An intelligent, time-optimized monitoring scheme for edge nodes

Monitoring activities over edge resources and services are essential in today's applications. Edge nodes can monitor their status and end users/applications requirements to identify their ‘matching’ and deliver alerts when violations are present. Violations are related to any disturbance of the desired Quality of Service (QoS). QoS depends on a number of performance metrics and can differ among applications. In this paper, we propose the use of an intelligent mechanism to be incorporated in monitoring tools adopted by edge nodes. The proposed mechanism observes the realizations of performance parameters that result in specific QoS values and decides when it is the right time to ‘fire’ mitigation actions. Hence, edge nodes are capable of changing their configuration to secure the desired QoS levels as dictated by end users/applications requirements. In our work, a mitigation action could involve either upgrades in the current services/resources or offloading tasks by transferring computational load and data to peer nodes or the Cloud. We present our model and provide formulations for the solution of the problem. A high number of simulations reveal the performance of the proposed mechanism. Our experiments show that our scheme outperforms any deterministic model defined for the discussed setting as well as other efforts found in the relevant literature

Dynamic Scheduling and Optimal Reconfiguration of UPF Placement in 5G Networks

Multi-access Edge Computing (MEC) is a key technology in the road to 5G and beyond networks. Significant reductions in both latency and backhaul traffic can be achieved by placing server applications, and network functions at the network edge. However, this implies new challenges for their dynamic placement and management. In this paper, we tackle the problem of dynamic placement reconfiguration of 5G User Plane Functions (UPFs) in a MEC ecosystem to adapt to changes in user locations while ensuring QoS and network operator expenditures reduction. In this vein, an Integer Linear Programming (ILP) solution is proposed to determine the optimal UPF placement configuration (e.g., number of UPFs and user-UPF mapping) by considering several cost components along with service requirements. Moreover, a scheduling technique based on Optimal Stopping Theory (OST) is presented to decide the optimal reconfiguration time according to instantaneous values of latency violations and established QoS thresholds. Extensive simulation results demonstrate their effectiveness, achieving significant improvements in metrics such as number of re-computation events, reconfiguration costs, and number of latency violations over time

On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain

An intelligent, time-optimized monitoring scheme for edge nodes

Monitoring activities over edge resources and services are essential in today's applications. Edge nodes can monitor their status and end users/applications requirements to identify their ‘matching’ and deliver alerts when violations are present. Violations are related to any disturbance of the desired Quality of Service (QoS). QoS depends on a number of performance metrics and can differ among applications. In this paper, we propose the use of an intelligent mechanism to be incorporated in monitoring tools adopted by edge nodes. The proposed mechanism observes the realizations of performance parameters that result in specific QoS values and decides when it is the right time to ‘fire’ mitigation actions. Hence, edge nodes are capable of changing their configuration to secure the desired QoS levels as dictated by end users/applications requirements. In our work, a mitigation action could involve either upgrades in the current services/resources or offloading tasks by transferring computational load and data to peer nodes or the Cloud. We present our model and provide formulations for the solution of the problem. A high number of simulations reveal the performance of the proposed mechanism. Our experiments show that our scheme outperforms any deterministic model defined for the discussed setting as well as other efforts found in the relevant literature. © 2019 Elsevier Lt