Dan Stanzione

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanitys projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

Thermal-Aware Task Scheduling to Minimize Energy Usage of Blade Server Based Datacenters

Blade severs are being increasingly deployed in modern datacenters due to their high performance/cost ratio and compact size. In this study, we document our work on blade server based datacenter thermal management. Our goal is to minimize the total energy costs (usage) of datacenter operation while providing a reasonable thermal environment for their reliable operation. Due to special characteristics of blade servers, we argue that previously proposed power-oriented schemes are ineffective for blade server-based datacenters and that task-oriented scheduling is a more practicable approach since the contribution to the total energy cost from cooling and computing systems varies according to the utilization rates. CFD simulations are used to evaluate scheduling results of three different task scheduling algorithms: uniform outlet profile (UOP), minimal computing energy (MCE), and uniform task (UT), under four different blade-server energy consumption models: discretenonoptimal (DNO), discreteoptimal (DO), analognonoptimal (ANO), and analogoptimal (AO). Simulation results show that the MCE algorithm, in most cases, results in a minimal total energy cost - a conclusion that differs from the findings of previous research. UOP performs better than UT at low datacenter utilization rates, whereas UT outperforms UOP at high utilization rates

Dynamic virtual clustering with xen and moab

As larger and larger commodity clusters for high performance computing proliferate at research institutions around the world, challenges in maintaining effective use of these systems also continue to increase. Among the many challenges are maintaining the appropriate software stack for a broad array of applications, and sharing workload across clusters. The Dynamic Virtual Clustering (DVC) system integrates the Xen virtual machine with the Moab scheduler to allow for creation of virtual clusters on a per-job basis. These virtual clusters can provide a unique software environment for a particular application, or can provide a consistent software environment across multiple heterogeneous clusters. In this paper, the overhead of Xen-based DVC vs. native cluster performance is examined for workloads consisting of both serial and MPI-based parallel jobs.

Dynamic Virtual Clustering

Multiple clusters co-existing in a single research campus has become commonplace at many university and government labs, but effectively leveraging those resources is difficult. Intelligently forwarding and spanning jobs across clusters can increase throughput, decrease turnaround time, and improve overall utilization. Dynamic virtual clustering (DVC) is a system of virtual machines, deployed in a single or multi-cluster environment, to increase cluster utilization by enabling job forwarding and spanning, flexibly allow software environment changes, and effectively sandbox users and processes from each other and the system. This paper presents both the initial implementation of DVC and performance results from synthetic workloads executed under DVC.

Jetstream: a self-provisioned, scalable science and engineering cloud environment

Jetstream will be the first production cloud resource supporting general science and engineering research within the XD ecosystem. In this report we describe the motivation for proposing Jetstream, the configuration of the Jetstream system as funded by the NSF, the team that is implementing Jetstream, and the communities we expect to use this new system. Our hope and plan is that Jetstream, which will become available for production use in 2016, will aid thousands of researchers who need modest amounts of computing power interactively. The implementation of Jetstream should increase the size and disciplinary diversity of the US research community that makes use of the resources of the XD ecosystem.

HPC Cluster Readiness of Xen and User Mode Linux

This paper examines the suitability of different virtualization techniques in a high performance cluster environment. A survey of visualization techniques is presented. Two representative technologies (Xen and User Mode Linux) are selected for an in depth analysis of cluster readiness in terms of their performance, reliability, and their overall impact on complexity of cluster administration

An international bioinformatics infrastructure to underpin the Arabidopsis community

The future bioinformatics needs of the Arabidopsis community as well as those of other scientific communities that depend on Arabidopsis resources were discussed at a pair of recent meetings held by the Multinational Arabidopsis Steering Committee and the North American Arabidopsis Steering Committee. There are extensive tools and resources for information storage, curation, and retrieval of Arabidopsis data that have been developed over recent years primarily through the activities of The Arabidopsis Information Resource, the Nottingham Arabidopsis Stock Centre, and the Arabidopsis Biological Resource Center, among others. However, the rapid expansion in many data types, the international basis of the Arabidopsis community, and changing priorities of the funding agencies all suggest the need for changes in the way informatics infrastructure is developed and maintained. We propose that there is a need for a single core resource that is integrated into a larger international consortium of investigators. We envision this to consist of a distributed system of data, tools, and resources, accessed via a single information portal and funded by a variety of sources, under shared international management of an International Arabidopsis Informatics Consortium (IAIC). This article outlines the proposal for the development, management, operations, and continued funding for the IAIC.

iPlant atmosphere: a gateway to cloud infrastructure for the plant sciences

The cloud platform complements traditional compute and storage infrastructures by introducing capabilities for efficiently provisioning resources in a self-service, on-demand manner. The new provisioning model promises to accelerate scientific discovery by improving access to customizable and task-specific computing resources. This paradigm is well-suited, especially for those applications tailored to leverage cloud-style of infrastructure capabilities. Adoption of the cloud model has been challenging for many domain scientists and scientific software developers due to the technical expertise required to effectively utilize this infrastructure. Some of the key limitations of cloud infrastructure are: limited integration with institutional authentication and authorization frameworks, lack of frameworks to enable domain-specific configurations for instances, and integration with scientific data repositories alongside existing computational clusters and grid deployments. Specifically designed to address some of these operational barriers towards adoptions by the plant sciences community, the iPlant Collaborative cloud platform, aptly named Atmosphere, is an open-source, robust, configurable gateway that extends established cloud infrastructure to meet the diverse computing needs for the plant science. Atmosphere manages the Virtual Machine (VM) lifecycle while maximizing the utilization of cloud resources for scientific workflows. Thus, Atmosphere allows researchers developing novel analytical tools to deploy them with ease while abstracting the underlying computing infrastructure, at the same time making it relatively easy for the users to access these tools via web browser. Atmosphere also provides a rich extensible Application Programming Interface (APIs) for integration and automation with other services. Since its launch, Atmosphere has seen a wide adoption by the plant sciences community for a broad array of applications that range from image processing to next generation sequence (NGS) analysis and can serve as a template for providing similar capabilities to other domains.

The iPlant Collaborative: Cyberinfrastructure to Feed the World

As plant biology becomes a data-driven science, new computing technologies are needed to address many formidable challenges. The iPlant Collaborative provides cyberinfrastructure for researchers and developers to collaborate in creating better tools, workflows, algorithms, and ontologies.

New Frontiers for Organismal Biology

Understanding how complex organisms function as integrated units that constantly interact with their environment is a long-standing challenge in biology. To address this challenge, organismal biology reveals general organizing principles of physiological systems and behavior—in particular, in complex multicellular animals. Organismal biology also focuses on the role of individual variability in the evolutionary maintenance of diversity. To broadly advance these frontiers, cross-compatibility of experimental designs, methodological approaches, and data interpretation pipelines represents a key prerequisite. It is now possible to rapidly and systematically analyze complete genomes to elucidate genetic variation associated with traits and conditions that define individuals, populations, and species. However, genetic variation alone does not explain the varied individual physiology and behavior of complex organisms. We propose that such emergent properties of complex organisms can best be explained through a renewed emphasis on the context and life-history dependence of individual phenotypes to complement genetic data.