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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Computing the Envelope for Stepwise Constant Resource Allocations

Estimating tight resource level is a fundamental problem in the construction of flexible plans with resource utilization. In this paper we describe an efficient algorithm that builds a resource envelope, the tightest possible such bound. The algorithm is based on transforming the temporal network of resource consuming and producing events into a flow network with noises equal to the events and edges equal to the necessary predecessor links between events. The incremental solution of a staged maximum flow problem on the network is then used to compute the time of occurrence and the height of each step of the resource envelope profile. The staged algorithm has the same computational complexity of solving a maximum flow problem on the entire flow network. This makes this method computationally feasible for use in the inner loop of search-based scheduling algorithms.

Muscettola, Nicola↗
NASA NTRS2020

Modeling Uncertainty in Large Natural Resource Allocation Problems

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.

Cai, Yongyang↗
NASA NTRS1990

Neural-Network Processor Would Allocate Resources

Global optimization problems solved quickly. Neural-network processor optimizes allocation of M resources among N expenditures according to cost of pairing each resource with each expenditure and subject to limit on number of resources feeding into each expenditure and/or limit on number of expenditures to which each resource allocated. One cell performs several analog and digital functions. Potential applications include assignment of jobs, scheduling, dispatching, and planning of military maneuvers.

Eberhardt, Silvio P.↗
NASA NTRS1998

Probabilistic resource allocation system with self-adaptive capability

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and weighted links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Weights are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Yufik, Yan M.↗
NASA NTRS1996

Probabilistic resource allocation system with self-adaptive capability

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and directed links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Reliability values are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Yufik, Yan M.↗
NASA NTRS1994

Method for optimizing resource allocation in a government organization

The managers in Federal agencies are challenged to control the extensive activities in government and still provide high-quality products and services to the American taxpayers. Considering today's complex social and economic environment and the $3.8 billion daily cost of operating the Federal Government, it is evident that there is a need to develop decision-making tools for accurate resource allocation and total quality management. The goal of this thesis is to provide a methodical process that will aid managers in Federal Government to make budgetary decisions based on the cost of services, the agency's objectives, and the customers' perception of the agency's product. A general resource allocation procedure was developed in this study that can be applied to any government organization. A government organization, hereafter the 'organization,' is assumed to be a multidivision enterprise. This procedure was applied to a small organization for the proof of the concept. This organization is the Technical Services Directorate (TSD) at the NASA Lewis Research Center in Cleveland, Ohio. As part of the procedure, a nonlinear programming model was developed to account for the resources of the organization, the outputs produced by the organization, the decision-maker's views, and the customers' satisfaction with the organization. The information on the resources of the organization was acquired from current budget levels of the organization and the human resources assigned to the divisions. The outputs of the organization were defined and measured by identifying metrics that assess the outputs, the most challenging task in this study. The decision-maker's views are represented in the model as weights assigned to the various outputs and were quantified by using the analytic hierarchy process. The customer's opinions regarding the outputs of the organization were collected through questionnaires that were designed for each division individually. Following the philosophy of total quality management, information on customers' satisfaction is presented in the model as the quality of output. The model is a nonlinear one whose objective is to maximize customers' satisfaction such that the total cost of operation does not exceed the organization's budget. This model represents a structured approach or policy mechanism, at the agency level, to make capital investment decisions based on the priorities of the agency and the quality of outputs. This procedure applied to TSD resulted in a resources allocation scheme that was reasonable and acceptable to the decision-makers and, as expected, dependent on the assumptions and accuracy of the data used in the model.

Afarin, James↗
DOE OSTI2022

Single Carrier Modulation Resource Allocation for Massive MIMO with Virtual Antennas

Today’s Massive MIMO cellular operation is dominated by orthogonal frequency division multiplexing (OFDM) modulation. One of the advantages of OFDM is the flexibility to carve up the available spectrum into resource blocks (RBs) that can operate adjacent to one another. Massive MIMO adds spatial multiplexing layers on top of the RBs, enabling the simultaneous operation of dozens of UEs. Cyclic prefixed single carrier modulation (CP-SCM) is an alternative to OFDM that also benefits from massive MIMO and is useful as an OFDM alternative in specific scenarios. However, until recently, no resource allocation method has been proposed for CP-SCM. In this paper, we present a CP-SCM resource allocation scheme using virtual antennas in a massive MIMO time domain duplexed scenario. By creating several virtual antennas for each physical antenna, the benefits of massive MIMO are achieved with a smaller physical antenna count. Resources are quantized into data streams, and each user can be assigned a variable number of simultaneous streams. This paper presents a detailed development and analysis of multi-stream processing (MSP) for both uplink detection and downlink precoding. We also introduce heterogeneous MSP, where CP-SCM and OFDM signals can be processed in the same MSP framework.

5G and beyond communications↗
NASA NTRS1990

Space Station Freedom resource allocation accommodation of technology payload requirements

An overview of the Office of Aeronautics, Exploration, and Technology (OAET) Space Station Freedom Technology Payload Development Program is provided, and the OAET Station resource requirements are reviewed. The requirements are contrasted with current proposed resource allocations. A discussion of the issues and conclusions are provided. It is concluded that an overall 20 percent resource allocation is appropriate to support OAET's technology development program, that some resources are inadequate even at the 20 percent level, and that bartering resources among U.S. users and international partners and increasing the level of automation may be viable solutions to the resource constraint problem.

Avery, Don E.↗
NASA NTRS2002

The JPL Resource Allocation Planning and Scheduling Office (RAPSO) process

The Jet Propulsion Laboratory's Resource Allocation Planning and Scheduling Office is chartered to divide the limited amount of tracking hours of the Deep Space Network amongst the various missions in as equitable allotment as can be achieved. To best deal with this division of assets and time, an interactive process has evolved that promotes discussion with agreement by consensus between all of the customers that use the Deep Space Network (DSN). Aided by a suite of tools, the task of division of asset time is then performed in three stages of granularity. Using this approach, DSN loads are either forecasted or scheduled throughout a moving 10-year window.

RAPSO Resource Allocation Planning and Scheduling ↗
NASA NTRS1991

Space Network Control Conference on Resource Allocation Concepts and Approaches

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Moe, Karen L.↗
NASA NTRS2004

Updated System-Availability and Resource-Allocation Program

A second version of the Availability, Cost and Resource Allocation (ACARA) computer program has become available. The first version was reported in an earlier tech brief. To recapitulate: ACARA analyzes the availability, mean-time-between-failures of components, life-cycle costs, and scheduling of resources of a complex system of equipment. ACARA uses a statistical Monte Carlo method to simulate the failure and repair of components while complying with user-specified constraints on spare parts and resources. ACARA evaluates the performance of the system on the basis of a mathematical model developed from a block-diagram representation. The previous version utilized the MS-DOS operating system and could not be run by use of the most recent versions of the Windows operating system. The current version incorporates the algorithms of the previous version but is compatible with Windows and utilizes menus and a file-management approach typical of Windows-based software.

Viterna, Larry↗
NASA NTRS1990

Resource Allocation Planning Helper (RALPH): Lessons learned

The current task of Resource Allocation Process includes the planning and apportionment of JPL's Ground Data System composed of the Deep Space Network and Mission Control and Computing Center facilities. The addition of the data driven, rule based planning system, RALPH, has expanded the planning horizon from 8 weeks to 10 years and has resulted in large labor savings. Use of the system has also resulted in important improvements in science return through enhanced resource utilization. In addition, RALPH has been instrumental in supporting rapid turn around for an increased volume of special what if studies. The status of RALPH is briefly reviewed and important lessons learned from the creation of an highly functional design team are focused on through an evolutionary design and implementation period in which an AI shell was selected, prototyped, and ultimately abandoned, and through the fundamental changes to the very process that spawned the tool kit. Principal topics include proper integration of software tools within the planning environment, transition from prototype to delivered to delivered software, changes in the planning methodology as a result of evolving software capabilities and creation of the ability to develop and process generic requirements to allow planning flexibility.

Durham, Ralph↗
NASA NTRSDate not supplied

Joint Communication Resource Allocation and Velocity Selection in Urban Air Mobility via Multi-agent Reinforcement Learning

With traffic congestion problems becoming more severe in urban areas, the National Aeronautics and Space Administration promotes the Urban Air Mobility (UAM) concept, which envisages a safe and efficient air transportation system. However, the increased communication demands in UAM can exacerbate the spectrum scarcity. Therefore, a new communication resource allocation solution is necessary. In this paper, we focus on uplink UAM communications, where multiple aerial vehicles (AV) perform cargo/passenger delivery tasks. With predefined flight paths, AVs make decisions on communication resource allocation and velocity selection to complete their missions under safety constraints. Accordingly, we formulate a joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. We first model the optimization problem as a Markov game and propose a multi-agent reinforcement learning based solution. Simulation results corroborate the effectiveness of the proposed solution.

Ruixuan Han↗
NASA NTRS1991

Electronic neural network for dynamic resource allocation

A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.

Thakoor, A. P.↗
NASA NTRS1994

Low-Earth-Orbiter resource allocation and capacity planning for the DSN using LEO4CAST

The Deep Space Network provides tracking and communication services for a number of U.S. and international low-Earth-orbiting (LEO) and near-Earth missions. This service is supplied by the 26-m subnet (located at each of the DSN complexes), the 9-m and the 34-m Antenna Research System antennas at Goldstone, and the 11-m antennas (following the orbital VLBI mission). An increasing number of LEO missions are planned for DSN support, which will result in increasingly complex ground resource allocation and mission support trades. To support TDA decision making on mission support and cost-effective ground system evolution for this 26-m subnet, LEO4CAST has been developed. LEO4CAST is a tool that uses statistical approaches to provide useful information for long-term ground system capacity planning and near-term resource allocation (prior to detailed time-of-day scheduling). LEO4CAST is currently beta-test software and is being exercised by both the Office of Telecommunications and Data Acquisition (TDA) and the JPL Systems Division.

Fox, G.↗
NASA NTRS1991

Dynamic resource allocation scheme for distributed heterogeneous computer systems

This invention relates to a resource allocation in computer systems, and more particularly, to a method and associated apparatus for shortening response time and improving efficiency of a heterogeneous distributed networked computer system by reallocating the jobs queued up for busy nodes to idle, or less-busy nodes. In accordance with the algorithm (SIDA for short), the load-sharing is initiated by the server device in a manner such that extra overhead in not imposed on the system during heavily-loaded conditions. The algorithm employed in the present invention uses a dual-mode, server-initiated approach. Jobs are transferred from heavily burdened nodes (i.e., over a high threshold limit) to low burdened nodes at the initiation of the receiving node when: (1) a job finishes at a node which is burdened below a pre-established threshold level, or (2) a node is idle for a period of time as established by a wakeup timer at the node. The invention uses a combination of the local queue length and the local service rate ratio at each node as the workload indicator.

Liu, Howard T.↗