The TMN that appears to operate the various communication networks generally and efficiently is developed under the different platform environment such as the different hardware and the different operating system. One of the main problems is that all the agents of the TMN system must be duplicated and maintain the software and the data blocks that perform the identical function. Therefore, the standard of the Q3 interface development cannot be defined and the multi-platform cannot be supported in the development of the TMN agent. In order to overcome these problems, the Farming methodology that is based on the Farmer model has been suggested. The main concept of the Farming methodology is that the software and the data components that are duplicated and stored in each distributed object are saved in the Platform Independent Class Repository (PICR) by converting into the format of the independent componentware in the platform, so that the componentwares that are essential for the execution can be loaded and used statically or dynamically from PICR as described in the framework of each distributed object. The distributed TMN agent of the personal communication network is designed and developed by using the Farmer model.

Providing workflow function is one of the most important research issues in the next generation Internet services such as Web Service and Grid Computing. Scalability for Internet scale services, reliability for unstable Internet resources, and management functions of workflow systems are the essential requirements in these environments. However, existing workflow enactment models for enterprises could not meet these requirements. This paper proposes the PeerFlow that is a P2P based workflow enactment model, to provide workflow functions for the next generation Internet services. To apply P2P model to the workflow enactment model, we introduce the concept of the instance buddy and the index data of workflow instances, then propose the principle architecture of the PeerFlow. The instance buddy enables the autonomous processing of peers, and it is used for recovery and monitoring functions. This paper also presents the recovery capabilities of PeerFlow with formal proofs for the reliability issues and a performance evaluation with SimPy, the Python simulation package.