An Ontological Framework for Opportunistic Composition of IoT Systems

As the number of connected devices rapidly increases, largely thanks to uptake of IoT technologies, there is significant stimulus to enable opportunistic interactions between different systems that encounter each other at run time. However, this is complicated by diversity in IoT technologies and implementation details that are not known in advance. To achieve such unplanned interactions, we use the concept of a holon to represent a system's services and requirements at a high level. A holon is a self-describing system that appears as a whole when viewed from above whilst potentially comprising multiple sub-systems when viewed from below. In order to realise this world view and facilitate opportunistic system interactions, we propose the idea of using ontologies to define and program a holon. Ontologies offer the ability to classify the concepts of a domain, and use this formalised knowledge to infer new knowledge through reasoning. In this paper, we design a holon ontology and associated code generation tools. We also explore a case study of how programming holons using this approach can aid an IoT system to self-describe and reason about other systems it encounters. As such, developers can develop system composition logic at a high-level without any preconceived notions about low-level implementation details. © 2020 IEEE

Self-awareness in Software Engineering:A systematic literature review

Background: Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments. Objective: We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems. Method: We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in software engineering and explore how self-awareness is engineered and incorporated in software systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others. Results: Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. Though there have been recent attempts to define and engineer self-awareness in software engineering, there is no consensus on the definition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated. Conclusions: Our survey reveals that self-awareness for software systems is still a formative field and that there is growing attention to incorporate self-awareness for better reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems outline possible research directions

Cloud service brokerage: Exploring characteristics and benefits of B2B cloud marketplaces

With the increasing popularity of cloud computing, a new technology and business model called cloud service brokerage (CSB) is emerging. CSB is, in essence, a middleman in the cloud-computing supply chain to connect prospective cloud buyers with suitable service providers. This chapter focuses on a type of CSB, B2B cloud marketplaces. Recently, this type of marketplace has evolved into two broad categories—business application marketplaces and API marketplaces. This chapter reviews the characteristics of B2B cloud marketplaces, and their benefits, which include ease-of-use and ease-of-integration, enhanced security, increased manageability, faster implementation, and cost reduction. The chapter concludes with two mini-case studies, on Salesforce AppExchange and RapidAPI, to illustrate how firms could use B2B cloud marketplaces to generate, capture and measure business value

SLO-ML:A Language for Service Level Objective Modelling in Multi-cloud applications

Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow

Adaptive Service Deployment using In-Network Mediation

Serendipitous peer discovery is important for emerging Internet applications, particularly in dynamic environments (e.g., the IoT, ubiquitous and fog domains) where a large number of resources operate different services in any one locality and resource availability varies unpredictably over time. The current approach is to select services at design time based on offered providers and their reputation. This obviously has its limitations, particularly in terms of scalability and adaptivity, let alone the challenges of crossing vendor and operator divides. This work demonstrates how an application is better able to dynamically adapt to unforeseen environmental changes through in-network mediation of service requests. In our model, application developers express their service needs using intents. These are mapped to appropriate service providers with explicit consideration of the intermediate network. We design a general architecture and associated algorithms to realise intent formulation and processing for mapping application intents to service providers. Our results demonstrate the feasibility of adopting in-network mediation to enable adaptive application deployment using declarative intents