Resource Management for Rapid Application Turnaround on Enterprise Desktop Grids
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Idletime scheduling with preemption intervals
Proceedings of the twentieth ACM symposium on Operating systems principles
Predicting Free Computing Capacities on Individual Machines
GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
An analysis of idle CPU cycles at university computer labs
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
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We present a simple technique for predicting the probability that an idle workstation will continue to be idle for $i$ minutes, given that it has been idle for $x$ minutes (i.e., find the {\em remaining idle period probability} $P(i;x)$). By idle we mean that the workstation owner is not interactively using the workstation or executing other tasks on it. The results are particularly applicable to the scheduling of tasks in systems that harvest cycles from idle-only workstations. Our Remaining Idle Period Probability Predictor (RIPPP) uses the distribution of the lengths of idle periods on the managed workstations. Collecting, storing, and processing these distributions (in the form of histograms) is a small overhead on modern workstations (a few kilobytes of storage per workstation). We investigated the behavior of our RIPPP with usage traces of 31 workstations collected over a five month period, and discovered the following six results. (1) The distribution of one month of idle periods predicts the remaining idle period probability in the next month for most workstations. (2) Different workstations tend to have significantly different idle period length distributions. (3) The average length of an idle period does not necessarily correlate well with the probability of being able to find long idle periods, contrary to intuition and previous scheduling heuristics. (4) A workstation that has been idle a long time does not necessarily have a high probability of remaining idle for a long time. (5) Using the time of day can improve predictions. (6) The length of the previous and the current idle periods are positively correlated, but the length of the previous idle period is not strongly correlated with finding long remaining idle periods. Based on these studies, we conclude that an effective way to find idle workstations is to collect their idle period length distribution and use it to compute $P(i;x)$. We believe our analysis will be applicable to predicting the length of busy periods, which is useful for deciding whether to migrate or suspend tasks when a workstation becomes busy (the owner reclaims it). From our results, we have developed a remaining idle period probability toolkit which includes a statistics collector and a prediction library in C. This will be available from our project homepage.