Testing self-adaptive applications with simulation of context events

Modern trends in mobile computing have raised the expectations of users in terms of such features such as context-awareness and self-adaptiveness. With such capabilities, applications can autonomously sense their context and automate a number of tasks, effectively reducing the attention required by the end users. This paper presents a custom simulation engine, designed to support the testing of applications developed using the MUSIC platform. The simulation tool consists of a platform-independent server module, deployed along with the application, and a client module which is responsible for interpreting and executing the simulation script. The use of the tool is demonstrated in the scope of the SatMotion application, which is designed to assist satellite antenna installers with specialized functionality

An Adaptation Reasoning Approach for Large Scale Component-based Applications

There is a growing demand for context-aware applications that can dynamically adapt to their run-time environment. An application offers a collection of functionalities that can be realized through a composition of software components and/or services that are made available at runtime. With the availability of alternative variants of such components and/or services that provide the basic functionalities, while differ in extra-functional characteristics, characterized by quality of services (QoS), an unforeseen number of application variants can be created. The variant that best fits the current context is selected through adaptation reasoning, which can suffer from the processing capabilities of resource-scarce mobile devices, especially when a huge number of application variants needs to be reason about. In this paper, we present a reasoning approach, which provides a meaningful adaptation decision for adaptive applications having a large number of variants within a reasonable time frame. The approach is validated through two arbitrary applications with large number of variants. Keywords: self-adaptation, ubiquitous computing, adaptation reasoning, variability, scalability, utility functio

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