Real time analysis and priority scheduler generation for hardware-software systems with a synthesized run-time system

In this paper, a scheduling algorithm for multi-processor real-time systems with relative timing constraints and resource constraints is proposed. The proposed algorithm has the capability to satisfy any timing constraints expressed as inequalities in two variables and a constant. This implies that scheduling problems imposed on various timing constraints such as end-to-end timing constraints and/or relative timing constraints, which are known to be difficult to handle, can be solved. Since the problem addressed in this paper is a generalization of some NP-complete problems, it is difficult to solve efficiently. Thus, the goal of the authors is to derive a heuristic algorithm which performs well on many instances of the problem, although in some cases it will take enormous space and time. Experimental results show that the proposed algorithm achieves a success rate of 80% in 3 minutes of computation time.

Traditional scheduling models assume that the execution time of a job in a periodic job-set is constant in every instance of its execution. This assumption does not hold in real-time systems wherein job execution time is known to vary. A second feature of traditional models is their lack of expressiveness, in that constraints more complex than precedence constraints (for instance, relative timing constraints) cannot be modeled. Thirdly, the schedulability of a real-time system depends upon the degree of clairvoyance afforded to the dispatcher. In this paper, we shall discuss Totally Clairvoyant Scheduling, as modeled within the E-T-C scheduling framework [Sub05]. We show that this instantiation of the scheduling framework captures the central issues in a real-time flow-shop scheduling problem and devise a polynomial time sequential algorithm for the same. The design of the polynomial time algorithm involves the development of a new technique, which we term Mutable Dynamic Programming. We expect that this technique will find applications in other areas of system design, such as Validation and Software Verification.

Allocation problem has always been one of the fundamental issues of building the applications in real-time computing systems. For real-time applications, the allocation problem should directly address the issues of task and communication scheduling. In this context, the allocation of tasks has to fully utilize the available processors and the scheduling of tasks has to meet the specified timing constraints. Clearly, the execution of tasks under the allocation and schedule has to satisfy the precedence, resources, and other synchronization constraints among them. Recently, the timing requirements of the real-time systems emerge that the relative timing constraints are imposed on the consecutive executions of each task and the inter-task temporal relationships are specified across task periods. In this paper we consider the allocation and scheduling problem of the periodic tasks with such timing requirements. Given a set of periodic tasks, we consider the least common multiple (LCM) of the task periods. Each task is extended to several instances within the LCM. The scheduling window for each task instance is derived to satisfy the timing constraints. We develop a simulated annealing algorithm as the overall control algorithm. An example problem of the sanitized version of the Boeing 777 Aircraft Information Management System is solved by the algorithm. Experimental results show that the algorithm solves the problem in a reasonable time complexity.

High efficient task scheduling algorithms can greatly influence the performance of reconfigurable systems. This paper analyzed the disadvantages of some current on-line reconfigurable system task scheduling algorithms, and proposed a new scheduling algorithm based on placement cost. This scheduling algorithm considered three types of cost, including hardware task execution time in FPGA, occupied FPGA area and fragmentation situation of FPGA, and also considered the unified scheduling of hardware and software tasks. When a hardware task was in scheduling, if the placement cost exceeded the setting threshold value, the task would be rejected to run in FPGA, but its software implementation could run in CPU. By reasonably rejecting some high cost tasks, this algorithm could achieve a higher overall successful scheduling rate. The simulation results show that this scheduling algorithm can achieve a higher deadline-guarantee ratio than others.

Partial reconfiguration (PR) of FPGAs can be used to dynamically extend and adapt the functionality of computing systems, swapping in and out HW tasks. To coordinate the on-demand task execution, we propose and implement a run time system manager for scheduling software (SW) tasks on available processor(s) and hardware (HW) tasks on any number of reconfigurable regions of a partially reconfigurable FPGA. Fed with the initial partitioning of the application into tasks, the corresponding task graph, and the available task mappings, the RTSM considers the runtime status of each task and region, e.g. busy, idle, scheduled for reconfiguration/execution, etc., to execute tasks. Our RTSM supports task reuse and configuration prefetching to minimize reconfigurations, task movement among regions to efficiently manage the FPGA area, and RR reservation for future reconfiguration and execution. We validate its correctness using our RTSM to execute an image processing application on a ZedBoard platform. We also evaluate its features within a simulation framework, and find that despite the technology limitations, our approach can give promising results in terms of quality of scheduling.

In heterogeneous reconfigurable systems including FPGA and CPU, besides software tasks and hardware tasks, there exist hybrid tasks which can be either executed on CPU as software tasks or implemented on FPGA as hardware tasks. This paper mainly focuses on the online scheduling problem within heterogeneous reconfigurable systems. According to a reconfigurable system prototype, a task scheduler model and its corresponding scheduling algorithm are presented. The model combines task allocation and task placement with task migration, making use of the great flexibility of hybrid tasks. Simulation results have demonstrated that the proposed algorithm can provide preferable scheduling performance and reduce the rejection rate.KeywordsReconfigurable SystemFPGAHybrid TaskScheduling

This paper presents an integrated approach to real-time processing and analysis of large-scale data streams using artificial intelligence methods, multi-agent systems, and multi-layered control architectures. Particular attention is devoted to combining technologies that are traditionally applied separately: unmanned aerial vehicle (UAV) control systems, situational centers, streaming video services, and distributed sensor networks. The proposed model accounts for the stochastic nature of the environment, the dynamic behaviour of data flows, and resource constraints, which are critical factors for real-time systems. The study develops a multi-level multi-agent architecture that includes central, regional, and edge nodes, as well as local agents such as drones, sensors, and client-side video devices. A mathematical formalization of agent interaction and reward functions is proposed to ensure a balance between service quality, latency, packet loss, and energy consumption. The use of multi-agent reinforcement learning (MARL) algorithms is introduced to support adaptive decision-making in real time, along with the development of a digital twin of the environment for predicting future system states using deep and generative models. The results demonstrate that the integration of edge computing, hierarchical decision-making, and local agent autonomy significantly enhances system resilience, provides stable performance under external disturbances, and enables effective infrastructure scaling. The findings form a scientific foundation for designing adaptive, robust, and self-learning next-generation digital ecosystems capable of intelligent processing of large data streams in real time

Real-time performance is the primary requirement for edge computing systems. However, with the surge in data volume and the growing demand for computing power, a computing framework consisting solely of CPUs is no longer competent. As a result, CPU+ heterogeneous architecture using accelerators to improve edge computing systems' computing capacity has received great attention. The type of accelerators determines the performance of the edge computing system largely. The accelerators include Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). FPGAs with its reconfigurability and high energy efficiency are widely used in many edge computing scenarios. Nontheless, the performance depends also on the scheduling efficiency between software tasks on CPUs and hardware tasks on FPGAs. Unfortunately, the existing strategies have not fully exploited the differences between hardware and software tasks, thus resulting in low scheduling efficiency. This paper proposes a task scheduling framework on the Dynamic Partial Reconfiguration (DPR) platform. We take full account of the characteristics of task switching overhead and predictable execution time of hardware tasks in DPR, and reduce the number of task-switching times and active tasks in the system, thus improving the scheduling efficiency. We conduct a set of experiments on the Zynq platform to verify the proposed framework. Experimental results demonstrate that when the execution time of the accelerator exceeds the reconfiguration cost by an order of magnitude, the efficiencies of all the cases are more than 98%, and the efficiencies can reach 90%-98% in the same order of magnitude.

Real time analysis has become a norm in today's fast and competitive business environment. Availability of recent data is a requirement to perform real time analysis. The data warehouse is meant to store historical data for analysis and these are populated from time to time through a process called as extract transform and load (ETL). The ETL process was done during nights or weekends to make the transactional data available at the data warehouse for analysis. But the delay of a day or weekend is not acceptable for real time analysis hence a concept of near real time ETL is developed to perform real time analysis. We explore in this study methods of how near real time ETL could be achieved. We focus on three ways of how near real time ETL could be performed. First is the Meta data management which if done effectively will greatly reduce the development time. Second is the concept of change data capture when implemented with some intelligent would greatly help in making the correct data available for analysis at the warehouse. The third is the use of parallel techniques to partition the flow of ETL and reduce the time window with which the data are available at the warehouse.

Atmospheric organic aerosols (OA) have profound effects on air quality, visibility, and radiative forcing of climate. Quantitative assessment of gas-particle equilibrium of OA components is critical to understand formation, growth, distribution, and evolution of OA in the atmosphere. This study presents a novel ambient pressure measurement approach developed and tested for untargeted screening of individual components in complex OA mixtures, followed by targeted chemical speciation of identified species and assessment of their physicochemical properties such as saturation vapor pressure and enthalpies of sublimation/evaporation. The method employs temperature-programmed desorption (TPD) experiments coupled to "direct analysis in real time" (DART) ionization source and high resolution mass spectrometry (HRMS) detection. Progression of the mass spectra is acquired in the TPD experiments over a T = 25-350 °C temperature range, and extracted ion chromatograms (EIC) of individual species are used to infer their apparent enthalpies of sublimation/evaporation (ΔHsub*) and saturation vapor pressure (pT*, Pa, or CT*, μg m-3) as a function of T. We validate application of this method for analysis of selected organic compounds with known ΔHsub and CT values, which showed excellent agreement between our results and the existing data. We then extend these experiments to interrogate individual components in complex OA samples generated in the laboratory-controlled ozonolysis of α-pinene, limonene, and β-ocimene monoterpenes. The abundant OA species of interest are distinguished based on their accurate mass measurements, followed by quantitation of their apparent ΔHsub* and CT* values from the corresponding EIC records. Comparison of C298K* values derived from our experiments for the individual OA components with the corresponding estimates based on their elemental composition using a "molecular corridors" (MC) parametrization suggests that the MC calculations tend to overestimate the saturation vapor pressures of OA components. Presented results indicate very promising applicability of the TPD-DART-HRMS method for the untargeted analysis of organic molecules in OA and other environmental mixtures, enabling rapid detection and quantification of organic pollutants in the real-world condensed-phase samples at atmospheric pressure and without sample preparation.

Human breath is composed of many volatile metabolites that can potentially provide information about a person's health status. A sensitive assay to identify these volatile organic compounds as biomarkers is ideal and important for early detection of many diseases. However, the diagnosis through online breath analysis based on mass spectrometry is difficult due to the low concentration of pertinent compounds in breath. The DART (direct analysis in real time) ion source is a novel atmospheric pressure ionization technique, which can efficiently ionize gaseous, liquid and solid samples with high throughput. A major problem in analysis of gaseous samples is its poor detection sensitivity due to open-air sampling. Recently, a confined DART ion source (cDART) was developed, where the plasma generated by the atmospheric pressure glow discharge ionizes gas-phase molecules in a Tee-shaped flow tube instead of open air. It leads to a significant increase of the ionization efficiency of gaseous samples. In this study, the cDART source was modified and applied in online analysis of exhaled breath. The limit of detection of the improved cDART source was determined to be at the level of low ppb. The cDART source was used in the real-time monitoring of an oral anesthetic drug. The concentration variations of two active ingredients in exhaled breath, menthol and benzocaine, and their metabolites with time were obtained. The results show that the cDART ion source is a powerful analytical tool to provide high sensitivity and high throughput analysis for volatile organic compounds in human breath, and can find potential applications in clinical diagnosis and therapy.

Glycosides are a kind of highly important natural aromatic precursors in tobacco leaves. In this study, a novel HKUST-1-coated monolith dip-it sampler was designed for the fast and sensitive analysis of trace glycosides using direct analysis in real-time mass spectrometry. This device was prepared in two steps: in situ polymerization of monolith in a glass capillary of dip-it and layer-by-layer growth of HKUST-1 on the surface of monolith. Sufficient extraction was realized by immersing the tip to solution and in situ desorption was carried out by plasma direct analysis in real time. Compared with traditional solid-phase microextraction protocols, sample desorption was not needed anymore, and only extraction conditions were needed to be optimized in this method, including the gas temperature of direct analysis in real time, extraction time, and CH3 COONH4 additive concentration. This method enabled the simultaneous detection of six kinds of glycosides with the limits of detection of 0.02-0.05μg/mL and the linear ranges covering two orders of magnitude with the limits of quantitation of 0.05-0.1μg/mL. Moreover, the developed method was applied for the glycosides analysis of three tobacco samples, which only took about 2s for every sample.

Rapid screening of pesticides present on the surfaces of fruits and vegetables has been facilitated by using a Direct Analysis in Real Time (DART(®)) open air surface desorption ionization source coupled to an Exactive(®) high-resolution accurate mass benchtop orbitrap mass spectrometer. The use of cotton and polyester cleaning swabs to collect and retain pesticides for subsequent open air desorption ionization is demonstrated by sampling the surface of various produce to which solutions of pesticides have been applied at levels 10 and 100 times below the tolerance levels established by the United States Environmental Protection Agency (US EPA). Samples analyzed include cherry tomatoes, oranges, peaches and carrots each chosen for their surface characteristics which include: smooth, pitted, fuzzy, and rough respectively. Results from the direct analysis of fungicides on store-bought oranges are also described. In all cases, the swabs were introduced directly into the heated ionizing gas of the DART source resulting in production of protonated pesticide molecules within seconds of sampling. Operation of the orbitrap mass spectrometer at 25,000 full-width half maximum resolution was sufficient to generate high-quality accurate mass data. Stable external mass calibration eliminated the need for addition of standards typically required for mass calibration, thus allowing multiple analyses to be completed without instrument recalibration.

Direct analysis in real time (DART) is a recently developed ambient ionization technique for mass spectrometry to enable rapid and sensitive analyses with little or no sample preparation. After swab-based field sampling, the organothiophosphate malathion was analyzed using DART-Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) and tandem mass spectrometry (MS/MS). Mass resolution was documented to be over 800,000 in full-scan MS mode and over 1,000,000 for an MS/MS product ion produced by collision-induced dissociation of the protonated analyte. Mass measurement accuracy below 1 ppm was obtained for all DART-generated ions that belonged to the test compound in the mass spectra acquired using only external mass calibration. This high mass measurement accuracy, achievable at present only through FTMS, was required for unequivocal identification of the corresponding molecular formulae.

Rapid screening for smokeless powders using DART-HRMS and thermal desorption DART-HRMS