Kavitha Chandrasekar

Middleware alternatives for storm surge predictions in Windows Azure

Cloud computing is a resource of significant value to computational science, but has proven itself to be not immediately realizable by the researcher. The cloud providers that offer a Platform-as-a-Service (PaaS) platform should, in theory, offer a sound alternative to infrastructure-as-a-service as it could be easier to take advantage of for computational science kinds of problems. The objective of our study is to assess how well the Azure platform as a service can serve a particular class of computational science application. We conduct a performance evaluation using three approaches to executing a high-throughput storm surge application: using Sigiri, a large scale resource abstraction tool, Windows Azure HPC scheduler, and Daytona, an Iterative Map-reduce runtime for Azure. The differences in the approaches including early performance measures for up to 500 instances are discussed.

Effectiveness of Hybrid Workflow Systems for Computational Science

Abstract The workflow and its supporting systems are integral to computational science. Tailored to loosely coupled, and largely coarse-grained tasks, the workflow replaces the script as a way to automate the multiple steps of a large scale model. Workflow reuse has been at the subworkflow level but this restricts, over the long run, a workflow to running on the system on which it was developed. A scientist wanting to use two workflows developed by two different people and for different workflow systems will need to have access to both workflow systems. The contribution this paper makes is a qualitative and quantitative study of the tradeoffs of a hybrid workflow solution that utilizes multiple workflow systems and solutions to execute a single workflow. Our results indicate that the major tradeoffs are not in performance as much as they are in complexity.

The SEAD DataNet prototype: data preservation services for sustainability science

In this poster we will present the SEAD project [1] and its prototype software and describe how SEAD approaches long-term data preservation and access through multiple partnerships and how it supports sustainability science researchers in their data management, analysis and archival needs. SEADs initial prototype system currently is being tested by ingesting datasets from the National Center for Earth Surface Dynamics (1.6 terabyte of data containing over 450,000 files) [2] and packaging them for transmission to long-term archival storage.

Executing Storm Surge Ensembles on PAAS Cloud

Cloud computing services are becoming increasingly viable for scientific model execution. As a leased computational resource, cloud computing enables a computational modeler at a smaller university to carry out sporadic large-scale experiments, and allows others to pay for CPU cycles as needed, without incurring high maintenance costs of a large compute system. In this chapter, we discuss the issues involved in running high throughput ensemble applications on a Platform-as-a-Service cloud. We compare two frameworks deploying and running these applications, namely Sigiri and MapReduce. We motivate the need for a pipelined architecture to application deployment, and discus a couple of methodologies to balance the loads, minimize storage overhead, and reduce overall execution time.