A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.