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The paper discusses the applications of 'Dynamic Scheduling' technique, which has been invented for the scheduling of Flexible Manufacturing System, to two space related scheduling problems: operation scheduling of a future space transportation system, and resource allocation in a space system with limited resources such as space station or space shuttle.
Deep Space Network scheduling program allocates resources based on the users requirements. The system reviews and allocates the requests for equipment and resources. Depending upon the program input either the seven day or the twelve week schedule is generated.
Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).
Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).
Two hypotheses concerning the way in which short-term memory interacts with another task in a dual task situation are considered. It is noted that when two tasks are combined, the activity of controlling and organizing performance on both tasks simultaneously may compete with either task for a resource; this resource may be space in a central mechanism or general processing capacity or it may be some task-specific resource. If a special relationship exists between short-term memory and control, especially if there is an identity relationship between short-term and a central controlling mechanism, then short-term memory performance should show a decrement in a dual task situation. Even if short-term memory does not have any particular identity with a controlling mechanism, but both tasks draw on some common resource or resources, then a tradeoff between the two tasks in allocating resources is possible and could be reflected in performance. The persistent concurrence cost in memory performance in these experiments suggests that short-term memory may have a unique status in the information processing system.
Space Technology 5 (ST-5) is a three-satellite constellation, technology validation mission under the New Millennium Program at NASA to be launched in March 2006. One of the key technologies to be validated is a lights-out, model-based operations approach to be used for one week to control the ST-5 constellation with no manual intervention. The ground architecture features the GSFC Mission Services Evolution Center (GMSEC) middleware, which allows easy plugging in of software components and a standardized messaging protocol over a software bus. A predictive modeling tool built on MatLab's Simulink software package makes use of the GMSEC standard messaging protocol to interface to the Advanced Mission Planning System (AMPS) Scenario Scheduler which controls all activities, resource allocation and real-time re-profiling of constellation resources when non-nominal events occur. The key features of this system, which we refer to as the ST-5 Simulink system, are as follows: Original daily plan is checked to make sure that predicted resources needed are available by comparing the plan against the model. As the plan is run in real-time, the system re-profiles future activities in real-time if planned activities do not occur in the predicted timeframe or fashion. Alert messages are sent out on the GMSEC bus by the system if future predicted problems are detected. This will allow the Scenario Scheduler to correct the situation before the problem happens. The predictive model is evolved automatically over time via telemetry updates thus reducing the cost of implementing and maintaining the models by an order of magnitude from previous efforts at GSFC such as the model-based system built for MAP in the mid-1990's. This paper will describe the key features, lessons learned and implications for future missions once this system is successfully validated on-orbit in 2006.
System-level tools and methodologies associated with an integrated approach to the development of multiprocessor systems are examined. Tools for capturing initial program structure, automated program partitioning, automated resource allocation, and high-level modeling of the combined application and resource are discussed. The primary language focus of the current implementation is Ada, although the techniques should be appropriate for other programming paradigms.
Space Station Freedom (SSF) is designed to be an Earth orbiting multidisciplinary R&D facility capable of evolving to accommodate a variety of potential uses. In order to identify SSF evolution requirement and define potential growth configurations, NASA-Langley is analyzing user resource requirements for the post-PMC time frame. The analysis goal is to define resource levels, including crew, power, and volume, which allow full utilization of SSF capabilities commensurate with minimum essential user requirements. Multiple scenarios were studied including core R&D and combined SEI plus R&D utilization. An analysis is presented of a core R&D utilization scenario. Included are discussions of resource allocation assumptions for specific R&D disciplines, user requirements trends, and growth resource projections. These preliminary results show total resource requirements of 13 crew, 150 kW power, and additional lab volume equivalent to a second U.S. lab module. Additionally, orthogonal growth structure was identified as required to support SSF systems and users.
The Life Support Flight Program is evaluating regenerative technologies, including those that utilize higher plants, as a means to reduce resupply over long duration space missions. Constructed to assist in the evaluation process is the CELSS Test Facility Engineering Development Unit (CTF-EDU) an environmentally closed (less than 1% mass and thermal leakage) technology test bed. This ground based fully functional prototype is currently configured to support crop growth, utilizing the power, volume and mass resources allocated for two space station racks. Sub-system technologies were selected considering their impact on available resources, their ability to minimize integration issues, and their degree of modularity. Gas specific mass handling is a key sub-system technology for both biological and physical/chemical life support technologies. The CTF-EDU requires such a system to accommodate non-linear oxygen production from crops, by enabling the control system to change and sustain partial pressure set points in the growth volume. Electrochemical cells are one of the technologies that were examined for oxygen handling in the CTF-EDU. They have been additionally considered to meet other regenerative life support functions, such as oxygen generation, the production of potable water from composite waste streams, and for having the potential to integrate life support functions with those of propulsion and energy storage. An oxygen removal system based on an electrochemical cell was chosen for the EDU due to it's low power, volume and mass requirements (10W, 0.000027 cu m, 4.5 kg) and because of the minimal number of integration considerations. Unlike it's competitors, the system doesn't require post treatments of its byproducts, or heat and power intensive regenerations, that also mandate system redundancy or cycling. The EDUs oxygen removal system only requires two resources, which are already essential to controlled plant growth: electricity and water. Additionally, the amount of oxygen that is removed from the EDU is directly proportional to the cell input current via Faraday's constant, potentially allowing for a mol/electron measurement of photosynthetic rate. The currently operative oxygen removal system has maintained reduced oxygen set points within the EDU, and preparation is underway to verify of the accuracy of electrochemical measurement of oxygen production and hence, photosynthesis. This paper examines the working principles of the electrochemical cell, outlines the overall design of the oxygen removal system and its integration with other EDU subsystems, and summarizes test results obtained over crop growth cycles in the CTF-EDU.
In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.
The Ground Resource Allocation and Planning Environment (GRAPE 1.0) is a Web-based, collaborative team environment based on the Microsoft SharePoint platform, which provides Deep Space Network (DSN) resource planners tools and services for sharing information and performing analysis.
The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.
Interactive Manpower Allocation and Reporting System (MARS) helps planners make manpower allocation decisions. Includes provisions to enter overall constraints for projects and persons, assign individuals to projects, compute automatically overhead category, report on deviations from constraints, and generate manpower resource allocation reports.
The Program Management Tool (PMT) is a comprehensive, Web-enabled business intelligence software tool for assisting program and project managers within NASA enterprises in gathering, comprehending, and disseminating information on the progress of their programs and projects. The PMT provides planning and management support for implementing NASA programmatic and project management processes and requirements. It provides an online environment for program and line management to develop, communicate, and manage their programs, projects, and tasks in a comprehensive tool suite. The information managed by use of the PMT can include monthly reports as well as data on goals, deliverables, milestones, business processes, personnel, task plans, monthly reports, and budgetary allocations. The PMT provides an intuitive and enhanced Web interface to automate the tedious process of gathering and sharing monthly progress reports, task plans, financial data, and other information on project resources based on technical, schedule, budget, and management criteria and merits. The PMT is consistent with the latest Web standards and software practices, including the use of Extensible Markup Language (XML) for exchanging data and the WebDAV (Web Distributed Authoring and Versioning) protocol for collaborative management of documents. The PMT provides graphical displays of resource allocations in the form of bar and pie charts using Microsoft Excel Visual Basic for Application (VBA) libraries. The PMT has an extensible architecture that enables integration of PMT with other strategic-information software systems, including, for example, the Erasmus reporting system, now part of the NASA Integrated Enterprise Management Program (IEMP) tool suite, at NASA Marshall Space Flight Center (MSFC). The PMT data architecture provides automated and extensive software interfaces and reports to various strategic information systems to eliminate duplicative human entries and minimize data integrity issues among various NASA systems that impact schedules and planning.
Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.
Following the end of the Angolan Civil War (1975-2002), human habitation in Iona National Park has grown exponentially, as has the livestock population. An ongoing drought beginning in 2017 has brought people, livestock, and wildlife into increasing competition for resources within the park. This study used Earth observation data, primarily Landsat and Sentinel imagery, to examine landscape trends to improve wildlife preservation approaches in Iona National Park, Angola. In collaboration with the NGO African Parks, we developed a robust land use and land cover (LULC) classification model using remote sensing data to augment sparse ground-based data in this arid land region. We used Google Earth Engine and a random forest classifier to map vegetation types, water bodies, and potential wildlife habitats. This analysis resulted in a high spatial resolution LULC time-series between 1984-2023, highlighting critical periods of socioecological change over the past 40 years. These results increased the partner’s ability to make scientifically grounded decisions about resource allocation and conservation priorities. This analysis supports the feasibility of applying remote sensing techniques coupled with machine learning models in dry regions, where standard survey methods are frequently limited by accessibility and resource availability. However, we identified limitations in ground-truth data and the difficulty of recognizing certain vegetation types in arid areas. Despite these limitations, the study demonstrated Earth observations' ability to transform wildlife management techniques in distant and data-scarce locations, providing a reproducible foundation for similar ecosystems around the world.
This document presents the implementation plan for Ames Research Center (ARC) within the overall framework of the NASA Strategic Plan. It describes how ARC intends to implement its Center of Excellence responsibilities, Agency assigned missions, Agency and Enterprise lead programs, and other roles in support of NASA's vision and mission. All Federal agencies are required by the 1993 Government Performance and Results Act to implement a long-term strategic planning process that includes measurable outcomes and strict accountability. At NASA, this planning process is shaped by the Space Act of 1958, annual appropriations, and other external mandates, as well as by customer requirements. The resulting Strategic Plan sets the overall architecture for what we do, identifies who our customers are, and directs where we are going and why. The Strategic Plan is the basis upon which decisions regarding program implementation and resource deployment are made. Whereas the strategic planning process examines the long-term direction of the organization and identifies a specific set of goals, the implementation planning process examines the detailed performance of the organization and allocates resources toward meeting these goals. It is the purpose of this implementation document to provide the connection between the NASA Strategic Plan and the specific programs and support functions that ARC employees perform. This connection flows from the NASA Strategic Plan, through the various Strategic Enterprise plans to the ARC Center of Excellence, primary missions, Lead Center programs, program support responsibilities, and ultimately, to the role of the individual ARC employee.
The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.