Let $\varepsilon > 0$. Let $k_d(\varepsilon)$ be the minimum number of balls $B(x, \varepsilon) \subset \mathbb{R}^d$, $x \in \mathbb{S}^{d-1}$, w.r.t. the usual metric in $\mathbb{R}^d$, needed in order for the balls to cover $\mathbb{S}^{d-1}$.
Is there a neat way to calculate $k_d(\varepsilon)$? I'm interested in the rate at which it increases as $d$ grows. For example, the ratios $k_{d+1}(\varepsilon) / k_d(\varepsilon)$, would suffice.
So far, I have tried to look at regular polygons with vertexes on $\mathbb{S}^{d-1}$ and areas of spherical caps around $x$ in comparison to the area of $\mathbb{S}^{d-1}$, but things tend to get quite messy: For example with the spherical caps you end up looking at regularized incomplete Beta functions.
If somebody has a slick way to approach this, I would appreciate it if they shared. Of course literary references to something related to this would be great as well.
Thanks!
EDIT: It turns out that people have been seriously working on optimal spherical coverings. However, they study something called "covering density" which I'm not instantly sure how to turn into a number of spheres.