Journal of Telecommunications and Information Technology, 2018, nr 1

We consider a two-link system that accommodates Poisson arriving calls from different service-classes and propose a multirate teletraffic loss model for its analysis. Each link has two thresholds, which refer to the number of in-service calls in the link. The lowest threshold, named support threshold, defines up to which point the link can support calls offloaded from the other link. The highest threshold, named offloading threshold, defines the point where the link starts offloading calls to the other link. The adopted bandwidth sharing policy is the complete sharing policy, in which a call can be accepted in a link if there exist enough available bandwidth units. The model does not have a product form solution for the steady state probabilities. However, we propose approximate formulas, based on a convolution algorithm, for the calculation of call blocking probabilities. The accuracy of the formulas is verified through simulation and found to be quite satisfactory

An energy-friendly scheduler for edge computing systems

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster