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

The aggregate computing power of consumer devices - desktop and laptop computers, tablets, smartphones - far exceeds that of institutional computing resources. “志愿计算”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。 In addition to providing additional, much-needed computational resources to scientists, volunteer computing publicizes scientific research and engages citizens in science. BOINC 是志愿者计算的主要软件系统。 它是在 2002 年开始在美国国家科学基金会 (NSF) 的支持下在加州大学伯克利分校开发的。到目前为止，BOINC 一直基于独立竞争项目的模型。 科学家们建立自己的BOINC服务器，将他们的应用程序移植到BOINC上运行，并宣传他们的项目来吸引志愿者。 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). 这些项目总共拥有约 400,000 名活跃志愿者和 12 PetaFLOPS 的计算吞吐量。这种模式虽然在一定程度上取得了成功，但已经达到了极限。 项目和志愿者的数量停滞不前。 志愿计算正在提供大量的计算能力，但仅限于少数研究项目。 对于其他科学家来说，存在两个主要障碍。 First, creating a BOINC project has significant overhead: learning a new technology, creating a public web site, generating publicity, and so on. 其次，志愿计算具有风险和不确定性；无法保证新项目会吸引志愿者。 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.当前在集群节点上运行的作业将透明地卸载到志愿者计算机上。使用这些设施的科学家将看到更快的周转时间；他们会从志愿者计算中受益，甚至不知道它的存在。 2) The project will change the volunteer interface so that participants sign up for scientific areas and goals rather then for particular projects.例如，参与者可能会报名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其计算资源分配给进行此类研究的项目。 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) 作业格式转换：将作业描述（输入/输出文件规范、资源和时间要求）映射到其 BOINC 等效项。 3）应用程序打包：将现有应用程序（例如nanoHub的模拟工具和TACC的Autodock）改编为在BOINC下运行。 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. 这将使用基于哈希值管理文件的现有 BOINC 组件来消除文件的重复传输和存储。 5) 作业监控和控制：采用现有的基于 Web 或命令行的工具来监控批量作业的进度以及中止作业，以与 BOINC 配合使用。这将使用 BOINC 提供的现有 Web RPC 来实现这些目的。 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.该项目的第二部分——一个根据志愿者科学偏好分配计算能力的经纪系统——将被设计和原型化。 这涉及到几个子任务：1）设计志愿者偏好的模式，包括科学领域和子领域、项目国籍和机构、具体项目和应用、包含/排除等。 2) 设计一个架构，用于为作业流分配属性（例如，其区域、子区域、机构等），以及为作业流分配配额或优先级。 3）设计一个关系数据库来存储上述信息。 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. 这将作为 BOINC“客户经理”实施，以便志愿者看到一个界面，而不是许多单独的项目和网站。

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.