Collaborative Research: SI2-SSI: Adding Volunteer Computing to the Research Cyberinfrastructure

消费设备-台式机和笔记本电脑、平板电脑、智能手机-的总计算能力远远超过机构计算资源。“志愿者计算”使用这些由其所有者自愿提供的消费设备来进行科学计算。除了向科学家提供额外的、急需的计算资源外，志愿者计算还宣传科学研究，并让公民参与科学。BOINC是志愿者计算的主要软件系统。它是由加州大学伯克利分校开发的，从2002年开始得到NSF的支持。到目前为止，中银国际一直以独立竞争项目的模式为基础。科学家们建立了自己的BOINC服务器，移植了他们的应用程序，以便在BOINC上运行，并宣传他们的项目以吸引志愿者。在许多科学领域，大约有40个这样的项目：例如爱因斯坦@HOME、欧洲核子研究中心和SETI@HOME(天体物理学)、Rosetta@HOME和GPUGrid.net(生物医学)、气候预测.net(气候研究)和IBM世界社区网格(多应用)。这些项目总共有大约40万名活跃的志愿者和1千万亿次的计算吞吐量。这种模式虽然在一定程度上取得了成功，但也达到了极限。项目和志愿者的数量停滞不前。志愿者计算正在提供大量的计算能力，但只提供给少数几个研究项目。对于其他科学家来说，有两个主要障碍。首先，创建BOINC项目有很大的开销：学习一项新技术，创建一个公共网站，产生宣传，等等。其次，志愿者计算是有风险和不确定的；不能保证一个新的项目会吸引志愿者。该项目旨在打破这一障碍，让所有从事高吞吐量计算的科学家都可以使用志愿者计算，方法是用新的“中央经纪人”模型取代竞争项目模型。新的模式有两个相关的部分：1)BOINC与现有的高吞吐量计算设施的整合，如超级计算中心和科学门户网站。当前在群集节点上运行的作业将透明地分流到志愿计算机。使用这些设施的科学家将看到更快的周转时间；他们将受益于志愿者计算，而他们甚至不知道它的存在。2)该项目将改变志愿者界面，使参与者签约科学领域和目标，而不是特定的项目。例如，参与者可能会签约为癌症研究做出贡献。作为该项目的一部分开发的中央经纪人将动态地将他们的计算资源分配给进行这类研究的项目。该项目通过鼓励“志愿者计算”和让公民参与研究本身来动员公众对科学研究的支持和兴趣。它同时推进了NSF在扩大公民参与的同时促进科学发展的使命。该项目的第一年将对这些部分进行原型设计，并将BOINC与TACC和NanHub整合在一起。将BOINC与现有HTC系统集成涉及几个子任务：1)作业路径：修改TACC和NanHub(分别为Launcher和Rappture)使用的现有作业处理系统，以决定何时应将一组作业卸载到BOINC。此决策可能涉及作业的估计运行时间、输入和输出文件大小、数据敏感度、作业的最后期限或优先级以及作业提交者的身份。2)作业格式转换：将作业描述(输入/输出文件规格、资源和时间要求)映射到BOINC等效项。3)应用打包：适配现有应用(如NanHub的模拟工具和TACC的Autodock)以在BOINC下运行。我们将使用BOINC的虚拟机工具，该工具将应用程序打包为虚拟机映像(VirtualBox或Docker)和在VM中运行的程序。这允许现有的Linux应用程序在Windows和Mac等消费类桌面平台上运行，并提供强大的安全沙箱和高效的独立于应用程序的检查点/重启机制。4)文件处理：在现有存储系统(通常无法从防火墙外部访问)之间将输入和输出文件移动到互联网可见的服务器。这将使用现有的基于哈希管理文件的BOINC组件，以消除重复的文件传输和存储。5)作业监测和控制：调整现有的基于网络或命令行的工具，用于监测批次作业的进度和中止作业，以便与BOINC合作。这将使用BOINC提供的现有Web RPC用于这些目的。该项目将根据需要设计和实现新软件，测试正确性、性能和可伸缩性，并将其部署到生产环境中，从而执行这些任务。该项目的第二部分--根据志愿者的科学偏好分配计算能力的中介系统--将被设计并制作原型。这涉及几个分任务：1)设计志愿人员偏好的模式，包括科学领域和分领域、项目国籍和机构、具体项目和应用、纳入/排除等等。2)设计用于将属性分配给作业流(例如，其区域、子区域、机构等)以及用于为作业流分配配额或优先级的模式。3)设计存储上述信息的关系数据库。4)设计和实施以尊重志愿者偏好并优化配额、公平性和吞吐量标准的方式将志愿者资源分配到工作流的政策。这将作为BOINC的“客户经理”来实现，这样志愿者就可以看到单一的界面，而不是许多独立的项目和网站。

The aggregate computing power of consumer devices - desktop and laptop computers, tablets, smartphones - far exceeds that of institutional computing resources. "Volunteer computing" uses these consumer devices, volunteered by their owners, to do scientific computing. In addition to providing additional, much-needed computational resources to scientists, volunteer computing publicizes scientific research and engages citizens in science. BOINC is the primary software system for volunteer computing. It was developed at UC Berkeley with NSF support starting in 2002. Until now, BOINC has been based on a model of independent competing projects. Scientists set up their own BOINC servers, port their applications to run on BOINC, and publicize their projects to attract volunteers. There are about 40 such projects, in many areas of science: examples include Einstein@home, CERN, and SETI@home (astrophysics), Rosetta@home and GPUGrid.net (biomedicine), Climateprediction.net (climate study), and IBM World Community Grid (multiple applications). Together these projects have about 400,000 active volunteers and 12 PetaFLOPS of computing throughput. This model, while successful to an extent, has reached a limit. The number of projects and volunteers has stagnated. Volunteer computing is supplying lots of computing power, but only to a few research projects. For other scientists, there are two major barriers. First, creating a BOINC project has significant overhead: learning a new technology, creating a public web site, generating publicity, and so on. Second, volunteer computing is risky and uncertain; there is no guarantee that a new project will attract volunteers. This project aims to break this barrier, and to make volunteer computing available to all scientists doing high-throughput computing, by replacing the competing-projects model with a new "central broker" model. The new model has two related parts: 1) the integration of BOINC with existing high-throughput computing facilities such as supercomputing centers and science portals. Jobs currently run on cluster nodes will be transparently offloaded to volunteer computers. Scientists using these facilities will see faster turnaround times; they'll benefit from volunteer computing without even knowing it's there. 2) The project will change the volunteer interface so that participants sign up for scientific areas and goals rather then for particular projects. For example, a participant might sign up to contribute to cancer research. A central broker, to be developed as part of this project, would dynamically assign their computing resources to projects doing that type of research. This project mobilizes public support for and interest in scientific research by encouraging "volunteer computing" and engaging citizens in the conduct of the research itself. It simultaneously advances NSF's mission to advance science while broadening citizen engagement.The first year of this project will prototype each of these parts, and will integrate BOINC with TACC and nanoHub. Integrating BOINC with existing HTC systems involves several subtasks: 1) Job routing: modifying existing job processing systems used by TACC and nanoHub (Launcher and Rappture respectively) to decide when a group of jobs should be offloaded to BOINC. This decision might involve the estimated runtime of the jobs, input and output file sizes, data sensitivity, the deadline or priority of the jobs, and the identity of the job submitter. 2) Job format conversion: mapping job descriptions (input/output file specifications, resource and timing requirements) to their BOINC equivalents. 3) Application packaging: adapting existing applications (such as nanoHub's simulation tools and TACC's Autodock) to run under BOINC. We will use BOINC's virtual machine facility, which packages an application as a virtual machine image (VirtualBox or Docker) and a program to be run within the VM. This allows existing Linux applications to run on consumer desktop platforms such as Windows and Mac, as well as providing a strong security sandbox and an efficient application-independent checkpoint/restart mechanism. 4) File handling: moving input and output files between existing storage systems (typically inaccessible from outside firewalls) to Internet-visible servers. This will use existing BOINC components that manage files based on hashes to eliminate duplicate transfer and storage of files. 5) Job monitoring and control: adapting existing web- or command-line based tools for monitoring the progress of batches of jobs, and for aborting jobs, to work with BOINC. This will use existing Web RPCs provided by BOINC for these purposes. This project will carry out these tasks by designing and implementing new software as needed, testing for correctness, performance, and scalability, and deploying it in a production environment. The second part of the project - a brokering system for allocating computing power based on volunteer scientific preferences - will be designed and prototyped. This involves several subtasks: 1) Designing a schema for volunteer preferences, including scientific areas and sub-areas, project nationality and institutions, specific projects and applications, inclusions/exclusions, and so on. 2) Designing a schema for assigning attributes to job streams (e.g. their area, sub-area, institution, etc.), and for assigning quotas or priorities to job streams. 3) Designing a relational database for storing the above information. 4) Designing and implementing policies for assigning volunteer resources to job streams in a way that respects volunteer preferences and optimizes quota, fairness, and throughput criteria. This will be implemented as a BOINC "account manager" so that volunteers see a single interface rather than lots of separate projects and web sites.