A heuristic rule for routing customers to parallel servers

A practically important problem is the assignment of stochastically arriving service requests to one of several parallel service groups so as to minimize the long-run average sojourn time per service request. An exact solution of this multi-dimensional optimization problem is computationally infeasible. A simple heuristic solution method yielding a good suboptimal rule will be given for the case of server groups with different and generally distributed service times. This solution method is based on a decomposition approach and first principles from Markov decision theory. The main idea of the heuristic method is to apply one step of policy improvement to the best Bernoulli-splitting rule

Mean response times for optimistic concurrency control in a multi-processor database with exponential execution times

Using a non-productform queueing network model, two approximations are developed for computing the average response time of transactions in a multi-processor shared-memory database system with optimistic concurrency control. The time and resources needed for the validation of transactions are explicitly taken into account in the queueing model, since they are not always negligible. The performance of the approximations, tested against a simulation of the queueing model, is very good

The response time distribution in a multi-processor database with single queue static locking

A transaction scheduling mechanism is designed for a shared memory, multiprocessor database system. The scheduler used is a variant of static locking, adapted for real time and more than one processor. It is assumed that transactions arrive according to a Poisson process, execution times of transactions are independent and exponentially distributed and all transactions use the same number of data items. The system is then represented as a Markov model. A steady state is derived from this model. By examining the path through the system of a single transaction, a recursive relation that describes all moments of a transaction's response time is derived. The response time distribution is approximated by fitting a distribution to the first two moments. Simulation shows that this approximation gives excellent results