Let LL = loglikelihood
Here is a quick summary of what you see from the summary(glm.fit) output,
Null Deviance = 2(LL(Saturated Model) - LL(Null Model)) on df = df_Sat - df_Null
Residual Deviance = 2(LL(Saturated Model) - LL(Proposed Model)) df = df_Sat - df_Proposed
The Saturated Model is a model that assumes each data point has its own parameters (which means you have n parameters to estimate.)
The Null Model assumes the exact "opposite", in that is assumes one parameter for all of the data points, which means you only estimate 1 parameter.
The Proposed Model assumes you can explain your data points with p parameters + an intercept term, so you have p+1 parameters.
If your Null Deviance is really small, it means that the Null Model explains the data pretty well. Likewise with your Residual Deviance.
What does really small mean? If your model is "good" then your Deviance is approx Chi^2 with (df_sat - df_model) degrees of freedom.
If you want to compare you Null model with your Proposed model, then you can look at
(Null Deviance - Residual Deviance) approx Chi^2 with df Proposed - df Null = (n-(p+1))-(n-1)=p
Are the results you gave directly from R? They seem a little bit odd, because generally you should see that the degrees of freedom reported on the Null are always higher than the degrees of freedom reported on the Residual. That is because again, Null Deviance df = Saturated df - Null df = n-1
Residual Deviance df = Saturated df - Proposed df = n-(p+1)