Online scheduling with migration cost

In this paper, we consider a new variation of the classical online scheduling problem. In our model, an online scheduler is allowed to migrate the assigned jobs to different machines. Live migration is a powerful tool for load balancing. However, migration will incur additional cost in the destination machines. In this paper, we study the scheduling problem with migration cost model. Suppose that a job with processing time p which is already scheduled on the machine A is removed and transferred to the machine B, the load of the machine A will decrease p, but the load of the machine B will increase (1 + r) p, where 0 <= r <= 1 is a constant and it is called the migration factor. First, we propose an approximation algorithm for arbitrary machines. Then we give an improved algorithm for the case of two machines. Both algorithms are better than list scheduling algorithm [2] if the migration factor is smaller than a certain value. Finally, we implement our algorithms both in real data and random data. The experimental results indicate that the performances of algorithms are very close to the optimal algorithm.