Intelligent scaling for 6G IoE services for resource provisioning

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

Gastrostomy Tube Outcomes Among Surgical and Non-Surgical Services: A Retrospective Review

Background Our objective was to evaluate differences in baseline characteristics, complications, and mortality among patients receiving a gastrostomy tube (GT) by surgical or non-surgical services. Methods We performed a retrospective analysis of adult patients who underwent GT placement from 2014 to 2017 at a single institution. Using bivariate and multivariable analyses, we compared baseline characteristics, complications, and overall 30-day mortality of patients undergoing GT placement with surgical or non-surgical services. Results Of the 1339 adults who underwent GT placement, surgical and non-surgical services performed 45% (n = 609) and 55% (n = 730) procedures, respectively. Gastrostomy tube-related complications were similar (29.6% surgical vs 28.8% non-surgical, P = .76). Thirty-day mortality was higher among non-surgical services (23.7% vs 16.5%, P = .004). On multivariable analysis, this was not significant (OR 1.21, 95% CI 0.83; 1.77). Conclusion Surgical and non-surgical service placement of GTs had equivalent GT-related mortality and complication rates.

FogFrame: a framework for IoT application execution in the fog

Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.