Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet

TLDR

In modern multi‑computer networks, users often run tasks on any available machine, but current schedulers prioritize minimizing computer idle time rather than user wait time. This study investigates the advantages of a scheduler that simultaneously accounts for computer availability and task performance on each machine. The authors present SmartNet, a resource‑scheduling system, and evaluate it against load balancing and user‑directed assignment through large‑scale batch simulations of hundreds of networks and thousands of task mixes. Results show that SmartNet consistently reduces the maximum user wait time compared to both load balancing and user‑directed assignment.

Abstract

It is increasingly common for computer users to have access to several computers on a network, and hence to be able to execute many of their tasks on any of several computers. The choice of which computers execute which tasks is commonly determined by users based on a knowledge of computer speeds for each task and the current load on each computer. A number of task scheduling systems have been developed that balance the load of the computers on the network, but such systems tend to minimize the idle time of the computers rather than minimize the idle time of the users. The paper focuses on the benefits that can be achieved when the scheduling system considers both the computer availabilities and the performance of each task on each computer. The SmartNet resource scheduling system is described and compared to two different resource allocation strategies: load balancing and user directed assignment. Results are presented where the operation of hundreds of different networks of computers running thousands of different mixes of tasks are simulated in a batch environment. These results indicate that, for the computer environments simulated, SmartNet outperforms both load balancing and user directed assignments, based on the maximum time users must wait for their tasks to finish.