Month: February 2024

113 – Early life environmental exposures and children’s growth

Early life is an important period for growth and development and therefore, sensitive to environmental exposures, such as chemicals and nutrition. Endocrine disrupting chemicals (EDCs), ubiquitous in daily exposure, can lead to adverse health effects. Katherine Svensson’s doctoral thesis in Public Health Science investigates 26 EDCs in pregnant women and measured children’s growth up to 7 years, finding higher EDC levels linked to lower birthweight, slower weight gain, and sex-specific impacts on body fat. In our conversation, Katherine explains the significance of her results. Adherence to nutritional guidelines together with better regulation of EDCs can help to promote healthy environments for children’s growth.

Katherine Svensson’s doctoral thesis can be downloaded from DiVA: Early life environmental exposures and children’s growth: A longitudinal study evaluating prenatal exposure for endocrine disrupting chemicals and nutrition in relation to children’s growth up to seven years of age

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112 – Unchaining microservice chains

In modern software development, microservices are crucial. Instead of a monolithic application, where everything is tightly coupled, microservice architecture offers a way to develop, deploy and maintain services independently. This enhances agility, resilience, and scalability, but it also introduces challenges concerning for example resource allocation and performance optimization.

In his doctoral thesis in Computer Science, Michel Gokan Khan addresses such challenges in large scale microservice chains, specifically in cloud native computing. In our conversation, he explains some of the key contributions of his research. Two of them being the PerfSim, a tool designed by Michel that is a performance simulator for cloud native system, and NFV-Inspector, another tool designed by him to be able to profile and analyse microservices specifically in network functions virtualization (NFV) environments. Michel’s research is also able to show how machine learning can be used for optimizing microservice chains in cloud environments. By contributions such as these and more, Michel’s research helps to reveal the true potential of artificial intelligence in profiling, modelling, simulating, and most importantly optimizing the performance and cost of running microservice chains.

Michel Gokan Khan’s doctoral thesis can be downloaded from DiVA: Unchaining Microservice Chains: Machine Learning Driven Optimization in Cloud Native Systems

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