An Embryonics Inspired Architecture for Resilient Decentralised Cloud Service Delivery

Data-driven artificial intelligence applications arising from Internet of Things technologies can have profound wide-reaching societal benefits at the cross-section of the cyber and physical domains. Usecases are expanding rapidly. For example, smart-homes and smart-buildings provide intelligent monitoring, resource optimisation, safety, and security for their inhabitants. Smart cities can manage transport, waste, energy, and crime on large scales. Whilst smart-manufacturing can autonomously produce goods through the self-management of factories and logistics. As these use-cases expand further, the requirement to ensure data is processed accurately and timely is ever crucial, as many of these applications are safety critical. Where loss off life and economic damage is a likely possibility in the event of system failure. While the typical service delivery paradigm, cloud computing, is strong due to operating upon economies of scale, their physical proximity to these applications creates network latency which is incompatible with these safety critical applications. To complicate matters further, the environments they operate in are becoming increasingly hostile. With resource-constrained and mobile wireless networking, commonplace. These issues drive the need for new service delivery architectures which operate closer to, or even upon, the network devices, sensors and actuators which compose these IoT applications at the network edge. These hostile and resource constrained environments require adaptation of traditional cloud service delivery models to these decentralised mobile and wireless environments. Such architectures need to provide persistent service delivery within the face of a variety of internal and external changes or: resilient decentralised cloud service delivery. While the current state of the art proposes numerous techniques to enhance the resilience of services in this manner, none provide an architecture which is capable of providing data processing services in a cloud manner which is inherently resilient. Adopting techniques from autonomic computing, whose characteristics are resilient by nature, this thesis presents a biologically-inspired platform modelled on embryonics. Embryonic systems have an ability to self-heal and self-organise whilst showing capacity to support decentralised data processing. An initial model for embryonics-inspired resilient decentralised cloud service delivery is derived according to both the decentralised cloud, and resilience requirements given for this work. Next, this model is simulated using cellular automata, which illustrate the embryonic concept’s ability to provide self-healing service delivery under varying system component loss. This highlights optimisation techniques, including: application complexity bounds, differentiation optimisation, self-healing aggression, and varying system starting conditions. All attributes of which can be adjusted to vary the resilience performance of the system depending upon different resource capabilities and environmental hostilities. Next, a proof-of-concept implementation is developed and validated which illustrates the efficacy of the solution. This proof-of-concept is evaluated on a larger scale where batches of tests highlighted the different performance criteria and constraints of the system. One key finding was the considerable quantity of redundant messages produced under successful scenarios which were helpful in terms of enabling resilience yet could increase network contention. Therefore balancing these attributes are important according to use-case. Finally, graph-based resilience algorithms were executed across all tests to understand the structural resilience of the system and whether this enabled suitable measurements or prediction of the application’s resilience. Interestingly this study highlighted that although the system was not considered to be structurally resilient, the applications were still being executed in the face of many continued component failures. This highlighted that the autonomic embryonic functionality developed was succeeding in executing applications resiliently. Illustrating that structural and application resilience do not necessarily coincide. Additionally, one graph metric, assortativity, was highlighted as being predictive of application resilience, although not structural resilience

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures

Knowledge-based web services for context adaptation.

The need for higher value, reliable online services to promote new Internet-based business models is a requirement facing many technologists and business leaders. This need coupled with the trend towards greater mobility of networked devices and consumers creates significant challenges for current and future systems developers. The proliferation of mobile devices and the variability of their capabilities present an overwhelming number of options to systems designers and engineers who are tasked with the development of next generation context adaptive software services. Given the dynamic nature of this environment, implementing solutions for the current set of devices in the held makes an assumption that this deployment situation is somehow fixed this assumption does little to support the future and longer term needs within the marketplace. To add to the complexity, the timeframes necessary to develop robust and adaptive online software services can be long by comparison, so that the development projects and their resources are often behind on platform support before the first release is launched to the public. New approaches and methodologies for engineering dynamic and adaptive online services will be necessary and, as will be shown, are in fact mandated by the regulation imposed by service level guarantees. These new techniques and technology are commercially useless unless they can be used in engineering practice. New context adaptation processes and architectures must be capable of performing under strict service level agreements those that will undoubtedly govern future business relationships between online parties. This programme of engineering study and research investigates several key issues found in the emerging area of context adaptation services for online mobile networks. As a series of engineering investigations, the work described here involves a wider array of technical activity than found in traditional doctoral work and this is reflected throughout the dissertation. First, a clear definition of industrial motivation is stated to provide the engineering foundation. Next, the programme focuses on the nature of contextual adaptation through product development projects. The development process within these projects results in several issues with the commercial feasibility of the technology. From this point, the programme of study then progresses through the lifecycle of the engineering process, investigating at each stage the critical engineering challenges. Further analysis of the problems and possible solutions for deploying such adaptive solutions are reviewed and experiments are undertaken in the areas of systems component and performance analysis. System-wide architectural options are then evaluated with specific interest in using knowledge-base systems as one approach to solving some of the issues in context adaptation. The central hypothesis is that due to the dynamic nature of context parameters, the concept of a mobile device knowledge base as a necessary component of an architectural solution is presented and justified through prototyping efforts. The utility of web ontologies and other "soft computing" technologies on the nature of the solution are also examined through the review of relevant work and the engineering design of the demonstration system. These technology selections are supported directly by the industrial context and mission. In the final sections, the architecture is evaluated through the demonstration of promising techniques and methods in order to confirm understanding and to evaluate the use of knowledge-bases, AI and other technologies within the scope of the project. Through the implementation of a context adaptation architecture as a business process workflow, the impact of future trends of device reconfiguration are highlighted and discussed. To address the challenge of context adaptation in reconftgurable device architectures, an evolutionary computation approach is then presented as a means to provide an optimal baseline on which a service may execute. These last two techniques are discussed and new designs are proposed to specifically address the major issues uncovered in timely collection and evaluation of contextual parameters in a mobile service network. The programme summary and future work then brings together all the key results into a practitioner's reference guide for the creation of online context adaptive services with a greater degree of intelligence and maintainability while executing with the term of a service level agreement

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Factories of the Future

Engineering; Industrial engineering; Production engineerin