the NASA/LaRC PPARS System is described. An assessment is made of the effectiveness of PERT on various types of efforts as well as for specific purposes, namely, schedule planning, schedule analysis, schedule control, monitoring contractor schedule performance, and management reporting. This assessment is based primarily on the author's knowledge of the usage of PERT by NASA/LaRC personnel since the early 1960's. Both strengths and weaknesses of the technique for various applications are discussed. It is intended to serve as a reference guide for personnel performing project planning and control functions and technical personnel whose responsibilities either include schedule planning and control or require a general knowledge of the subject.

Sibbers, C. W.↗
NASA NTRS1987

Scheduling real-time, periodic jobs using imprecise results

A process is called a monotone process if the accuracy of its intermediate results is non-decreasing as more time is spent to obtain the result. The result produced by a monotone process upon its normal termination is the desired result; the error in this result is zero. External events such as timeouts or crashes may cause the process to terminate prematurely. If the intermediate result produced by the process upon its premature termination is saved and made available, the application may still find the result unusable and, hence, acceptable; such a result is said to be an imprecise one. The error in an imprecise result is nonzero. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. This problem differs from the traditional scheduling problems since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result. Consequently, the amounts of processor time assigned to tasks in a valid schedule can be less than the amounts of time required to complete the tasks. A meaningful formulation of this problem taking into account the quality of the overall result is discussed. Three algorithms for scheduling jobs for which the effects of errors in results produced in different periods are not cumulative are described, and their relative merits are evaluated.

Liu, Jane W. S.↗