We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

Abstract-We present a framework for making computation offloading decisions in computational grid settings in which schedulers determine when to move parts of a computation to more capable resources to improve performance. Such schedulers must predict when an offloaded computation will outperform one that is local by forecasting the local cost (execution time for computing locally) and remote cost (execution time for computing remotely and transmission time for the input/output of the computation to/from the remote system). Typically, this decision amounts to predicting the bandwidth between the local and remote systems to estimate these costs. Our framework unifies such decision models by formulating the problem as a statistical decision problem that can either be treated "classically" or using a Bayesian approach. Using an implementation of this framework, we evaluate the efficacy of a number of different decision strategies (several of which have been employed by previous systems). Our results indicate that a Bayesian approach employing automatic change-point detection when estimating the prior distribution is the best-performing approach.

Virtualization has become increasingly popular for enabling full system isolation, load balancing, and hardware multiplexing. This wide-spread use is the result of novel techniques such as paravirtualization that make virtualization systems practical and efficient. Paravirtualizing systems export an interface that is slightly different from the underlying hardware but that significantly streamlines and simplifies the virtualization process.In this work, we investigate the efficacy of using paravirtualizing software for performance-critical HPC kernels and applications. Such systems are not currently employed in HPC environments due to their perceived overhead. However, virtualization systems offer tremendous potential for benefitting HPC systems by facilitating application isolation, portability, operating system customization, and program migration.We present a comprehensive performance evaluation of Xen, a low-overhead, Linux-based, virtual machine monitor (VMM), for paravirtualization of HPC cluster systems at Lawrence Livermore National Lab (LLNL). We consider four categories of micro-benchmarks from the HPC Challenge (HPCC) and LLNL ASCI Purple suites to evaluate a wide range of subsystem-specific behaviors. In addition, we employ macro-benchmarks and HPC application to evaluate overall performance in a real setting. We also employ statistically sound methods to compare the performance of a paravirtualized kernel against three popular Linux operating systems: RedHat Enterprise 4 (RHEL4) for build versions 2.6.9 and 2.6.12 and the LLNL CHAOS kernel, a specialized version of RHEL4. Our results indicate that Xen is * This work is sponsored in part by grant from the National Science Foundation (ST-HEC-0444412). very efficient and practical for HPC systems.

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Abstract. We present the design and implementation of AppScale, an open source extension to the Google AppEngine (GAE) Platform-asa-Service (PaaS) cloud technology. Our extensions build upon the GAE SDK to facilitate distributed execution of GAE applications over virtualized cluster resources, including Infrastructure-as-a-Service (IaaS) cloud systems such as Amazon's AWS/EC2 and Eucalyptus. AppScale provides a framework with which researchers can investigate the interaction between PaaS and IaaS systems as well as the inner workings of, and new technologies for, PaaS cloud technologies using real GAE applications.