Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Efficient information collection in stochastic optimisation

This thesis focuses on a class of information collection problems in stochastic optimisation. Algorithms in this area often need to measure the performances of several potential solutions, and use the collected information in their search for high-performance solutions, but only have a limited budget for measuring. A simple approach that allocates simulation time equally over all potential solutions may waste time in collecting additional data for the alternatives that can be quickly identified as non-promising. Instead, algorithms should amend their measurement strategy to iteratively examine the statistical evidences collected thus far and focus computational efforts on the most promising alternatives. This thesis develops new efficient methods of collecting information to be used in stochastic optimisation problems. First, we investigate an efficient measurement strategy used for the solution selection procedure of two-stage linear stochastic programs. In the solution selection procedure, finite computational resources must be allocated among numerous potential solutions to estimate their performances and identify the best solution. We propose a two-stage sampling approach that exploits a Wasserstein-based screening rule and an optimal computing budget allocation technique to improve the efficiency of obtaining a high-quality solution. Numerical results show our method provides good trade-offs between computational effort and solution performance. Then, we address the information collection problems that are encountered in the search for robust solutions. Specifically, we use an evolutionary strategy to solve a class of simulation optimisation problems with computationally expensive blackbox functions. We implement an archive sample approximation method to ix reduce the required number of evaluations. The main challenge in the application of this method is determining the locations of additional samples drawn in each generation to enrich the information in the archive and minimise the approximation error. We propose novel sampling strategies by using the Wasserstein metric to estimate the possible benefit of a potential sample location on the approximation error. An empirical comparison with several previously proposed archive-based sample approximation methods demonstrates the superiority of our approaches. In the final part of this thesis, we propose an adaptive sampling strategy for the rollout algorithm to solve the clinical trial scheduling and resource allocation problem under uncertainty. The proposed sampling strategy method exploits the variance reduction technique of common random numbers and the empirical Bernstein inequality in a statistical racing procedure, which can balance the exploration and exploitation of the rollout algorithm. Moreover, we present an augmented approach that utilises a heuristic-based grouping rule to enhance the simulation efficiency by breaking down the overall action selection problem into a selection problem involving small groups. The numerical results show that the proposed method provides competitive results within a reasonable amount of computational time