1207322
Dynamic integrated scheduling of hard real-time, soft real-time, and non-real-time processes
Real-time systems are growing in complexity and real-time and soft real-time applications are becoming common in general-purpose computing environments. Thus, there is a growing need for scheduling solutions that simultaneously support processes with a variety of different timeliness constraints. Toward this goal we have developed the resource allocation/dispatching (RAD) integrated scheduling model and the rate-based earliest deadline (RBED) integrated multi-class real-time scheduler based on this model. We present RAD and the RBED scheduler and formally prove the correctness of the operations that RBED employs. We then describe our implementation of RBED and present results demonstrating how RBED simultaneously and seamlessly supports hard real-time, soft real-time, and best-effort processes.
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
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
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.
11473366
Class-Based Latency Assurances for Web Servers
This paper presents a fuzzy control approach for process assignment in order to guarantee absolute percentile delay in web servers. Comparing with mean delay used in previous works, percentile delay introduces strong nonlinearities to the plant; it is therefore difficult to achieve satisfactory performance by a linear controller, which is commonly used to guarantee mean delay performance. In this paper we propose a fuzzy controller. Its key feature is independence of the plant and robust to model uncertainty, which is very suitable to the strong nonlinear web servers. The experiments have demonstrated the efficiency of the proposed fuzzy controller in handing the variety of requests.
52872372
Modeling and Performance Analysis of a Multiserver Multiqueue System on the Grid
Grid computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and high-performance orientation. A Grid integrates and coordinates resources and users that live within different control domains with the goal of delivering nontrivial quality of service (QoS). Consequently, task management and scheduling is of central importance for the Grid-based systems. In this paper, we model the task dispatching and selecting on the distributed Grid computing system by a multiserver multiqueue (MSMQ) model, and propose a modeling and analysis technique based on Stochastic Petri Net (SPN) methods. An approximate analysis technique is also proposed to reduce the complexity of the model.
20106221
Radius Margin Bounds for Support Vector Machines with the RBF Kernel
An important approach for efficient support vector machine (SVM) model selection is to use differentiable bounds of the leave-one-out (loo) error. Past efforts focused on finding tight bounds of loo (e.g., radius margin bounds, span bounds). However, their practical viability is still not very satisfactory. Duan, Keerthi, and Poo (2003) showed that radius margin bound gives good prediction for L2-SVM, one of the cases we look at. In this letter, through analyses about why this bound performs well for L2-SVM, we show that finding a bound whose minima are in a region with small loo values may be more important than its tightness. Based on this principle, we propose modified radius margin bounds for L1-SVM (the other case) where the original bound is applicable only to the hard-margin case. Our modification for L1-SVM achieves comparable performance to L2-SVM. To study whether L1-or L2-SVM should be used, we analyze other properties, such as their differentiability, number of support vectors, and number of free support vectors. In this aspect, L1-SVM possesses the advantage of having fewer support vectors. Their implementations are also different, so we discuss related issues in detail.
5221251
On Stochastic High-Level Petri Nets
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.