Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies

Scalable Scheduling Policy Design for Open Soft Real-Time Systems

Open soft real-time systems, such as mobile robots, must respond adaptively to varying operating conditions, while balancing the need to perform multiple mission specific tasks against the requirement that those tasks complete in a timely manner. Setting and enforcing a utilization target for shared resources is a key mechanism for achieving this behavior. However, because of the uncertainty and non-preemptability of some tasks, key assumptions of classical scheduling approaches do not hold. In previous work we presented foundational methods for generating task scheduling policies to enforce proportional resource utilization for open soft real-time systems with these properties. However, these methods scale exponentially in the number of tasks, limiting their practical applicability. In this paper, we present a novel parameterized scheduling policy that scales our technique to a much wider range of systems. These policies can represent geometric features of the scheduling policies produced by our earlier methods, but only require a number of parameters that is quadratic in the number of tasks. We provide empirical evidence that the best of these policies are competitive with exact solution methods in small problems, and significantly outperform heuristic methods in larger ones

Scheduling Design with Unknown Execution Time Distributions or Modes

Open soft real-time systems, such as mobile robots, experience unpredictable interactions with their environments and yet must respond both adaptively and with reasonable temporal predictability. Because of the uncertainty inherent in such interactions, many of the assumptions of the real-time scheduling techniques traditionally used to ensure predictable timing of system actions do not hold in those environments. In previous work we have developed novel techniques for scheduling policy design where up-front knowledge of execution time distributions can be used to produce both compact representations of resource utilization state spaces and efficient optimal scheduling policies over those state spaces. This paper makes two main contributions beyond our previous work, to the state of the art in scheduling open soft real-time systems: (1) it shows how to relax the assumption that the entire distribution of execution times is known up front, to allow online learning of an execution time distribution during system run-time; and (2) it shows how to relax the assumption that the execution time of a system action can be characterized by a single distribution, to accommodate different execution time distributions for an action being taken in one of multiple modes. Each of these contributions allows a wider range of system actions to be scheduled adaptively and with temporal predictability, which increases the applicability of our approach to even more general classes of open soft real-time systems

An Analysis of Transaction Management in Distributed Real Time Databases: An Overview

A real time distributed computing has heterogeneously networked computers to solve a single problem. So coordination of activities among computers is a complex task and deadlines make more complex. The performance of the system depends on many factors such as traffic workloads data base system architecture, underlying processor, disk speeds, concurrency control, transaction management etc.[1,2,3,4,5,6].  A simulation study have  to be performed to analyze the performance under different transaction scheduling, different workloads, arrival rate priority policies, altering slack factors and preemptive policies. The performance of the distributed system under various conditions is to be monitored and parameters such as arrival rate, transaction size, transaction distribution policies, and execution time are to be analyzed

Spectrum Management using Markov Decision Processes

Abstract: The advent of cognitive radio technology has enabled dramatically more options in the use of RF spectrum, allowing multiple transmitters to effectively share spectrum in ways that were previously unavailable (either due to technical limitations or regulatory restrictions). In this dissertation, we investigate approaches to managing RF spectrum use, with a focus on combining multiple control decisions in a mutually beneficial manner. Our approach to making spectrum management decisions is grounded in Markov decision theory, which has a rich formal foundation and is frequently used to guide decision making in other disciplines. Here, we develop a set of Markov Decision Processes (MDPs) that model the RF spectrum management problem (in various forms). These MDPs are then queried to provide guidance for management decisions, including the combination of both admission and modulation decisions. This results in control decisions that are optimal in expectation. To address the computational complexity inherent in computing these control decisions, we develop heuristic approaches that mimic the MDP\u27s decisions based upon patterns observed in the MDP decision space. These heuristics are shown to closely approximate the optimal results from the MDP. Finally, we empirically assess the appropriateness of using Markov decision theory for RF spectrum management by comparing our MDPs to a discrete-event simulation model that relaxes several of the modeling assumptions made in the development of the MDPs

Scheduling Design and Verification for Open Soft Real-time Systems ∗

Open soft real-time systems, such as mobile robots, experience unpredictable interactions with their environments and yet must respond both adaptively and with reasonable temporal predictability. New scheduling approaches are needed to address the demands of such systems, in which many of the assumptions made by traditional real-time scheduling theory do not hold. In previous work we established foundations for a scheduling policy design and verification approach for open soft real-time systems, that can use different decision models, e.g., a Markov Decision Process (MDP), to capture the nuances of their scheduling semantics. However, several important refinements to the preliminary techniques developed in that work are needed to make the approach applicable in practice. This paper makes three main contributions to the state of the art in scheduling open soft real-time systems: (1) it defines a novel representation of the scheduling state space that is both more compact and more expressive than the model defined in our previous work; (2) it exploits regular structure of that representation to allow efficient verification of properties involving both discrete and continuous system state variables under specific scheduling policies; and (3) it removes the unnecessary use of a time horizon in our previous approach, thus allowing the more precise specification and enforcement of a wider range of scheduling policies for open soft real-time systems.