We define a collection of new problems referred to as ``machine activation''

problems. The central framework we introduce considers a collection of M

machines (unrelated or related), with machine $i$ having an activation

cost of $a_i$.

There is also a collection of N jobs that need to be performed, and

$p_{ij}$ is the processing time of job $j$ on machine $i$.

Standard scheduling models assume that the set of machines is fixed

and all machines are available. We assume that there is an activation cost

budget of $A$

-- we would like to select a subset S of the machines to activate

with total cost $a(S)le A$ and find a schedule for the jobs on the

machines in $S$ minimizing the makespan. In this work we develop

bi-criteria approximation algorithms for this problem based on both

LP rounding and a greedy approach.