Investigation of Effect of Different Run Time Distributions on Smartnet Performance

This thesis investigates using in line simulation the e ect of non deterministic runtime distributions on the performance of SmartNet s schedule execution using the Opportunistic Load Balancing OLB Algorithm the Limited Best Assignment LBA Algorithm an O mn Greedy Algorithm and an O mn Greedy Algorithm Smart Net is a framework for scheduling jobs and machines in a heterogeneous computing environment Its major strength is its use of both current machine loads and pre dicted job machine performance when generating schedules Schedules are built to meet various Quality of Service requirements using the above algorithms among others We enhanced SmartNet s simulator so that the runtime distributions could be used for experimentation The distributions were generated using derivations from our study on NAS Benchmarks Experiments were run for various categories of job machine heterogeneity to compare the algorithms which account for both load and expected performance the Greedy algorithms against OLB and LBA For all categories of heterogeneity the greedy algorithms outperformed the other two algorithms for both truncated Gaussian and exponential distributions For these same distributions the O mn Greedy algorithm performed as well as the O mn Greedy algorithm when the heterogeneity of jobs and machines was high