Mobile Service Clouds: A self-managing infrastructure for autonomic mobile computing services

Abstract. We recently introduced Service Clouds, a distributed infrastructure designed to facilitate rapid prototyping and deployment of autonomic communication services. In this paper, we propose a model that extends Service Clouds to the wireless edge of the Internet. This model, called Mobile Service Clouds, enables dynamic instantiation, composition, configuration, and reconfiguration of services on an overlay network to support mobile computing. We have implemented a prototype of this model and applied it to the problem of dynamically instantiating and migrating proxy services for mobile hosts. We conducted a case study involving data streaming across a combination of PlanetLab nodes, local proxies, and wireless hosts. Results are presented demonstrating the effectiveness of the prototype in establishing new proxies and migrating their functionality in response to node failures.

Building Adaptive Services for Distributed Systems

There exists a growing class of distributed applications that require adaptive middleware services, i.e., services that are able to monitor changes in the execution environment, and in the user requirements, reacting to these changes by adapting their behaviour. This paper presents a framework that supports the definition, implementation, and execution of reconfigurable service compositions, and puts forward an approach to the construction of adaptive distributed applications. Adaptiveness is achieved through the dynamic reconfiguration of service compositions in accordance with high-level policies. The framework allows those reconfigurations to be carried out transparently to the application. This approach is illustrated using a messaging applicatio

StarMX: A Framework for Developing Self-Managing Software Systems

The scale of computing systems has extensively grown over the past few decades in order to satisfy emerging business requirements. As a result of this evolution, the complexity of these systems has increased significantly, which has led to many difficulties in managing and administering them. The solution to this problem is to build systems that are capable of managing themselves, given high-level objectives. This vision is also known as Autonomic Computing. A self-managing system is governed by a closed control loop, which is responsible for dynamically monitoring the underlying system, analyzing the observed situation, planning the recovering actions, and executing the plan to maintain the system equilibrium. The realization of such systems poses several developmental and operational challenges, including: developing their architecture, constructing the control loop, and creating services that enable dynamic adaptation behavior. Software frameworks are effective in addressing these challenges: they can simplify the development of such systems by reducing design and implementation efforts, and they provide runtime services for supporting self-managing behavior. This dissertation presents a novel software framework, called StarMX, for developing adaptive and self-managing Java-based systems. It is a generic configurable framework based on standards and well-established principles, and provides the required features and facilities for the development of such systems. It extensively supports Java Management Extensions (JMX) and is capable of integrating with different policy engines. This allows the developer to incorporate and use these techniques in the design of a control loop in a flexible manner. The control loop is created as a chain of entities, called processes, such that each process represents one or more functions of the loop (monitoring, analyzing, planning, and executing). A process is implemented by either a policy language or the Java language. At runtime, the framework invokes the chain of processes in the control loop, providing each one with the required set of objects for monitoring and effecting. An open source Java-based Voice over IP system, called CC2, is selected as the case study used in a set of experiments that aim to capture a solid understanding of the framework suitability for developing adaptive systems and to improve its feature set. The experiments are also used to evaluate the performance overhead incurred by the framework at runtime. The performance analysis results show the execution time spent in different components, including the framework itself, the policy engine, and the sensors/effectors. The results also reveal that the time spent in the framework is negligible, and it has no considerable impact on the system's overall performance

An Enhanced Goal-Oriented Decision-Making Model for Self-Adaptive Systems

The thesis proposes a generic, configurable and enhanced goal-oriented decision-making model for self-adaptive software systems. The model has been designed to include feedback control loops as first class entities in the adaptation process whereby the decision-making processes can assess the impact of a previously executed decision, so that better decisions can be made in the future. Furthermore, the model provides the ability to detect and resolve conflicts amongst dependant adaptation requirements. The realization of the decision-model is extremely generic, flexible and extensible. It allows different voting algorithms to be specified for choosing a winner requirement for clusters of flexible adaptation requirements. Moreover, the implementation also allows for the specification of a wide variety of reinforcement learning algorithms to assess the impact of a previously executed decision. The implementation has been developed as a plug-in for a generic Java-based adaptation framework. It was tested using two case studies namely a News Web Application and an IP Telephony System. The aim of the conducted experiments was to assess the impact of the model on the systems goals and to determine the impact of feedback control loops as first class entities in the decision-making process. Based on the obtained results, it can be concluded that the model does improve the overall customer satisfaction level compared to a non-adaptive system. Moreover, it will be concluded that incorporating feedback loops as first class entities yields better results as compared to a decision-making model based solely on policies or goals

A Quality-Driven Approach to Enable Decision-Making in Self-Adaptive Software

Self-adaptive software systems are increasingly in demand. The driving forces are changes in the software “self” and “context”, particularly in distributed and pervasive applications. These systems provide self-* properties in order to keep requirements satisfied in different situations. Engineering self-adaptive software normally involves building the adaptable software and the adaptation manager. This PhD thesis focuses on the latter, especially on the design and implementation of the deciding process in an adaptation manager. For this purpose, a Quality-driven Framework for Engineering an Adaptation Manager (QFeam) is proposed, in which quality requirements play a key role as adaptation goals. Two major phases of QFeam are building the runtime adaptation model and designing the adaptation mechanism. The modeling phase investigates eliciting and specifying key entities of the adaptation problem space including goals, attributes, and actions. Three composition patterns are discussed to link these entities to build the adaptation model, namely: goal-centric, attribute-action-coupling, and hybrid patterns. In the second phase, the adaptation mechanism is designed according to the adopted pattern in the model. Therefore, three categories of mechanisms are discussed, in which the novel goal-ensemble mechanism is introduced. A concrete model and mechanism, the Goal-Attribute-Action Model (GAAM), is proposed based on the goal-centric pattern and the goal-ensemble mechanism. GAAM is implemented based on the StarMX framework for Java-based systems. Several considerations are taken into account in QFeam: i) the separation of adaptation knowledge from application knowledge, ii) highlighting the role of adaptation goals, and iii) modularity and reusability. Among these, emphasizing goals is the tenet of QFeam, especially in order to address the challenge of addressing several self- * properties in the adaptation manager. Furthermore, QFeam aims at embedding a model in the adaptation manager, particularly in the goal-centric and hybrid patterns. The proposed framework focuses on mission-critical systems including enterprise and service-oriented applications. Several empirical studies were conducted to put QFeam into practice, and also evaluate GAAM in comparison with other adaptation models and mechanisms. Three case studies were selected for this purpose: the TPC-W bookstore application, a news application, and the CC2 VoIP call controller. Several research questions were set for each case study, and findings indicate that the goal-ensemble mechanism and GAAM can outperform or work as well as a common rule-based approach. The notable difference is that the effort of building an adaptation manager based on a goal-centric pattern is less than building it using an attribute-action-coupling pattern. Moreover, representing goals explicitly leads to better scalability and understandability of the adaptation manager. Overall, the experience of working on these three systems show that QFeam improves the design and development process of the adaptation manager, particularly by highlighting the role of adaptation goals

Policy-Driven Adaptive Protection Systems.

PhDThe increasing number and complexity of security attacks on IT infrastructure demands for the development of protection systems capable of dealing with the security challenges of today’s highly dynamic environments. Several converging trends including mobilisation, externalisation and collaboration, virtualisation, and cloud computing are challenging traditional silo approaches to providing security. IT security policies should be considered as being inherently dynamic and flexible enough to trigger decisions efficiently and effectively taking into account not only the current execution environment of a protection system and its runtime contextual factors, but also dynamically changing the security requirements introduced by external entities in the operational environment. This research is motivated by the increasing need for security systems capable of supporting security decisions in dynamic operational environments and advocates for a policy-driven adaptive security approach. The first main contribution of this thesis is to articulate the property of specialisation in adaptive software systems and propose a novel methodological framework for the realisation of policy-driven adaptive systems capable of specialisation via adaptive policy transformation. Furthermore, this thesis proposes three distinctive novel protection mechanisms, all three mechanisms exhibit adaptation via specialisation, but each one presenting its own research novelty in its respective field. They are: 1. A Secure Execution Context Enforcement based on Activity Detection; 2. Privacy and Security Requirements Enforcement Framework in Internet-Centric Services; 3. A Context-Aware Multifactor Authentication Scheme Based On Dynamic Pin. 3 Along with a comprehensive study of the state of the art in policy based adaptive systems and a comparative analysis of those against the main objectives of the framework this thesis proposes, these three protection mechanisms serve as a foundation and experimental work from which core characteristics, methods, components, and other elements are analysed in detail towards the investigation and the proposition of the methodological framework presented in this thesis