In Business Process Management, the decentralized execution of business processes is one of the main research topics. Several models for processes’ modelling in order to facilitate their execution, have been proposed. LSAWfP is among the most recent in this area: it helps to specify administrative processes with grammatical models indicating, in addition to their fundamental elements, the permissions (reading, writing and execution) of each actor in relation to tasks. In this paper, we present a model for a completely decentralized execution of processes specified using LSAWfP. The presented model puts emphasis on actors’ views: it then allows the confidential execution of certain tasks by ensuring that, each actor potentially has a partial perception of the processes’ states. The model thus solves an often sidelined problem in business process execution. To accomplish this, it relies on three projection algorithms allowing to partially replicate the processes’ global execution states (artifact projection algorithm), to consistently update the obtained partial states (model projection algorithm) and to deduce new coherent global states (expansion-pruning algorithm). The proposal of these algorithms, the proof of underlying mathematical tools’ stability and a proposal of their implementation, are this paper’s contributions.

Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent request retransmissions, which are not well addressed by current state-of-the-art solutions. In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. By analyzing application keywords and edge server metrics, such as geographic location, workload, and keyword-based request frequency, the proposed solution dynamically selects the optimal edge server for application deployment. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and minimized retransmissions between servers. Compared to existing approaches, the proposed model provides a more robust solution to the complex and dynamic requirements of edge computing environments.

In Business Process Management (BPM), process modelling has been solved in various ways. However, there are no commonly accepted modelling tools (languages). Some of them are criticized for their inability to capture both the lifecycle, informational and organizational models of processes. For some others, process modelling is generally done using a single graph; this does not facilitate modularity, maintenance and scalability. In addition, some of these languages are very general; hence, their application to specific domain processes (such as administrative processes) is very complex. In this paper, we present a new language and a new methodology, dedicated to administrative process modelling. This language is based on a variant of attributed grammars and is able to capture the lifecycle, informational and organizational models of such processes. Also, it proposes a simple graphical formalism allowing to model each process's execution scenario as an annotated tree (modularity). In the new language, a particular emphasis is put on modelling (using "views") the perceptions that actors have on processes and their data.