Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
Today’s cloud computing techniques are becoming unsustainable for real time applications as very low latency is required with billions of connected devices. New paradigms are arising; the one that offers an integrated solution for extending cloud resources to the edge of the network and addresses current cloud issues is Fog Computing. Performing Fog Computing brings a set of challenges such as: provisioning edge nodes to perform task volumes downloaded from the Cloud; placing task volumes on edge nodes; resource management on edge nodes; need for a new programming model; programming, resource management, data consistency service discovery challenges; privacy and security and improving the quality of service (QoS) and user experience (QoE). This paper aims at introducing the Fog Computing concept and it presents a literature review on the way it is applied: context-sensitive applications and context-sensitive mobile service platforms. The result of the study is presented as the current research challenges for context aware mobile applications in Fog Computing infrastructure. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that lead us to question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and, in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a review of the literature on the main task scheduling algorithms in cloud and fog architecture; we studied and discussed their limitations, and we also explored and suggested some perspectives for improvement.

Abstract
We used BPMN diagrams to identify indicators that can assist teachers in their intervention actions to support students' self-regulation and co-regulation in an asynchronous e-learning context. The use of BPMN modeling, by making explicit the tasks and procedures implicit in the intervention of the e-learning teacher, also exposed which data were available for developing decision-support indicators, as well as the relevant moments for carrying out interventions. Such indicators can help e-learning teachers focus their interventions to support self-regulation and co-regulation of learning, as well as enabling the creation of live data dashboards to support decision-making for those interventions, thus this process can contribute to devise better instruments for teacher intervention in support of self-regulation and co-regulation of student learning. © 2021, Springer Nature Switzerland AG.