1971868
Diverse Soft Real-Time Processing in an Integrated System
The simple notion of soft real-time processing has fractured into a spectrum of diverse soft real-time types with a variety of different resource and time constraints. Schedulers have been developed for each of these types, but these are essentially point solutions in the space of soft real-time and no detailed unified definition of soft real-time has previously been provided that includes all types of soft realtime processing. We present a complete real-time taxonomy covering the spectrum of processes from best-effort to hard real-time. The taxonomy divides processes into nine classes based on their resource and timeliness requirements and includes four soft real-time classes, each of which captures a group of soft real-time applications with similar characteristics. We exploit the different features of each of the soft real-time classes to integrate all of them into a single scheduler together with hard real-time and best-effort processes and present results demonstrating their performance
7754068
Optimal state feedback based resource allocation for resource-constrained control tasks
In many application areas, including control systems, careful management of system resources is key to providing the best application performance. Most traditional resource management techniques for real-time systems with multiple control loops are based on open-loop strategies that statically allocate a constant CPU share to each controller, independent of their current resource needs. This provides average control performance with minimal overhead but in general fails to provide the best performance possible within the available resources. We show that by using feedback to dynamically allocate resources to controllers as a function of the current state of their controlled systems, control performance can be significantly improved. We present an optimal resource allocation policy that maximizes control performance within the available resources and provide experimental results showing that the optimal policy 1) significantly increases control performance compared to traditional control system implementations (by more than 20% in our experiments), 2) maximizes control performance over other feedback-based policies, 3) saves resources when perturbations occur infrequently, and 4) incurs negligible overhead.
7770599
Improving soft real-time performance through better slack reclaiming
Modern operating systems frequently support applications with a variety of timing constraints including hard real-time, soft real-time, and best-effort. To guarantee performance, critical applications typically over-reserve resources based on worst-case resource usage estimates, while others may reserve based on average-case or other estimates. When resources are fully subscribed, the performance of soft- and non-real-time applications depends upon the effective distribution of dynamic slack - reserved, but unused resources - from other tasks. Motivated by several representative examples, we derive four general principles for the effective management of slack. We have implemented these principles in four progressively better slack schedulers that demonstrate their effectiveness. BACKSLASH, which employs all four principles, misses fewer soft realtime deadlines than all of the other slack schedulers we examined
16836522
Real-time gang schedulings with workload models for parallel computers
Gang scheduling has been shown to be an effective job scheduling policy for parallel computers that combines elements of space sharing and time sharing. We propose new policies to enable gang scheduling to adapt to environments with real-time constraints. Our work, to our best knowledge, is the first work to attempt to address the real-time aspects of gang scheduling. Our system guided by a metric, called "task utilization workload", can schedule both real-time and non-real-time tasks at the same time. We report simulation results with a family of scheduling algorithms based on our proposed metric. Our scheme is designed to be a practical scheme to be used for large scale industrial and commercial parallel systems. Preliminary simulation results also show that our proposed policy is an effective scheme to perform real-time scheduling, while scheduling non-real-time jobs with fairness and good throughput.
31744215
Multiband room effect simulator for 5.1-channel sound system
The paper investigates a new room effect simulator with different quality of audio sound sources for a 5.1-channel sound system. The audio effect relates its impulse response with different room dimensions. A multiband equalizer is designed to generate many kinds of music impressions. We also modify the mono Dolby surround decoder into a simplified surround decoder so that the stereo room simulator can generate 5.1-channel surround outputs. All experiments are at a sampling rate of 44.1 kHz and 16 bits per sample. By the room effect simulator, we can get a full and a live listening experience.
11960535
Performance Modeling of Grid Resource Management and Scheduling Systems
Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.
10271144
Robust image authentication method surviving JPEG lossy compression
Image authentication verifies the originality of an image by detecting malicious manipulations. This goal is different from that of image watermarking which embeds into the image a signature surviving most manipulations. Existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we describe an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationship between DCT coefficients of the same position in separate blocks of an image. This relationship will be preserved when these coefficients are quantized in a JPEG compression process. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of how high the compression ratio is. We also show that, in different practical cases, the design of authenticator depends on the number of recompression times and on whether the image is decoded into integral values in the pixel domain during the recompression process. Theoretical and experimental results indicate that this technique is effective for image authentication.
14630306
Fuzzy neural network classification design using support vector machine
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the iris, vehicle and dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.