Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page

A Deep Recurrent Q Network Towards Self-adapting Distributed Microservices Architecture (in press)

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resources over-provisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms including: i) deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The DRQN implementation in this paper manages to outperform the above mentioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training times. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms

A deep recurrent Q network towards self-adapting distributed microservice architecture

One desired aspect of microservice architecture is the ability to self-adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE-K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-learning network (DRQN). It is argued that such integration between DRQN and Markov decision process (MDP) agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resource overprovisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms, including (1) a deep Q-learning network (DQN), (2) a dueling DQN (DDQN), (3) a policy gradient neural network, and (4) deep deterministic policy gradient. The DRQN implementation in this paper manages to outperform the aforementioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training time. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter is more effective compared to the standard ACS