Adaps – A three-phase adaptive prediction system for the run-time of jobs based on user behaviour

AbstractIn heterogeneous and distributed environments it is necessary to create schedules for utilising resources in an efficient way. This generation often poses a problem for a scheduler, since several aspects have to be considered. One way of supporting a scheduler is to provide accurate predictions of the run-times of the submitted jobs. A large number of current techniques offer statistical models that are deployed on previously filtered data. As users have different jobs, and because the attributes of their jobs differ, filtering data and choosing an appropriate prediction method has to cover these aspects. This article describes Adaps, a system for run-time prediction that works in three phases. Each is independently adjusting to the jobs of a user, based on historical information. This leads to a user specific clustering of data and to a flexible utilisation of different prediction techniques in order to create a user-centred prediction model

ADEPT Runtime/Scalability Predictor in support of Adaptive Scheduling

A job scheduler determines the order and duration of the allocation of resources, e.g. CPU, to the tasks waiting to run on a computer. Round-Robin and First-Come-First-Serve are examples of algorithms for making such resource allocation decisions. Parallel job schedulers make resource allocation decisions for applications that need multiple CPU cores, on computers consisting of many CPU cores connected by different interconnects. An adaptive parallel scheduler is a parallel scheduler that is capable of adjusting its resource allocation decisions based on the current resource usage and demand. Adaptive parallel schedulers that decide the numbers of CPU cores to allocate to a parallel job provide more flexibility and potentially improve performance significantly for both local and grid job scheduling compared to non-adaptive schedulers. A major reason why adaptive schedulers are not yet used practically is due to lack of knowledge of the scalability curves of the applications, and high cost of existing white-box approaches for scalability prediction. We show that a runtime and scalability prediction tool can be developed with 3 requirements: accuracy comparable to white-box methods, applicability, and robustness. Applicability depends only on knowledge feasible to gain in a production environment. Robustness addresses anomalous behaviour and unreliable predictions. We present ADEPT, a speedup and runtime prediction tool that satisfies all criteria for both single problem size and across different problem sizes of a parallel application. ADEPT is also capable of handling anomalies and judging reliability of its predictions. We demonstrate these using experiments with MPI and OpenMP implementations of NAS benchmarks and seven real applications

Plant Adaptation to Global Climate Change

Plant Adaptation to Global Climate Change discusses the issues of the impact of climate change factors (abiotic and biotic) on vegetation. This book also deals with simulation modeling approaches to understanding the long-term effects of different environmental factors on vegetation. This book is a valuable resource for the environmental science research community, including those interested in assessing climate change impacts on vegetation and researchers working on simulation modeling