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The transmission of medical data and the possibility for distant healthcare structures to share experiments about a given medical case raises several conceptual and technical questions. Good remote healthcare monitoring deals with more problems in personalized heath data processing compared to the traditional methods nowadays used in several parts of hospitals in the world. The adoption of telemedicine in the healthcare sector has significantly changed medical collaboration. However, to provide good telemedicine services through new technologies such as cloud computing, cloud storage, and so on, a suitable and adaptable framework should be designed. Moreover, in the chain of medical information exchange, between requesting agencies, including physicians, a secure and collaborative platform enhanced the decision-making process. This paper provides an in-depth literature review on the interaction that telemedicine has with cloud-based computing. On the other hand, the paper proposes a framework that can allow various research organizations, healthcare sectors, and government agencies to log data, develop collaborative analysis, and support decision-making. The electrocardiogram (ECG) and electroencephalogram EEG case studies demonstrate the benefit of the proposed approach in data reduction and high-fidelity signal processing to a local level; this can make possible the extracted characteristic features to be communicated to the cloud database. © 2021 International Measurement Confederation (IMEKO). All rights reserved.

A common practice to store data is to use remote Cloud-based storage systems. However, storing files in remote services can arise privacy and security issues, for example, they can be attacked or even discontinued. A possible solution to solve this problem is to split files into chunks and add redundancy by means of Secret Share techniques. When it comes to Internet of Things (IoT), Edge and Cloud environments, these techniques have not been evaluated for the purpose of storing files. This work aims to address this issue by evaluating two of the most common Secret Share algorithms in order to identify their suitability for different environments, while considering the size of the file and the availability of resources. In particular, we analysed Shamir's Secret Share schema and the Redundant Residue Number System (RRNS) to gauge their efficiency regarding storage requirement and execution time. We made our experiments for different file sizes (from 1kB up to 500MB), number of parallel threads (1 to 4) and data redundancy (0 to 7) in all aforementioned environments. Results were promising and showed that, for example, to have seven degrees of redundancy, Shamir uses eight times more storage than RRNS; or, Shamir is faster than RRNS for small files (up to 20 kB). We also discovered that the environment on which the computation should be performed depends on both file size and algorithm. For instance, when employing RRNS, files up to 500kB can be processed on the IoT, up to 50MB on the Edge, and beyond that on the Cloud; whereas, in Shamir's schema, the threshold to move the computation from the IoT to the Edge is about 50kB, and from the Edge to the Cloud is about 500kB., HITS

In modern software systems, deployment is an integral and critical part of application development (see, e.g., the DevOps approach to software development). Nevertheless, deployment is usually overlooked at the modeling level, thus losing the possibility to perform deployment conscious decisions during the early stages of development. In this paper, we address the problem of promoting deployment as an integral part of modeling, by focusing on the Abstract Behavioral Specification (ABS) language used for the specification of models of systems composed of concurrent objects consuming resources provided by deployment components. We extend ABS with class annotations expressing the resource requirements of the objects of that class. Then we define a tool that, starting from a high-level declaration of the desired system, computes a model instance of such system that optimally distributes objects over available deployment components.

In this paper, we introduce a formal model of the availability, budget compliance and sustainability of istributed services, where service sustainability is a new concept which arises as the composition of service availability and budget compliance. The model formalizes a distributed platform for monitoring the above service characteristics in terms of a parallel composition of task automata, where dynamically generated tasks model asynchronous events with deadlines. The main result of this paper is a formal model to optimize and reason about service characteristics through monitoring. In particular, we use schedulability analysis of the underlying timed automata to optimize and guarantee service sustainability.

As cloud computing becomes more predominant, the problem of scalability has become critical for cloud computing providers. The cloud paradigm is attractive because it offers a dramatic reduction in capital and operation expenses for consumers.