The optimization of resource management in large scale distributed systems (LSDS) with the capability of self-organization is a complex process. LSDS are highly dynamic systems, with permanent changes in their configurations, as peers may join and leave the system with no restriction or control. This paper present the architecture for monitoring and resource management based on existing middleware solutions through the design of algorithms and methods inspired by natural models. The architecture is decentralized and it aims to optimize resource management in different types of distributed systems such as Grid, P2P, and Cloud. The important components considered for the architecture are: allocation of resources, task scheduling, resource discovery, monitoring resources and provide fault tolerance. As the system may have a large number of nodes, we need a scalable algorithm for monitoring process, able to guarantee a fast convergence no matter what the structure of the network is. In this context, gossip-based algorithms offer solutions for various topics in LSDS. The project aims to highlights the original obtained results in internationally scientific community. The paper presents the expecting results and discusses the performance evaluation of proposed architecture. Secondly, the paper presents a gossip-based algorithm for monitoring large-scale distributed systems and analyzes its efficiency in a simulated environment provided by OverSim.