Automatic Adaptation of SOA Systems Supported by Machine Learning

Part 3: Service OrientationInternational audienceRecent advances in the development of information systems have led to increased complexity and cost in terms of the required maintenance and management. On the other hand, systems built in accordance with modern architectural paradigms, such as Service Oriented Architecture (SOA), posses features enabling extensive adaptation, not present in traditional systems. Automatic adaptation mechanisms can be used to facilitate system management. The goal of this work is to show that automatic adaptation can be effectively implemented in SOA systems using machine learning algorithms. The presented concept relies on a combination of clustering and reinforcement learning algorithms. The paper discusses assumptions which are necessary to apply machine learning algorithms to automatic adaptation of SOA systems, and presents a machine learning-based management framework prototype. Possible benefits and disadvantages of the presented approach are discussed and the approach itself is validated with a representative case study

Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm

An important aspect of modern computer systems is their ability to adapt. This is particularly important in the context of the use of mobile devices, which have limited resources and are able to work longer and more efficiently through adaptation. One possibility for the adaptation of mobile service execution is the use of the Mobile Cloud Computing (MCC) paradigm, which allows such services to run in computational clouds and only return the result to the mobile device. At the same time, the importance of machine learning used to optimize various computer systems is increasing. The novel concept proposed by the authors extends the MCC paradigm to add the ability to run services on a PC (e.g. at home). The solution proposed utilizes agent-based concepts in order to create a system that operates in a heterogeneous environment. Machine learning algorithms are used to optimize the performance of mobile services online on mobile devices. This guarantees scalability and privacy. As a result, the solution makes it possible to reduce service execution time and power consumption by mobile devices. In order to evaluate the proposed concept, an agent-based system for mobile service adaptation was implemented and experiments were performed. The solution developed demonstrates that extending the MCC paradigm with the simultaneous use of machine learning and agent-based concepts allows for the effective adaptation and optimization of mobile services

Closed-loop two-echelon repairable item systems

In this paper we consider closed loop two-echelon repairable item systems with repair facilities both at a number of local service centers (called bases) and at a central location (the depot). The goal of the system is to maintain a number of production facilities (one at each base) in optimal operational condition. Each production facility consists of a number of identical machines which may fail incidentally. Each repair facility may be considered to be a multi-server station, while any transport from the depot to the bases is modeled as an ample server. At all bases as well as at the depot, ready-for-use spare parts (machines) are kept in stock. Once a machine in the production cell of a certain base fails, it is replaced by a ready-for-use machine from that base's stock, if available. The failed machine is either repaired at the base or repaired at the central repair facility. In the case of local repair, the machine is added to the local spare parts stock as a ready-for-use machine after repair. If a repair at the depot is needed, the base orders a machine from the central spare parts stock to replenish its local stock, while the failed machine is added to the central stock after repair. Orders are satisfied on a first-come-first-served basis while any requirement that cannot be satisfied immediately either at the bases or at the depot is backlogged. In case of a backlog at a certain base, that base's production cell performs worse. To determine the steady state probabilities of the system, we develop a slightly aggregated system model and propose a special near-product-form solution that provides excellent approximations of relevant performance measures. The depot repair shop is modeled as a server with state-dependent service rates, of which the parameters follow from an application of Norton's theorem for Closed Queuing Networks. A special adaptation to a general Multi-Class MDA algorithm is proposed, on which the approximations are based. All relevant performance measures can be calculated with errors which are generally less than one percent, when compared to simulation results. \u

Interoperable subject retrieval in a distributed multi-scheme environment : new developments in the HILT project

The HILT (HIgh-Level Thesaurus) project (http://hilt.cdlr.strath.ac.uk/), based primarily at the Centre for Digital Library Research (CDLR) (http://cdlr.strath.ac.uk/) at Strathclyde University in Glasgow is entering its fourth stage following the completion of Phases I (http://hilt.cdlr.strath.ac.uk/index1.html) and II (http://hilt.cdlr.strath.ac.uk/index2.html) and the Machine to Machine (M2M) Feasibility Study (http://hilt.cdlr.strath.ac.uk/hiltm2mfs/). HILT is funded by the Joint Information Systems Committee (JISC) in the United Kingdom (UK) to examine an issue of global significance - facilitating interoperability of subject descriptions in a distributed, cross-service retrieval environment where different services use different subject and classification schemes to describe content, making cross-searching by subject difficult. HILT Phase I determined that there was a community consensus in the UK in favour of using inter-scheme mapping to achieve interoperability between services using different schemes, an approach followed by several recent projects (Heery et al, 2001; Koch et al, 2001; MACS, 2005; Saeed and Chaudhury 2002). HILT Phase II chose a spine-based approach to mapping and chose the Dewey Decimal Classification (DDC) as the central scheme to which all other schemes would be mapped. It also built an illustrative pilot mapping service, based on an adaptation of the Wordmap (http://www.wordmap.com/) terminology-handling software and made a range of recommendations on issues requiring further research and ongoing development requirements

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance

Self-adaptive system (SAS) can adapt itself to optimize various key performance indicators in response to the dynamics and uncertainty in environment. In this paper, we present Debt Learning Driven Adaptation (DLDA), an framework that dynamically determines when and whether to adapt the SAS at runtime. DLDA leverages the temporal adaptation debt, a notion derived from the technical debt metaphor, to quantify the time-varying money that the SAS carries in relation to its performance and Service Level Agreements. We designed a temporal net debt driven labeling to label whether it is economically healthier to adapt the SAS (or not) in a circumstance, based on which an online machine learning classifier learns the correlation, and then predicts whether to adapt under the future circumstances. We conducted comprehensive experiments to evaluate DLDA with two different planners, using 5 online machine learning classifiers, and in comparison to 4 state-of-the-art debt- oblivious triggering approaches. The results reveal the effectiveness and superiority of DLDA according to different metrics

A knowledge based machine tool maintenance planning system Using case-based reasoning techniques

In advanced manufacturing systems, Computer Numerical Control (CNC) machine tools are important equipment to manufacture product components of high precision, whilst from equipment maintenance point of view, they are regarded as the ‘products’ provided by machine tool manufacturers. Therefore, the reliability of CNC machine tools affects not only the quality of the components they manufacture, but also the reputation and profits of equipment suppliers. This paper presents a novel knowledge-based maintenance planning system to facilitate information and knowledge sharing between all stakeholders including machine tool manufacturers, users (manufacturing systems), maintenance service providers and part suppliers (for machine tools), in the emerging ‘Product-Service’ business model. Case Based Reasoning principles have been implemented to improve the efficiency of maintenance planning. Ontologies were adopted to represent field knowledge using adaptation guided retrievals based on semantic similarity and correlation. The adaption algorithm has been developed based on the Casual Theory and the dependence relationship to generate the solution for required maintenance problems. The proposed system was implemented using Content Management technologies, which proved to have advantages over traditional database systems in managing engineering knowledge, and has been verified using an example CNC machine tool. The results were commented by industrial collaborators as very promising and further exploitation in industry was recommended

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

Probability-based opportunity dynamic adaptation (PODA) of contention window for home M2M networks

With the emergence of the Internet of Things (IoT), the growing use of autonomous sensing and actuating devices in areas such as smart grid, e-healthcare, home networking, and machine-to-machine (M2M) communication has become an important communication paradigm. Nonetheless, to fully exploit the applications facilitated by M2M communication, service requirements such as data throughput, scalability and reliability must be in place. This paper proposes a new backoff adaptation mechanism known as probability-based opportunity dynamic adaptation (PODA) for M2M communication using the IEEE 802.11ah protocol. The proposed PODA is an enhanced version of the binary exponential backoff (BEB) where a station estimates the number of contending stations in a distributed manner and adaptively tunes its minimum contention window (CW) prior to the contention process for better network throughput and packet delivery ratio. Owing to its great flexibility and ease of implementation, BEB has been extended to home M2M communication such as wireless sensor networks and smart grid technologies without relying on wide area communication. However, the current form of BEB has its shortcomings in the emerging M2M paradigm. The adaptation of CW in PODA is based on the optimal station's access opportunity to improve network performance instead of direct CW scaling. Using the proposed adaptation method, the network throughput can be improved by as much as 18 percent in the home M2M network studied, while enhancing network reliability and fairness