Combining SLA prediction and cross layer adaptation for preventing SLA violations

Abstract. Service-based Applications (SBA) are deployed in highly dy-namic and distributed settings, where various parts of the constituent components- services and their infrastructure- are controlled by dif-ferent third parties. In such a loosely coupled environment, adaptation capabilities are needed to manage deviations and unforeseen situations which might lead to negative consequences (e.g. contractual penalties). Current approaches either focus on cross-layer-adaptation or the pre-vention of SLA violations. In contrast to this, the approach presented in this paper combines both. The paper presents an architecture as a generic framework for the management of arising problems during ser-vice execution. Multiple adaptation mechanisms are available to react on adaptation needs, acting on different layers of the SBA (including e.g. the composition layer and the infrastructure layer). The final goal of the cross-layer adaptation capability is to avoid the violation of agreed Service Level (in SLAs) and thus ensure the benefits of SBAs for both customers and providers.

Developing and operating time critical applications in clouds: the state of the art and the SWITCH approach

Cloud environments can provide virtualized, elastic, controllable and high quality on-demand services for supporting complex distributed applications. However, the engineering methods and software tools used for developing, deploying and executing classical time critical applications do not, as yet, account for the programmability and controllability provided by clouds, and so time critical applications cannot yet benefit from the full potential of cloud technology. This paper reviews the state of the art of technologies involved in developing time critical cloud applications, and presents the approach of a recently funded EU H2020 project: the Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications (SWITCH). SWITCH aims to improve the existing development and execution model of time critical applications by introducing a novel conceptual model—the application-infrastructure co-programming and control model—in which application QoS and QoE, together with the programmability and controllability of cloud environments, is included in the complete application lifecycle

Conceptualising capabilities and value co-creation in a digital business ecosystem (DBE): a systematic literature review

Digital Business Ecosystem (DBE) is a topical concept for business organisations to collaborate in driving product or service innovation. DBE is supported by digital technologies which aim to create and co-create values among the participated business organisations. For achieving successful collaboration, business organisations need to understand their capabilities that lead to value creation. This approach is vital for a business organisation to benefit from the values co-created when collaborating with others. Failing to do so will cause inefficient collaboration. However, there is a lack of capability and value co-creation studies in the DBE context. Therefore, this paper aims to conceptualise capabilities and value co-creation through a systematic literature review. We analysed the findings by thematic analysis. The review results produced a set of research themes surrounding the capability and value co-creation concepts. The research themes contribute to informing future avenues in digital business ecosystem research

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

SCALABLE PROCESSING OF MULTIPLE AGGREGATE CONTINUOUS QUERIES

Data Stream Management Systems (DSMSs) were developed to be at the heart of every monitor- ing application. Monitoring applications typically register hundreds of Continuous Queries (CQs) in DSMSs in order to continuously process unbounded data streams to detect events of interest. DSMSs must be designed to efficiently handle unbounded streams with large volumes of data and large numbers of CQs, i.e., exhibit scalability. This need for scalability means that the underlying processing techniques a DSMS adopts should be optimized for high throughput (i.e., tuple output rate). Towards this, two main approaches were proposed in the literature: (1) Multiple Query Opti- mization (MQO) and (2) Scheduling. In this dissertation we focus on optimizing the processing of multiple Aggregate Continuous Queries (ACQs), given their high processing cost and popularity in all monitoring applications. Specifically, in this dissertation, we explore shared processing of ACQs and introduce the con- cept of ’Weaveability’ as an indicator of the potential gains of sharing the processing of ACQs. We develop Weave Share, a multiple ACQs optimizer that considers the different uncorrelated factors of the processing cost, such as the input rate and ACQs’ specifications. In order to fully reap the benefits of the new weave-based optimization techniques, we conceptualize a new underlying ag- gregate operator implementation and realize it in the TriOps framework. TriOps enables adaptive sharing of multiple ACQs that have different window specification, predicates and group-by at- tributes. The properties of the proposed techniques are studied analytically and their performance advantages are experimentally evaluated using simulation and in the context of the AQSIOS DSMS prototype

Active aging in place supported by caregiver-centered modular low-cost platform

Aging in place happens when people age in the residence of their choice, usually their homes because is their preference for living as long as possible. This research work is focused on the conceptualization and implementation of a platform to support active aging in place with a particular focus on the caregivers and their requirements to accomplish their tasks with comfort and supervision. An engagement dimension is also a plus provided by the platform since it supports modules to make people react to challenges, stimulating them to be naturally more active. The platform is supported by IoT, using low-cost technology to increment the platform modularly. Is a modular platform capable of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding the person under supervision, as well as providing conditions for constant and more effective monitoring, through modules and tools that support decision making and tasks realization for active living. The constant monitoring allows knowing the routine of daily activities of the senior. The use of machine learning techniques allows the platform to identify, in real-time, situations of potential risk, allowing to trigger triage processes with the older adult, and consequently trigger the necessary actions so that the caregiver can intervene in useful time.O envelhecimento no local acontece quando as pessoas envelhecem na residência da sua escolha, geralmente nas suas próprias casas porque é a sua preferência para viver o máximo de tempo possível. Este trabalho de investigação foca-se na conceptualização e implementação de uma plataforma de apoio ao envelhecimento ativo no local, com particular enfoque nos cuidadores e nas suas necessidades para cumprir as suas tarefas com conforto e supervisão. Uma dimensão de engajamento também é um diferencial da plataforma, pois esta integra módulos de desafios para fazer as pessoas reagirem aos mesmos, estimulando-as a serem naturalmente mais ativas. A plataforma é suportada por IoT, utilizando tecnologia de baixo custo para incrementar a plataforma de forma modular. É uma plataforma modular capaz de responder às necessidades específicas do envelhecimento dos idosos no local e dos seus cuidadores, obtendo dados relativos à pessoa sob supervisão, bem como fornecendo condições para um acompanhamento constante e mais eficaz, através de módulos e ferramentas que apoiam a tomada de decisões e realização de tarefas para a vida ativa. A monitorização constante permite conhecer a rotina das atividades diárias do idoso, permitindo que, com a utilização de técnicas de machine learning, a plataforma seja capaz de detetar em tempo real situações de risco potencial, permitindo desencadear um processo de triagem junto do idoso, e consequentemente despoletar as ações necessárias para que o prestador de cuidados possa intervir em tempo útil