For modern computing platforms, heterogeneous processing nodes are the reality. Moreover, most traditional job assignment algorithms, which are designed for homogeneous systems, are not efficient for heterogeneous platforms. For the sake of gaining the least response time, this paper investigates how to best dispatch the arriving jobs in heterogeneous systems, where the available servers have different processing speeds.

The introduction presents well-known job dispatching policies like join-the-shortest-queue (JSQ), its variant JSQ-d (the server is chosen among d randomly selected servers), and join-idle-queue. Emphasizing their inefficiency in heterogeneous platforms, “power-of-d” policies are introduced. The paper presents techniques to smartly manage the utilization of faster processing nodes in heterogeneous systems by selecting which servers to be polled and how to assign the arrived jobs to them. In this manner, knowing the speed of polled servers is the key factor of performance. System modeling is included; k servers are divided into fast and slow servers with exponentially distributed service times.

Two scenarios are examined and analyzed. In the first scenario, to evaluate response time, a heterogeneous version of JSQ is detailed. The servers are distinguished based on fast or slow, and the job assignments on idle fast, idle slow, and no idle fast orders. In order to determine the system’s response time, the authors use a deterministic limiting system and a differential equation system to model behavior. The second scenario considers a set of servers without any prior knowledge of speed. After a period of random job assignments, servers with more accomplished jobs are assigned higher priority for polling and job assignments.

Based on innovations from system results, JSQ-d with accomplishment sampling (JSQ-DAS) is a nice idea. While the paper is good, it could be improved by including an analytical discussion of JSQ-DAS.