The job replication problem has been studied recently as a mechanism to improve performance and availability of systems with n parallel servers, each with its own queue. A dispatcher using some policy sends d (1 ≤ d ≤ n) copies of a job to d of the servers. Copies are eliminated from the system as soon as the first copy completes from any of the d servers. This article introduces a datadriven method to derive closed-form expressions for the average response time and other metrics of jobs as a function of the degree of replication d. This method consists of developing a simulator for the system in order to generate a very large number of datasets for a wide range of input parameters. A statistical and visualization analysis of the data provides the analytical models. It is important to emphasize the difference between using simulation methods to obtain the value of metrics (e.g., average response time) of a computer system given values of input parameters and using our data-driven method to obtain closed-form expressions that relate output metrics to input parameters. The latter is the focus of our approach. The analysis presented here covers results for homogeneous and heterogeneous servers with exponentially distributed service times and for homogeneous servers with hypo-exponentially and hyper-exponentially distributed service times. This article also presents a closed-form equation for the optimal replication degree for the case of homogeneous servers with hypo-exponentially distributed service times.

Checkpointing and rollback is a key mechanism used to improve the reliability of software systems. The benefits of this mechanism can be offset by the overhead of checkpointing when the failure rate is low. The problem of developing analytic models of rollback and checkpointing has been continuously addressed for over four decades using different assumptions. This paper examines the problem under a more realistic angle, i.e., one in which there are several software components sharing resources (e.g., processors and I/O devices) among themselves and with the checkpointing processes. Additionally, the paper allows for different components to have different computing, rollback, and checkpointing demands, as well as different failure distributions. Our models also allow for various checkpointing processes to be executing concurrently to checkpoint the state of different software components. The analytic models developed here combine Markov Chains and Queuing Networks and allow us to compute the following metrics: (1) average time needed by a component to complete its execution, (2) average throughput of a component, (3) availability of a component, and (4) checkpointing overhead. The models were validated through extensive simulation and experimentation.