An Accounting Framework on Federated Service-Oriented Architecture

Service-Oriented Architecture (SOA) is used in more and more scientific and business applications. But self-examination and adaptation are overlooked by most system designers. Extant service accounting functionalities mainly focus on error causes, which are insufficient for history analysis and event prediction. This paper not only analyzes system events from both service consumers and providers, but also, starting from Maslow's needs hierarchy, provides a layered accounting framework for service federations. More than that, in matching and prediction model, a pipeline approach is employed rather than deterministic finite automaton (DFA), and a dependence estimator algorithm is introduced avoiding the deficiency in naive Bayes network for machine-learning and prediction. And then, based on these modules, a self-healing layer is built up to achieve decomposing, ranking, re-composing functionalities

Granular approach to adaptivity in problem-based learning

Constructivist approach to learning has been around for quite some time. The constructivist theory has resulted in the development of a wide variety of learning environments, however the problem-based learning (PBL) environment is one of the most ideal and most popular area that implements the constructivism theory. PBL is an attractive approach to foster learner's critical problem solving and self-directed learning skills. However, it is difficult to implement effective PBL environments. A majority of existing PBL environments suffers from the fact that the students easily get inundated by the fine granularity of the problems and loose focus of overall aims of the learning process. This project has introduced student adaptivity technology into PBL environments to improve the effectiveness and efficiency of the learning process. To demonstrate the idea of PBL with student adaptivity, a web-based prototype is implemented in Process Costing, within the field of Accounting. Based on the architecture of the web-based intelligent educational systems, the problem base module is introduced. The basic architecture of the system is a typical three-tier, client-server structure. The client tier has the presentation interfaces that are implemented as HTML frames and run in a web browser. The application programs for performing adaptation, which were developed using PHP, reside in the middle layer, and communicate directly with the backend database: problem base, knowledge base that is the third tier. The web server as the communication channel also resides in the middle tier. With the system, students work on the real world costing calculation problems, and the system evaluates students' performance results on the problems to provide adaptation to the students. In summary, this project has successfully introduced the student adaptivity into the PBL environment. The strategies used in this thesis can be applied into the pure PBL educational systems to improve their adaptation capability

Temporal Models for History-Aware Explainability

On one hand, there has been a growing interest towards the application of AI-based learning and evolutionary programming for self-adaptation under uncertainty. On the other hand, self-explanation is one of the self-* properties that has been neglected. This is paradoxical as self-explanation is inevitably needed when using such techniques. In this paper, we argue that a self-adaptive autonomous system (SAS) needs an infrastructure and capabilities to be able to look at its own history to explain and reason why the system has reached its current state. The infrastructure and capabilities need to be built based on the right conceptual models in such a way that the system's history can be stored, queried to be used in the context of the decision-making algorithms. The explanation capabilities are framed in four incremental levels, from forensic self-explanation to automated history-aware (HA) systems. Incremental capabilities imply that capabilities at Level n should be available for capabilities at Level n + 1. We demonstrate our current reassuring results related to Level 1 and Level 2, using temporal graph-based models. Specifically, we explain how Level 1 supports forensic accounting after the system's execution. We also present how to enable on-line historical analyses while the self-adaptive system is running, underpinned by the capabilities provided by Level 2. An architecture which allows recording of temporal data that can be queried to explain behaviour has been presented, and the overheads that would be imposed by live analysis are discussed. Future research opportunities are envisioned

TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation

The paper is concerned with the issue of how software systems actually use Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power consumption on these resources. It argues the need for novel methods and tools to support software developers aiming to optimise power consumption resulting from designing, developing, deploying and running software on HPAs, while maintaining other quality aspects of software to adequate and agreed levels. To do so, a reference architecture to support energy efficiency at application construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 7 pages, LaTeX, 3 PNG figure

A deliberative model for self-adaptation middleware using architectural dependency

A crucial prerequisite to externalized adaptation is an understanding of how components are interconnected, or more particularly how and why they depend on one another. Such dependencies can be used to provide an architectural model, which provides a reference point for externalized adaptation. In this paper, it is described how dependencies are used as a basis to systems' self-understanding and subsequent architectural reconfigurations. The approach is based on the combination of: instrumentation services, a dependency meta-model and a system controller. In particular, the latter uses self-healing repair rules (or conflict resolution strategies), based on extensible beliefs, desires and intention (EBDI) model, to reflect reconfiguration changes back to a target application under examination

Adaptability Checking in Multi-Level Complex Systems

A hierarchical model for multi-level adaptive systems is built on two basic levels: a lower behavioural level B accounting for the actual behaviour of the system and an upper structural level S describing the adaptation dynamics of the system. The behavioural level is modelled as a state machine and the structural level as a higher-order system whose states have associated logical formulas (constraints) over observables of the behavioural level. S is used to capture the global and stable features of B, by a defining set of allowed behaviours. The adaptation semantics is such that the upper S level imposes constraints on the lower B level, which has to adapt whenever it no longer can satisfy them. In this context, we introduce weak and strong adaptabil- ity, i.e. the ability of a system to adapt for some evolution paths or for all possible evolutions, respectively. We provide a relational characterisation for these two notions and we show that adaptability checking, i.e. deciding if a system is weak or strong adaptable, can be reduced to a CTL model checking problem. We apply the model and the theoretical results to the case study of motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte

Learning a world model and planning with a self-organizing, dynamic neural system

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing self-organizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.Comment: 9 pages, see http://www.marc-toussaint.net

Self-managed cells and their federation

Future e-Health systems will consist of low-power, on-body wireless sensors attached to mobile users that interact with a ubiquitous computing environment. This kind of system needs to be able to configure itself with little or no user input; more importantly, it is required to adapt autonomously to changes such as user movement, device failure, the addition or loss of services, and proximity to other such systems. This extended abstract describes the basic architecture of a Self-Managed Cell (SMC) to address these requirements, and discusses various forms of federation between/among SMCs. This structure is motivated by a typical e-Health scenario

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour