Augmenting Data Systems with Prediction Based Embeddings
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Design-Aids for Real-Time Systems (DARTS) is a tool that assists in defining embedded computer systems through tree structured graphics, military standard documentation support, and various analyses including automated Software Science parameter counting and metrics calculation. These analyses provide both static and dynamic design quality feedback which can potentially aid in producing efficient, high quality software systems.
Each RAID level reflects a different design architecture. Associated with each is a backdrop of imposed limitations, as well as possibilities which may be exploited within the architectural constraints of that level. There are three unique features that differentiate RAID 7 from all other levels. RAID 7 is asynchronous with respect to usage of I/O data paths. Each I/O drive (includes all data and one parity drives) as well as each host interface (there may be multiple host interfaces) has independent control and data paths. This means that each can be accessed completely, independently, of the other. This is facilitated by a separate device cache for each device/interface as well. RAID 7 is asynchronous with respect to device hierarchy and data bus utilization. Each drive and each interface is connected to a high speed data bus controlled by the embedded operating system to make independent transfers to and from central cache. RAID 7 is asynchronous with respect to the operation of an embedded real time process oriented operating system. This means that exclusive and independent of the host, or multiple host paths, the embedded OS manages all I/O transfers asynchronously across the data and parity drives. A key factor to consider is that of the RAID 7's ability to anticipate and match host I/O usage patterns. This yields the following benefits over RAID's built around micro-code based architectures. RAID 7 appears to the host as a normally connected Big Fast Disk (BFD). RAID 7 appears, from the perspective of the individual disk devices, to minimize the total number of accesses and optimize read/write transfer requests. RAID 7 smoothly integrates the random demands of independent users with the principles of spatial and temporal locality. This optimizes small, large, and time sequenced I/O requests which results in users having an I/O performance which approaches performance to that of main memory.
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Cyber-physical systems (CPS) are highly susceptible to malicious attacks due to their complex dynamics and interconnectivity. A comprehensive understanding of their vulnerabilities is essential for designing effective resilience measures. This paper presents a data-driven attack generative system for evaluating the vulnerability of CPS. The proposed approach formulates the vulnerability assessment problem as determining the feasibility of a specific attack set based on two boundary functions that represent the effectiveness and stealthiness of attacks. The attack generative model is trained using a custom loss function, with two universal approximators designed to learn the effectiveness and stealthiness functions simultaneously. Theoretical results for successful generation and asymptotic convergence of the resulting training algorithm are given. As a result, the proposed approach is evaluated via numerical simulation of an IEEE 14-bus system and gas pipeline systems, demonstrating its viability in learning how to attack nonlinear CPS and identify potential vulnerabilities.
This thesis presents a method for conducting hierarchical simulations to assess system hardware and software dependability. The method is intended to model embedded microprocessor systems. A key contribution of the thesis is the idea of using fault dictionaries to propagate fault effects upward from the level of abstraction where a fault model is assumed to the system level where the ultimate impact of the fault is observed. A second important contribution is the analysis of the software behavior under faults as well as the hardware behavior. The simulation method is demonstrated and validated in four case studies analyzing Myrinet, a commercial, high-speed networking system. One key result from the case studies shows that the simulation method predicts the same fault impact 87.5% of the time as is obtained by similar fault injections into a real Myrinet system. Reasons for the remaining discrepancy are examined in the thesis. A second key result shows the reduction in the number of simulations needed due to the fault dictionary method. In one case study, 500 faults were injected at the chip level, but only 255 propagated to the system level. Of these 255 faults, 110 shared identical fault dictionary entries at the system level and so did not need to be resimulated. The necessary number of system-level simulations was therefore reduced from 500 to 145. Finally, the case studies show how the simulation method can be used to improve the dependability of the target system. The simulation analysis was used to add recovery to the target software for the most common fault propagation mechanisms that would cause the software to hang. After the modification, the number of hangs was reduced by 60% for fault injections into the real system.
Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)—are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool.
The data requirements of mission critical defense systems have been increasing dramatically. Command and control, intelligence, logistics, and even weapons systems are being required to integrate, process, and share ever increasing volumes of information. To meet this need, systems are now being specified that incorporate data base management subsystems for handling storage and retrieval of information. It is expected that a large number of the next generation of mission critical systems will contain embedded data base management systems. Since the use of Ada has been mandated for most of these systems, it is important to address the issues of providing data base management capabilities that can be closely coupled with Ada. A comprehensive distributed data base management project has been investigated. The key deliverables of this project are three closely related prototype systems implemented in Ada. These three systems are discussed.
With the sharp increasing volume of user data, Deep Learning Recommendation Model (DLRM) becomes an indispensable infrastructure in large technology companies. However, large-scale DLRM on the multi-GPU platform is still inefficient due to unbalanced workload partitioning and intensive inter-GPU communication. To this end, we propose OPER, an OPtimality guided Embedding table placement for large-scale Recommendation model training and inference. OPER explores the potential of mitigating remote memory access latency in DLRM through fine-grained embedding table placement. Specifically, OPER proposes a theoretical modeling that builds up the relationship between EMT placement and the embedding communication latency in both training and inference. OPER proves the NP hardness of finding the optimal embedding table placement and proposes a heuristic algorithm that yields near optimal placement. OPER implements a SHMEM-based embedding table training system and a unified embedding index mapping to support fine-grained embedding table sharding and placement. Comprehensive experiments reveal that OPER achieves on average 3.4× and 5.1× speedup on training and inference respectively over state-of-the-art DLRM frameworks.
The concept of reliability in systems theory is discussed. Emphasis is placed on identifying failed or degraded systems and constructing a strategy to control the system in the failed mode. Systems are assumed to be linear and transformations into the failed mode occur smoothly. The family into which a nominal system is embedded is examined along with the relations within the family. The class of systems into which a given system can be algebraically transformed is investigated.
NASA Spacelink is a comprehensive electronic data base of NASA and other source educational and informational materials. This service originates at Marshall Space Flight Center (MSFC) in Huntsville, Alabama. This is an education service of NASA Headquarters, through the MSFC Education Office, that first began in February of 1988. The new NASA Spacelink Public Electronic Library was the result of a study conducted to investigate an upgrade or redesign of the original NASA Spacelink. The UNIX Operating System was chosen to be the host operating system for the new NASA Spacelink Public Electronic Library. The UNIX system was selected for this project because of the strengths built into the embedded communication system and for its simple and direct file handling capabilities. The host hardware of the new system is a Sun Microsystems SPARCserver 1000 computer system. The configuration has four 50-MHz SuperSPARC processors with 128 megabytes of shared memory; three SB800 serial ports allowing 24 cable links for phone communications; 4.1 gigabytes of on-line disk storage; and ten (10) CD-ROM drives. Communications devices on the system are sufficient to support the expected number of users through the Internet, the local dial services, long distance dial services; the MSFC PABX, and the NPSS (NASA Packet Switching System) and 1-800 access service for the registered teachers.
A Probabilistic Risk Assessment (PRA) aims to identify and assess potential risks to system technical performance requirements for the purpose of furnishing risk insights into project decisions. PRAs have traditionally been conducted manually using software with an isolated data model. As system complexity rises it becomes difficult to ensure consistency between a PRA, the evolving system design, and other engineering analyses; the techniques for conducting PRAs must evolve to meet this challenge. This work presents progress towards a methodology and tooling for conducting a PRA by leveraging data in the system model, embedded for other purposes and analyses, to conduct a PRA. An approach for identifying the appropriate probabilistic equation for each risk scenario from a standard library is presented, which is a significant step towards the quantification of the likelihood of a risk scenario occurrence. The final calculation of the likelihood of occurrence is left as an item of future work. We also present the development of preliminary tooling to carry out the methodology on a well-formed system model. The information needed to conduct the PRA is embedded in a consistent manner in a system model, so the model-based PRA can be regularly executed as the system model changes. The ability to modify the PRA in concert with lifecycle evolution affords a project the opportunity to track the extent to which system modification impacts compliance with requirements. These aspects of this model-based PRA methodology make it capable of managing risk in increasingly complex technical systems.
Projection‐based embedding theory (PBET) is used to calculate and assess the challenging spin‐crossover energies for a selection of small Fe‐containing systems by embedding the metal center into the frozen potential of the ligands. MP2, CCSD, and CCSD(T) are embedded in potentials from the SCAN and r 2 SCAN functionals and compared with the canonical values for the constituent methods and previously reported reference values. Considering the PBET calculations as a correction for the underlying DFT, the embedding calculations are able to provided improvement for most cases. In some cases, the PBET methods are able to compensate for limitations in the wave function methods and produce results similar to more rigorous calculations from the literature. For the systems with spin‐crossover energies near zero, the current methodology fails to provide consistent improvement. In conclusion, the isolated recalculation of the electronic structure around the metal center when embedded into a DFT treatment of the ligand field shows promise as a pragmatic and lower cost treatment compared to the canonical treatment of the whole system of the difficult class of spin‐crossover complexes.
An Intelligent Tutoring System (ITS) transcends conventional computer-based instruction. An ITS is capable of monitoring and understanding student performance thereby providing feedback, explanation, and remediation. This is accomplished by including models of the student, the instructor, and the expert technician or operator in the domain of interest. The space shuttle fuel cell is the technical domain for the project described below. One system, Microcomputer Intelligence for Technical Training (MITT), demonstrates that ITS's can be developed and delivered, with a reasonable amount of effort and in a short period of time, on a microcomputer. The MITT system capitalizes on the diagnostic training approach called Framework for Aiding the Understanding of Logical Troubleshooting (FAULT) (Johnson, 1987). The system's embedded procedural expert was developed with NASA's C-Language Integrated Production (CLIP) expert system shell (Cubert, 1987).
The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.
An Ada-Based Expert System Building Tool Design Research Project was conducted. The goal was to investigate various issues in the context of the design of an Ada-based expert system building tool. An attempt was made to achieve a comprehensive understanding of the potential for embedding expert systems in Ada systems for eventual application in future projects. The current status of the project is described by introducing an operational prototype, ART/Ada. How the project was conducted is explained. The performance of the prototype is analyzed and compared with other related works. Future research directions are suggested.
Six common issues that are encountered in the development of software for embedded computer systems are discussed from the perspective of their interrelationships with the development process and/or the system itself. Particular attention is given to concurrent hardware/software development, prototyping, the inaccessibility of the operational system, fault tolerance, the long life cycle, and inheritance. It is noted that the life cycle for embedded software must include elements beyond simply the specification and implementation of the target software.
Design and optimization is substantially more complex in multidisciplinary and large-scale engineering applications due to the existing inherently coupled interactions. The paper introduces a quasi-procedural methodology for multidisciplinary optimization that is applicable for nonhierarchic systems. The necessary decision-making support for the design process is provided by means of an embedded expert systems capability. The method employs a decomposition approach whose modularity allows for implementation of specialized methods for analysis and optimization within disciplines.