This paper presents a theoretical basis for scheduling approaches based on purely control-flow graphs. This formulation includes a control flow graph model based on a finite discrete-time homogeneous Markov chain suitable to represent complex control structures. A probabilistic finite state machine is introduced to model the resulting schedule and evalute the effectiveness of the scheduling approaches for control flow graphs. The need of such models is imposed by the nature of real time systems in which the control sequence depends on external conditions
AbstractThe two large classes of sequential control are those in which certain combinations of state...
The Processing Graph Method (PGM) --- a dataflow model widely used in the design and analysis of emb...
In industry, discrete models can be used to describe and analyze a class of event driven systems. Th...
It is known that in many applications, because of selection state-ments, e.g., if-statement, the com...
In this paper, we present a novel scheduling algorithm targeted towards minimizing the average execu...
This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for ...
A linear time algorithm is presented for finding dominators in control flow graphs. 1 Introduction ...
Abstract: Predictive and optimal process control using finite Markov chains is considered. A basic p...
This paper is about the incremental computation of control sequences for discrete event systems in u...
In this paper, we discuss simulation results for the traffic signal control problem. Our algorithms ...
Continuous-time Controlled Markov Chains are a useful model for many processes where it is necessary...
In the paper different methods of production order scheduling and control that are elaborated by the...
We introduce a general theoretical framework, based on the Markov chain imbedding approach, that lea...
The goal of scheduling problems is to assign machines to a pre-specified jobs which require processi...
Directed Acyclic Graph Scheduling is a technique used to implement the real-time execution of Digita...
AbstractThe two large classes of sequential control are those in which certain combinations of state...
The Processing Graph Method (PGM) --- a dataflow model widely used in the design and analysis of emb...
In industry, discrete models can be used to describe and analyze a class of event driven systems. Th...
It is known that in many applications, because of selection state-ments, e.g., if-statement, the com...
In this paper, we present a novel scheduling algorithm targeted towards minimizing the average execu...
This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for ...
A linear time algorithm is presented for finding dominators in control flow graphs. 1 Introduction ...
Abstract: Predictive and optimal process control using finite Markov chains is considered. A basic p...
This paper is about the incremental computation of control sequences for discrete event systems in u...
In this paper, we discuss simulation results for the traffic signal control problem. Our algorithms ...
Continuous-time Controlled Markov Chains are a useful model for many processes where it is necessary...
In the paper different methods of production order scheduling and control that are elaborated by the...
We introduce a general theoretical framework, based on the Markov chain imbedding approach, that lea...
The goal of scheduling problems is to assign machines to a pre-specified jobs which require processi...
Directed Acyclic Graph Scheduling is a technique used to implement the real-time execution of Digita...
AbstractThe two large classes of sequential control are those in which certain combinations of state...
The Processing Graph Method (PGM) --- a dataflow model widely used in the design and analysis of emb...
In industry, discrete models can be used to describe and analyze a class of event driven systems. Th...