Challenges on Probabilistic Modeling for Evolving Networks

TL;DR

This paper surveys the challenges and developments in probabilistic modeling for complex, evolving networks, highlighting issues in modeling, optimization, and application areas like performance, management, and security.

Contribution

It provides a comprehensive overview of the current state, challenges, and future directions in probabilistic modeling for dynamic networks.

Findings

Identifies key challenges in probabilistic modeling of evolving networks.

Highlights the importance of probabilistic approaches in network management and security.

Discusses potential future research directions in the field.

Abstract

With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors, incomplete management information and dependency relationship between the entities among the emerging complex networks. Due to the complexity of emerging networks, it is not always possible to build precise models in modeling and optimization (local and global) for networks. This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application areas of network performance, network management and network security for evolving networks.