Load balanced scheduling and reliability modeling of grid transaction processing system

What is it about?

On-demand computing is a popular enterprise model in which the computing resources are made available to the users as needed. On-demand computing based transaction processing system which has grown rapidly in recent years is an information processing system with the stringent requirements of resources to meet the fluctuating demands. Concepts such as grid computing, utility computing, autonomic computing, and adaptive management seem very similar to the concept of on-demand computing. When demands of resources fluctuate, the system needs load balancing for the efficient utilization of the computational resources. Furthermore, scheduling is needed to assign the transactions to the appropriate resources. Thus, modeling of load balanced scheduling along with reliability analysis for this system is a challenging task. This paper presents the load balanced scheduling and reliability modeling in such an environment by using colored Petri nets (CPNs). CPNs which combine Petri nets with programming languages is a powerful modeling technique. The proposed CPN-based modeling pattern formally describes the process of trans- action distribution and execution within the on-demand computing environment. Moreover, the CPN-based model uses the hierarchical modeling capability of CPNs, including different levels of abstraction (sub-modules). This helps easily handling and extending the model. Since on-demand computing based transaction processing system executes a number of concurrent transactions. The CPN-based model is extended to express the concurrency, thus improving the reliability results. This paper takes the example of grid transaction processing (GTP) system with the problem of load balanced scheduling modeling and reliability evaluation.

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Why is it important?

This paper explains the modeling and analysis of the on-demand computing based transaction processing system. The colored Petri nets tool is used here for modeling the system.

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