Optimization in Graph Transformation Systems with Time Using Petri Net Based Techniques

Extra–functional properties of IT systems have to be analyzed and subsequently optimized carefully during the design phase in order to assure a proper quality of service and decrease operational costs. Several verification and validation methods are known to check the correctness of the system services, while optimization may serve to reach boundaries thus minimizing costs or duration of operating the system. However, the combination of the best practices of the two fields according to the purpose of the analysis is a challenging question. In a previous paper, we showed how such a problem can be formalized and solved when the evolution of the system is captured by graph transformation systems (GTS) with cost parameters attached to each graph transformation rule denoting the cost of firing the rule.This technique is adapted in the current paper to deliver a time–optimal trajectory in a GTS with time. While the cost of a GT rule sequence always equals to the sum of the cost of the involved GT rules, the concurrent application of GT rules may reduce the minimal duration of a GT rule sequence, which is a major conceptual difference concerning optimization

Optimal trajectory generation for Petri nets

Recently, the increasing complexity of IT systems requires the early verification and validation of the system design in order to avoid the costly redesign. Furthermore, the efficiency of system operation can be improved by solving system optimization problems (like resource allocation and scheduling problems). Such combined optimization and validation, verification problems can be typically expressed as reachability problems with quantitative or qualitative measurements. The current paper proposes a solution to compute the optimal trajectories for Petri net-based reachability problems with cost parameters. This is an improved variant of the basic integrated verification and optimization method introduced in [11] combining the efficiency of Process Network Synthesis optimization algorithms with the modeling power of Petri nets

Model transformation by graph transformation: A comparative study

This is an electronic version of the paper presented at the Model Transformation in Practice, held in Montego Bay on 2005Graph transformation has been widely used for expressing model transformations. Especially transformations of visual models can be naturally formulated by graph transformations, since graphs are well suited to describe the underlying structures of models. Based on a common sample model transformation, four different model transformation approaches are presented which all perform graph transformations. At first, a basic solution is presented and crucial points of model transformations are indicated. Subsequent solutions focus mainly on the indicated problems. Finally, a first comparison of the chosen approaches to model transformation is presented where the main ingredients of each approach are summarized

A combination of Petri nets and Process Network Synthesis

Abstract – Resource allocation and scheduling optimization problems are core problems in the field of IT systems. However, such problems frequently underlie several additional constraints. The formalization of a real life problem requires a well-defined mathematical and modeling approach providing an integrated verification and optimization. The current paper proposes such methods adapting Process Network Synthesis algorithms to Petri net reachability problem: combining the efficiency of PNS optimization algorithms with the modeling power of Petri nets. They provide powerful techniques to compute optimal trajectories for the reachability analysis of the modeled system.

Optimization in Graph Transformation Systems Using Petri Net Based Techniques ⋆

Introduction. The design of business or production systems frequently necessitate to build a system that is simultaneously correct and optimal, i.e., the system has to simultaneously fulfill logical and numerical conditions. For instance, in workflows, each activity can be constrained by certain budget restrictions, and a typical requirement is to find the cost-optimal solution trajectory respectin