In this chapter, we consider a joint sampling and scheduling problem for optimizing data freshness in multisource systems. Data freshness is measured by a nondecreasing penalty function of Age of Information, where all sources have the same age-penalty function. Sources take turns to generate update samples, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new sample in its turn. We aim to find the optimal scheduler–sampler pairs that minimize the total-average age-penalty (Ta-AP). We start the chapter by providing a brief explanation of the sampling problem in the light of single–source networks, as well as some useful insights and applications on age of information and its penalty functions. Then, we move on to the multisource networks, where the problem becomes more challenging. We provide a detailed explanation of the model and the solution in this case. Finally, we conclude this chapter by providing an open question in this area and its inherent challenges.