How to calculate goodness of fit in glm (R)

I have the following result from running glm function.

How can I interpret the following values:

• Null deviance
• Residual deviance
• AIC

Do they have something to do with the goodness of fit? Can I calculate some goodness of fit measure from these result such as R-square or any other measure?

Call:
glm(formula = tmpData$Y ~ tmpData$X1 + tmpData$X2 + tmpData$X3 +
as.numeric(tmpData$X4) + tmpData$X5 + tmpData$X6 + tmpData$X7)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-0.52628  -0.24781  -0.02916   0.25581   0.48509

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept         -1.305e-01  1.391e-01   -0.938   0.3482
tmpData$X1 -9.999e-01 1.059e-03 -944.580 <2e-16 *** tmpData$X2         -1.001e+00  1.104e-03 -906.787   <2e-16 ***
tmpData$X3 -5.500e-03 3.220e-03 -1.708 0.0877 . tmpData$X4         -1.825e-05  2.716e-05   -0.672   0.5017
tmpData$X5 1.000e+00 5.904e-03 169.423 <2e-16 *** tmpData$X6          1.002e+00  1.452e-03  690.211   <2e-16 ***