Context-oriented programming is an emerging technique that enables dynamic behaviour variation based on context changes. In COP, context can be handled directly at the code level by enriching the business logic of the application with code fragments responsible for performing context manipulation, thus providing the application code with the required adaptive behavior. Unfortunately, the whole set of sensors, effectors, and adaptation processes is mixed with the application code, which often leads to poor scalability and maintainability. In addition, the developers have to surround all probable behavior inside the source code. As an outcome, the anticipated adjustment is restricted to the amount of code stubs on hand offered by the creators. Context-driven adaptation requires dynamic composition of context-dependent parts. This can be achieved trough the support of a component model that encapsulates the context-dependent functionality and decouples them from the application’s core-functionality. The complexity behind modeling the context-dependent functionality lies in the fact that they can occur separately or in any combination, and cannot be encapsulated because of their impact across all the software modules. Before encapsulating crosscutting context-dependent functionality into a software module, the developers must first identify them in the requirements documents. This requires a formal development paradigm for analyzing the context-dependent functionality; and a component model, which modularizes their concerns. COCA-MDA is proposed in this article as model driven architecture for constructing self-adaptive application from a context oriented component model.

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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. Index TermsService oriented architecture, self-adaptive architectures, reinforcement learning, Q-learning algorithms, deep Q-Learning networks, recurrent Q-learning networks, policy approximation, multi agents environment.