This project proposes to use Markovian Arrival Process (MAPs and MMAPs) to classify workload traces and to use MMAPs to generate synthetic workload traces. In order to do this, we propose to enhance current MAP fitting techniques in at least two ways, namely
- Achieve an automated MAP fitting technique that obtains a MMAP by observing workload traces such that we can distinguish different facet of the request arrival process
- Develop a variant of MAPs that take explicitly takes phases into account since interarrival times of file IO requests often contain phases of user request where in the statistical properties of the interarrival times differ significantly between adjacent phases (compile phase versus install phase).
As a result of the MAP fitting techniques, we obtain many important statistical properties of workload traces (moments, joint moments, autocorrelation) that provide a base for characterizing and comparing workload traces. The resulting MAPs or MMAPs can be simulated to generate synthetic workload traces. With these steps, we seek to obtain modeling, techniques and tools that help to generate more accurate workload models than what is possible with current state-of-the-art techniques.