Optimal Autonomic Management of Service-Based Business Processes in the Cloud

Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.

Autonomic Security Management for IoT Smart Spaces

Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.

Designing Run-time Evolution for Dependable and Resilient Cyber-Physical Systems Using Digital Twins

The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.

A Programming Approach to Collective Autonomy

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.