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The emergence of new computing paradigms such as fog and edge computing provides the Internet of Things with needed connectivity and high availability. In the context of e-health systems, wearable sensors are being used to continuously collect information about our health, and forward it for processing by the Internet of Medical Things (IoMT). E-health systems are designed to assist subjects in real-time by providing them with a range of multimedia-based health services and personalised treatment with the promise of reducing the economic burden on health systems. Nonetheless, any service downtime, particularly in the case of emergency services, can lead to adverse outcomes and in the worst case, loss of life. In this paper, we use an interdisciplinary approach that combines stochastic models with surrogate-assisted optimisation algorithms to maximise e-health system availability considering the budget to acquire redundant components as a constraint, comparing three nature-inspired meta-heuristic optimisation algorithms.

With the growth of cloud computing usage by commercial companies, service providers are looking for ways to improve the quality of their services. Failures in the power subsystem represent a major risk of cloud data centre unavailability at the physical level. At same time, software-level mechanisms (such as application checkpointing) can be used to maintain the application consistency after a downtime and also improve its availability. This paper analyses the impact of power subsystem failures on cloud applications availability, as well as the impact of having checkpoint mechanisms to recover the system from software-level failure. We propose stochastic models to represent the cloud power subsystem, the cloud application, and also the checkpoint-based retrieval mechanisms. The results show that the selection of a given type of checkpoint mechanism does not present a significant impact on the observed metrics. On the other hand, improving the power infrastructure implies gains in performance and availability.

Mobile edge computing (MEC) is a network architecture that takes advantage of cloud computing features (such as high availability and elasticity) and makes use of computational resources available at the edge of the network in order to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users. However, the request placement depends not only on the geographical location of the servers, but also on their requirements. Based on this fact, this paper proposes a stochastic Petri net (SPN) model to represent an MEC scenario and analyse its performance, focusing on the parameters that can directly impact the service mean response time (MRT) and resource utilisation level. In order to present the applicability of our work, we propose three case studies with numerical analysis using real-world values. The main objective is to provide a practical guide to help infrastructure administrators to adapt their architectures, finding a trade-off between MRT and level of resource usage.

E-health systems can be used to monitor people in real-time, offering a range of multimedia-based health services, at the same time reducing the cost since cheaper devices can be used to compose it. However, any downtime, mainly in the case of critical health services, can result in patient health problems and in the worst case, loss of life. In this paper, we use an interdisciplinary approach combining stochastic models with optimisation algorithms to analyse how failures impact e-health monitoring system availability. We propose surrogate models to estimate the availability of e-health monitoring systems that rely on edge, fog, and cloud infrastructures. Then, we apply a multi-objective optimisation algorithm, NSGA-II, to improve system availability considering component costs as constraint. Results suggest that replacing components with more reliable ones is more effective in improving the availability of an e-health monitoring system than adding more redundant components.

Large data centers are complex systems that depend on several generations of hardware and software components, ranging from legacy mainframes and rack-based appliances to modular blade servers and modern rack scale design solutions. To cope with this heterogeneity, the data center manager must coordinate a multitude of tools, protocols, and standards. Currently, data center managers, standardization bodies, and hardware/software manufacturers are joining efforts to develop and promote Redfish as the main hardware management standard for data centers, and even beyond the data center. The authors hope that this article can be used as a starting point to understand how Redfish and its extensions are being targeted as the main management standard for next-generation data centers. This article describes Redfish and the recent collaborations to leverage this standard.

Although cloud infrastructures can be used as High Performance Computing (HPC) platforms, many issues from virtualisation overhead have kept them almost unrelated. However, with advent of container-based virtualisation, this scenario acquires new perspectives because this technique promises to decrease the virtualisation overhead, achieving a near-native performance. In this work, we analyse the performance of a container-based virtualisation solution - Linux Container (LXC) - against a hypervisor-based virtualisation solution - KVM - under HPC activities. For our experiments, we consider CPU and (network and inter-process) communication performance. Results show that hypervisor type can impact distinctly in performance according to resource used by HPC application.