mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines

Evaluating Security Assurance Case Adaptation

Security certification processes for information systems involve expressing security controls as functional and non-functional requirements, monitoring deployed mechanisms that satisfy the requirements, and measuring the degree of confidence in system compliance. With the potential for systems to perform runtime self-adaptation, functional changes to remedy system performance may impact security control compliance. This impact can extend throughout a network of related controls causing significant degradation to the system’s overall compliance status. We represent security controls as security assurance cases and implement them in XML for management and evaluation. The approach maps security controls to softgoals, introducing achievement weights to the assurance case structure as the foundation for determining security softgoal satisficing levels. Potential adaptations adjust the achievement weights to produce different satisficing levels. We show how the levels can be propagated within the network of related controls to assess the overall security control compliance of a potential adaptation

Tele Assistance: : A Self-Adaptive Service-Based System Exemplar

Research on adaptive and self-managing systems is hindered by a lack of prototypical applications that researchers could use to evaluate and compare new methods, techniques and tools. To address this limitation, we introduce a reference implementation of a Tele Assistance System (TAS) for research on self-adaptation in the domain of service-based systems. Our TAS exemplar of service-based systems comes with pre-defined scenarios for comparing the effectiveness of different self-adaptation solutions. Other researchers can easily exploit the underlying service platform, reusable components and development method we devised for TAS to speed up the engineering of additional research exemplars for service-based systems

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

Deep Q Learning for Self Adaptive Distributed Microservices Architecture (in press)

One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behavior, and provide a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. The behaviour of a microservices architecture is continuously changing overtime, which makes it a challenging task to use a statistical model to identify both the normal and abnormal behaviour of the services running. The performance of the microservices cluster could fluctuate around the demand to accommodate scalability, orchestration and load balancing demands. To achieve the desired high levels of self-adaptability, this research implements microservices architectures model following the MAPE-K model. Our proposed architecture employs Markov decision process (MDP) to identify the transition from one cluster state to another. Our proposed architecture employs a deep Q- learning network (DQN) for dynamically selecting the adaptation action that yield the highest reward. This paper evaluates the effectiveness of using DQN and MDP agent to achieve high level of self-adaptability of microservice architecture. We argue in this paper that such integration between DQN and MDP in MAPE-K model offers microservice architecture with self-adaptability against the contextual changes in the operational environment. The self-adaptation property is achieved by allowing the MDP agent to explore the observation space and lets the DQN to select the adaptation policy with the highest reward, then the MDP agent executes the adaptation action and observes the changes. We believe integrating DQN into the adaptation action selection process improves the effectiveness of the adaptation and reduces the adaptation risk including resources over-provisioning and thrashing. The proposed model preserves the cluster state and preventing multiple actions to taking place at the same time. Our model also guarantees that the executed adaptation action fits the current execution context and achieves the adaptation goals

AN APPROACH FOR COMMUNICATION RELAIBILITY USING SELF-ADAPTIVE AUTONOMIC COMPUTING TECHNIQUES

Interdependency of electric power grids and information and communication technology is a rapidly growing topic. With the introduction of Smart Grid, handling dynamic load tracking, dynamic tariffs, clients that can consume but also produce electricity that can be delivered to the grid has become a part of everyday operational cycles within power supply companies. Hence, electricity distribution and power supply companies are in need for introduction of efficient mechanisms for the optimal tracking and use of available electric energy. In this paper, we describe the low voltage (LV) distribution network monitoring system developed for the Electric Power Industry of Serbia (EPS) electricity distribution company. The system we present is implemented in a way so that it provides abilities to measures, communicates and stores real-time data, translating it into actionable information needed by EPS to meet the described challenges regarding LV distribution networks. The implemented system is using self-adaptive autonomic computing techniques to provide a reliable data transfer from measurement devices deployed in different parts of the LV distribution network

High-level Language Support for the Control of Reconfigurations in Component-based Architectures

International audienceNowadays, smart home is extended beyond the house itself to encompass connected platforms on the Cloud as well as mobile personal devices. This Smart Home Extended Architecture (SHEA) helps customers to remain in touch with their home everywhere and any time. The endless increase of connected devices in the home and outside within the SHEA multiplies the deployment possibilities for any application. Therefore, SHEA should be taken from now as the actual target platform for smart home application deployment. Every home is different and applications offer different services according to customer preferences. To manage this variability, we extend the feature modeling from software product line domain with deployment constraints and we present an example of a model that could address this deployment challenge

Managing software evolution through midleware and policy-based software adaptation framework

Software evolution is a process that is needed in order for software to remain useful. Thus, software evolution should be properly planned and controlled to prevent its negative impact from affecting any organization. Software adaptation concept is one of the promising ways to control software evolution. In this approach, software is made adaptable to minimize the impact of change. A lot of researches on software adaptation focus on adaptability of mobile based and network application due to its context sensitivity and quality-of-service requirements. However, there is still lack of work in enterprise system domain with multiple delivery channels, which focus on adaptability of its context environment such as the changes introduced to its devices. Hence, the purpose of this research is to develop a middleware and policy-based, adaptation framework to manage negative effects of software evolution in an enterprise system. The main research focus is on the changes introduced at the device layer. The concept of policy is used to specify adaptations requirements. This research provides a framework called Middleware and Policy-Based Framework to Manage Software Evolution (MiPAF), which can be used to develop adaptive software, allowing parameterized and compositional adaptation. Furthermore, the framework can be used by client-server and web-based application. A policy language called MiPAF Policy Language (MPL) is created to be used with the framework. MiPAF is formally specified using Z Notation and the policy language is described using pseudo code. A tool is provided to assist developers in creating the policy. For evaluation of the framework, a set of runtime components were developed and implemented for Unit Trust System (UTS) Front-end and web-based UTS, two industrial-based case studies. The evaluation result shows that MiPAF excellently fulfil all the evaluation criteria described in this thesis

Behavioural Model-based Control for Autonomic Software Components

International audienceAutonomic Managers (AMs) have been largely used to autonomously control reconfigurations within software compo- nents. This management is performed based on past monitoring events, configurations as well as behavioural programs defining the adaptation logics and invariant properties. The challenge here is to provide assurances on navigation through the configuration space, which requires taking decisions that involve predictions on possible futures of the system. This paper proposes the design of AMs based on logical discrete control approaches, where the use of behavioural models enriches the manager with a knowledge not only on events, states and past history, but also with possible future configurations. We define a Domain Specific Language, named Ctrl-F, which provides high-level constructs to describe behavioural programs in the context of software components. The formal definition of Ctrl-F is given by translation to Finite State Automata, which allow for the exploration of behavioural programs by verification or Discrete Controller Synthesis, automatically generating a controller enforcing correct behaviours. We implement an AM by integrating the result of Ctrl-F compilation and validate it with an adaptation scenario over Znn.com, a self-adaptive case study