Summary: An Opportunity Cost Approach for Job Assignment and Reassignment in a Scalable
Yair Amir, Baruch Awerbuch, and R. Sean Borgstrom are affiliated with
the Department of Computer Science, the Johns Hopkins University, Baltimore MD 21218.
Amnon Barak and Arie Keren are affiliated with
the Institute of Computer Science, the Hebrew University of Jerusalem, 91904 Israel
AN OPPORTUNITY COST APPROACH FOR JOB ASSIGNMENT IN ASCALABLE COMPUTING
Yair Amir, Baruch Awerbuch, Amnon Barak, R. Sean Borgstrom, and Arie Keren
1 This research is supported, in part, by the Defense Advanced Research Projects Agency (DARPA)
under grant F306029610293 to Johns Hopkins University.
A new method is presented for job assignment to and reassignment between machines in a computing
cluster. Our method is based on a theoretical framework that has been experimentally tested and shown
to be useful in practice. This ``opportunity cost'' method converts the usage of several heterogeneous
resources in a machine to a single homogeneous ``cost''. Assignment and reassignment are then
performed based on that cost. This is in contrast to traditional, ad hoc methods for job assignment and
reassignment. These treated each resource as an independent entity with its own constraints, as there was