Slicing is an emergent technology for 5G. It decomposes a single Radio Access Network (RAN) into multiple virtual networks "slices" to meet demands in term of throughput, mobility, latency, reliability, etc. Slicing needs real-time reconfigurations to keep current with demands' dynamics. This results in an increased cost of Operation Expenditures (OPEX). We approached this challenge as an optimization problem of infrastructure's resources. We virtualized and pooled Baseband Units (BBUs) resources on cloud. Dynamic allocation and interconnection with Remote Radio Heads (RRHs) are made possible by leveraging the advents of Network Function Virtualization (NFV) and Software-defined Networking (SDN). We implemented Distributed Base Station (DBS) using open software platform along to a public service orchestration tool for clouds. Our contribution is integrating service selection and deployment with real-time monitoring that allowed auto-control of resources by looping resources' lifecycle. In our experiments, we deployed several slices and we tested two scenarios. First scenario addressed slices' auto-scaling (Infrastructure Scale-Out/Scale-In) when free resources are available in the pool. Second scenario simulated slices' breathing (orchestration of resources) when the pool of resources is exhausted. In first scenario, results show that leveraging cloud elasticity by auto-scaling resources saves costs by providing exactly "what-is-needed" "when-it-isneeded" in term of cloud computing. In second scenario, results show that slices' breathing maximizes the usability by employing our "inhale-and-exhale" heuristic. It is about reusing resources from under-loaded slices in favor of overloaded ones with seamless effect on the user-experience.
Ever since the COVID-19 pandemic started, all the governments have been trying to limit its effects on their citizens and countries. This pandemic was harsh on different levels for almost all populations worldwide and this is what drove researchers and scientists to get involved and work on several kinds of simulations to get a better insight into this virus and be able to stop it the earliest possible. In this study, we simulate the spread of COVID-19 in Lebanon using an Agent-Based Model where people are modeled as agents that have specific characteristics and behaviors determined from statistical distributions using Monte Carlo Algorithm. These agents can go into the world, interact with each other, and thus, infect each other. This is how the virus spreads. During the simulation, we can introduce different Non-Pharmaceutical Interventions -or more commonly NPIsthat aim to limit the spread of the virus (wearing a mask, closing locations, etc). Our Simulator was first validated on concepts (e.g. Flattening the Curve and Second Wave scenario), and then it was applied on the case of Lebanon. We studied the effect of opening schools and universities on the pandemic situation in the country since the Lebanese Ministry of Education is planning to do so progressively, starting from 21 April 2021. Based on the results we obtained, we conclude that it would be better to delay the school openings while the vaccination campaign is still slow in the country.
Vaccination has been the long-awaited solution ever since the COVID-19 pandemic started. But the problem is that vaccine shots cannot be delivered at the same time to all populations, because of their limited quantity from one side, and their high demand from the other side. Therefore, countries need a way to test the effect of different distribution strategies before applying them. But how can they do this? To assist countries with this task, we built an agent-based model that runs on top of the Monte Carlo algorithm. This model simulates the spread of COVID-19 in a country where we can apply different NPIs at different times, and we can supply different kinds of vaccines using different strategies. In this study, we tested the outcomes of four vaccination strategies: older first, younger first, a mixed strategy, and a random strategy. We simulated these strategies in two different countries: France and Colombia. Then, we performed a comparative analysis to find which strategy might be the best for each country. Our results show that what is good for a country is not necessarily the best for the other one. Therefore, we proved that a vaccination strategy should be adapted to the structure of the population we are vaccinating. The system we built helps countries in this direction by allowing them to test the outcomes of their strategies before applying them in real life to select the one that minimizes human losses (deaths and infections).
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