Utilising stream reasoning techniques to underpin an autonomous framework for cloud application platforms

As cloud application platforms (CAPs) are reaching the stage where the human effort required to maintain them at an operational level is unsupportable, one of the major challenges faced by the cloud providers is to develop appropriate mechanisms for run-time monitoring and adaptation, to prevent cloud application platforms from quickly dissolving into a non-reliable environment. In this context, the application of intelligent approaches to Autonomic Clouds may offer promising opportunities. In this paper we present an approach to providing cloud platforms with autonomic capabilities, utilising techniques from the Semantic Web and Stream Reasoning research fields. The main idea of this approach is to encode values, monitored within cloud application platforms, using Semantic Web languages, which then allows us to integrate semantically-enriched observation streams with static ontological knowledge and apply intelligent reasoning. Using such run-time reasoning capabilities, we have developed a conceptual architecture for an autonomous framework and describe a prototype solution we have constructed which implements this architecture. Our prototype is able to perform analysis and failure diagnosis, and suggest further adaptation actions. We report our experience in utilising the Stream Reasoning technique in this context as well as further challenges that arise out of our work

Quantifying volume, velocity, and variety to support (Big) data-intensive application development

© 2017 IEEE. In the era of digital economies, data can be considered as the new commodity, fueling the next-generation software services and applications. Increasing amounts of data, generated on a daily basis by various domains, such as social networks, stock exchanges, the Internet of Things, and cyber-physical systems, are soon expected to exceed the yottabyte1 frontier. To process this overwhelming amount, Big Data solutions are being developed to enable a new generation of data-centric/data-intensive applications (DIAs) and services. However, many of such applications currently fail to meet the increasingly demanding data management requirements. In particular, proper techniques and tools to support architects and developers in DIA design are required to cope with these pressing Big Data challenges. This paper makes an initial step in this direction, aiming at reducing the gap between the architects and DIAs they have to develop. The proposed approach extends the conventional Big Data process workflow with a way of capturing and modeling the 'three Vs' of Big Data (i.e. volume, velocity, and variety) to provide useful insights on the overall process, knowing the behavior of its individual components. Starting from the V-attributes of the Big Data process components, the proposed framework provides an estimation of its V-metrics by evaluating a performance model generated from the process. To demonstrate the feasibility and the effectiveness of the approach, a case study on a computer vision DIA is reported

Data agility through clustered edge computing and stream processing

© 2018 John Wiley & Sons, Ltd. The Internet of Things is underpinned by the global penetration of network-connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing – a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in-memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in-memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud-enabled architectures, and has the potential to address a wide range of IoT-related data-intensive time-critical scenarios