TDQN: Trace-driven analytic queuing network modeling of computer systems

Abstract

Many current performance studies of computer systems are evaluated by subjecting the system under evaluation to a workload obtained from publicly available traces. Many current performance studies of computer systems are evaluated by using publicly available traces as inputs. This approach provides credibility to performance studies but generally precludes the use of computationally efficient analytic models that rely on strict stochastic assumptions. Simulation or prototype implementations, less general and more time consuming methods, are used when the use of traces limits the applicability of analytic performance models. Analytic models (e.g., queuing theory for single queues or queuing networks) have extensively been used to estimate job execution times under steady state conditions. This paper discusses the use of closed Queuing Network (QN) models during finite time intervals to estimate the execution time of jobs submitted to a computer system. The paper presents the Trace-Driven Queuing Network (TDQN) algorithm that allows job traces to be used as input to analytic models. Validations against experimental results showed that the absolute relative error between measurements and execution time predictions obtained with the TDQN algorithm is below 10% in most cases. Additionally, the paper shows how the TDQN algorithm can be used to estimate the makespan of a stream of jobs submitted to a scheduler, which decides which computer of a cluster will process the job.