To build an efficient metacomputer, one must assign jobs to the various networked machines intelligently. With a good strategy for job assignment, the metacomputer completes all of its work swiftly. With a poor job assignment strategy, the metacomputer does little better than its individual machines. Resource heterogeneity makes job assignment more complex. Placing a job on one machine might risk depleting its small memory.
Bin packing on memory protects the system against thrashing.
Load balancing protects the system against high CPU loads. Combining the two approaches, however, gives a heuristic algorithm with no clear theoretical merit. The Cost-Benefit Framework, developed in this work, offers a new approach to job assignment on metacomputers. It smoothly handles heterogeneous resources by converting them into a unitless cost. This work extends that research.