This article describes an approach for predicting the run-time of jobs using a technique that works in three phases. Each one is independently adjusting to a user's behaviour in order to lead to accurate forecasts. In heterogeneous and distributed environments it is necessary to create schedules for utilizing the resources in an efficient way, but the generation of these schedules often poses a problem for a scheduler, as it has to incorporate several aspects like priorities, system load, Service Level Agreements. One possibility to support a scheduler in doing its work is to provide accurate predictions of the run-times of the submitted jobs.A large number of current techniques for run-time prediction offer statistical models - in the majority of cases linear ones - that are deployed on previously filtered data. As users have different jobs due to their field of work, and the attributes of their jobs differ, because of the different requirements they have, filtering data and choosing an appropriate method for a forecast has to cover these aspects. Motivated by this we propose an adaptive prediction system, where in each one of the phases we adjust our methodology on basis of the former behaviour of a user. This leads to a user specific clustering of data and to a flexible utilization of different prediction techniques in order to create a user-centred prediction model.

In this article we describe an approach for the prediction of the run-time of jobs in heterogeneous environments that applies a meta-prediction algorithm working in multiple phases. For an efficient utilization of hardware resources, it is necessary to support schedulers with detailed information about the jobs that are going to be dispatched. One technique is to provide accurate forecasts of application run-times. A large number of current approaches focus on limited sets of prediction techniques (often linear ones) whereas most times only one is deployed on a dataset, ignoring the different characteristics that are "denoted" inherently by the data and therefore are not obvious. For that reason we are proposing an adaptive system for run-time prediction that offers a large set of different and differently parameterized predictors respectively, whereas only the appropriate prediction techniques for specifically filtered clusters of jobs are executed.

This article describes a system for run-time prediction of applications in heterogeneous environments. To exploit the power of computational grids, scheduling systems need profound information about the job to be executed. The run-time of a job is - beside others - not only dependent of its kind and complexity but also of the adequacy and load of the remote host where it will be executed. Accounting and billing are additional aspects that have to be considered when creating a schedule. Currently predictions are achieved by using descriptive models of the applications or by applying statistical methods to former jobs mostly neglecting the behaviour of users. Motivated by this, we propose a method that is not only based on the characteristics of a job but also takes the behaviour of single users and groups of similar users respectively into account. The basic idea of our approach is to cluster users, hosts and jobs and apply multiple methods in order to detect similarities and create forecasts. This is achieved by tagging jobs with attributes and by deriving predictions for similar attributed jobs whereas the recent behaviour of a user determines which predictions are finally taken.