Analysis and summary of stakeholder recommendations First Kepler/CORE Stakeholders Meeting, May 13-15, 2008

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1 Analysis and summary of stakeholder recommendations First Kepler/CORE Stakeholders Meeting, May 13-15, 2008 I. Assessing Kepler/CORE development priorities The first Kepler Stakeholder s meeting brought together the Kepler/CORE team and Kepler project stakeholders. Thirty-four participants represented over twenty distinct R&D projects depending in various degrees on Kepler. A major objective of the meeting was to gather recommendations for Kepler/CORE development. These recommendations were gathered and assessed in two ways. First, a series of panels proposed and discussed specific, concrete recommendations related to seven distinct areas of Kepler development. Meeting participants evaluated each recommendation using an audience response system. Second, meeting participants voted on the relative importance of 14 general areas of development. II. General priorities for Kepler/CORE development Each meeting participant was given the opportunity to indicate the most important areas for Kepler development by distributing up to three among the topics listed in the table below. The table is sorted by number of given to each. Votes Topic 15 Packaging, distribution, and build 12 Distributed workflow execution 10 Web-based user interface 10 GUI design 8 Data management (access, share, etc.) 7 Provenance tracking 7 Sharing actors and workflows 5 Running external apps 4 Workflow design 4 Monitor and debug runs 4 Test framework 3 Parameter sweeps 2 Streaming data 0 Sharing data and reporting results III. Evaluation of specific recommendations During each of the seven panel discussions, specific recommendations for Kepler/CORE development were made by the panel members and other meeting participants. At the end of each session all participants evaluated these recommendations anonymously,

2 indicating their support for each according to the following scheme: A. to the success of my project, Kepler/CORE should do this now. B. Important for my project, Kepler/CORE should do this eventually. C. Useful but not essential for my project, not essential that Kepler/CORE do this. D. Useless for my project, Kepler/CORE should not do this. E. Not sure. I don t understand this recommendation or am not sure if it is useful for my project. Note that participants were urged to vote from the point of view of the success of the particular project(s) they represented. A total of 117 recommendations were made and evaluated in this way, and an average of 30 (range 26-34) were obtained for each recommendation. IV. Ranking of recommendations The recommendations were ranked by two simple measures of agreement. First, the recommendations were sorted by the number of A, i.e. by the number of participants who said that the recommendation was critical to the success of their project. This approach ignores all other. Between 0 and 19 (average=6.4) critical were received by each recommendation. The distribution of the critical vote count is shown below: Distribution of recommendations by critical awarded 35 s with the given number of critical awarded

3 Second, the recommendations were ranked according to a weighted computed as follows: = 2 * critical + important - useless That is, the weighted is twice the number of A plus the B minus the D. This approach takes into account those indicating that a recommendation is important but not critical, as well as those requesting that the recommendation not be addressed by Kepler/CORE at all. s ranged from 7 to 45 in value, with an average of The distribution of weighted s is plotted below: Distribution of recommendations by weighted 25 s with the given weighted < V. Consistency check Despite the broad range of agreement on the various proposals, considerable overlap was found between the recommendations ranked in the top ~25% according to both measures of agreement. In particular, the top 20% of recommendations according to weighted all are within the top 26% ranked recommendations according to the number of critical received. Twenty-four recommendations gathered nine or more critical (compared to an average count of 6) and weighted s of 28 or more (compared to an average of 19). Only nine additional recommendations had critical >= 9 or a weighted >= 28. Finally, none of the 33 recommendations ranked highest by either measure received more than three useless. We conclude there was strong support for the recommendations that d highly according to both measures, and moderate support for those that d well by one measure or the other.

4 VI. Twenty-four recommendations with strong support The following table lists the recommendations with weighted >= 28 and critical >= 9. The recommendations are ordered by weighted Refactor to separate execution engine completely from user interface using MVC pattern -- create a UI Event model and clientserver event subscription framework to support multiple clients on varying platforms. Kepler should be refactored for a cleaner split, code-wise, between workflow authoring and runtime environments. The Kepler import/export functionality for actors using the KAR (Kepler Archive) format should be fully supported. Provide a mechanism to precisely reference and/or embed component dependencies and their versions (think DEB and RPM formats) jar/class dependencies, native code dependencies, data dependencies. There need to be well-defined, stable versions of the Kepler kernel and standard extensions that other projects can conveniently build against Allow various UIs to attach/detach from execution engine The Kepler development environment must facilitate the development of actors and extensions The copyrights and licensing that apply to any packaging or build of Kepler should be apparent Develop a generic component in Kepler for running external jobs, sets of jobs, and parameter sweeps Manage component versioning support actor contracts for backward compatibility using standards such as LSIDs and DOIs Kepler should provide an interface/api for monitoring and controlling Kepler from external applications. Actor implementations should be divorced from their visual representation required by workflow authoring environments. Kepler should allow workflows to be applied to different input data sets and re-parameterized without modifying the workflow specification. Develop and/or adopt standard interfaces for Kepler to integrate with archives and data systems. Kepler should support uniform support for both local and remotely stored data. Resolve dependency and versioning conflicts among components (dynamic class loading and unloading) Make it easy to document and save actors from within the editing GUI environment, and support controlled annotations of actors and

5 GUI environment, and support controlled annotations of actors and workflows It needs to be clear what project and individual engineers are responsible for developing and maintaining each part of Kepler and each extension stored in the Kepler source code repository Better debugging and error reporting support for running external applications (including expression actors, R, Matlab) Use an abstract repository API to support saving to multiple repositories Provide an improved actor versioning environment that provides users with a way to know what has changed between versions, and prevents newly invalidated workflows from running Kepler should provide explicit support for parameter sweeps Kepler should supply best practices, checklists, FAQs, "prototypical" actor examples, etc Provide a better way to share code than in large KAR files Development overhead must be reduced. VII. Nine additional recommendations with moderate support The following table lists additional recommendations with weighted >= 28 or critical >= 9. The recommendations are ordered by weighted Support customizable web applications for: (1) discovering workflows and components; (2) configuring workflows; (3) executing workflows; (4) presenting results Kepler needs better approaches for discovering actors and their capabilities by users. Allow browsing and search for actors based on metadata + documentation Kepler needs to support access to multiple distributed data stores Remote control panel for managing Kepler jobs Support multiple re-entry to workflows via checkpointing Kepler should support alternative data/project stores Kepler should support provenance archives that contains all information needed for repeatability, including source code, test data, dependencies (e.g., jars), metadata, etc. Kepler/CORE should put most of its effort into making it easy (ideally, "fun") to extend the system.

6 VIII. Well supported recommendations by panel topic The following tables group the strongly and moderately supported recommendations above according to the panel discussions in which they were suggested. In one case, a recommendation made by one panel is grouped with another panel. s are ordered according to their weighted s within each table. Strongly represented recommendations are in black normal font, moderately supported recommendations in blue italics. Panel 1. Managing data, workflows, and multiple workflow runs within projects Develop a generic component in Kepler for running external jobs, sets of jobs, and parameter sweeps Kepler should provide an interface/api for monitoring and controlling Kepler from external applications. Kepler should allow workflows to be applied to different input data sets and re-parameterized without modifying the workflow specification. Develop and/or adopt standard interfaces for Kepler to integrate with archives and data systems. Kepler should support uniform support for both local and remotely stored data Kepler should provide explicit support for parameter sweeps Kepler needs to support access to multiple distributed data stores (recommended by Panel 4) Kepler should support alternative data/project stores Kepler should support provenance archives that contains all information needed for repeatability, including source code, test data, dependencies (e.g., jars), metadata, etc.

7 Panel 2. Sharing actors and workflows between projects Provide a mechanism to precisely reference and/or embed component dependencies and their versions (think DEB and RPM formats) jar/class dependencies, native code dependencies, data dependencies The Kepler import/export functionality for actors using the KAR (Kepler Archive) format should be fully supported Manage component versioning support actor contracts for backward compatibility using standards such as LSIDs and DOIs. Resolve dependency and versioning conflicts among components (dynamic class loading and unloading) Make it easy to document and save actors from within the editing GUI environment, and support controlled annotations of actors and workflows Use an abstract repository API to support saving to multiple repositories Provide an improved actor versioning environment that provides users with a way to know what has changed between versions, and prevents newly invalidated workflows from running Provide a better way to share code than in large KAR files Kepler needs better approaches for discovering actors and their capabilities by users. Allow browsing and search for actors based on metadata + documentation.

8 Panel 3. Moving beyond the desktop environment Refactor to separate execution engine completely from user interface using MVC pattern -- create a UI Event model and clientserver event subscription framework to support multiple clients on varying platforms. Kepler should be refactored for a cleaner split, code-wise, between workflow authoring and runtime environments Allow various UIs to attach/detach from execution engine. Actor implementations should be divorced from their visual representation required by workflow authoring environments Support customizable web applications for: (1) discovering workflows and components; (2) configuring workflows; (3) executing workflows; (4) presenting results Remote control panel for managing Kepler jobs Support multiple re-entry to workflows via checkpointing Panel 4. Distributed workflow execution and data management One recommendation of this panel was moderately well supported but is grouped better with the recommendations made by Panel 1 above. Panel 5. Designing and implementing actors and workflows Better debugging and error reporting support for running external applications (including expression actors, R, Matlab) Kepler should supply best practices, checklists, FAQs, "prototypical" actor examples, etc. Panel 6. Testing and debugging workflows, monitoring runs, and optimizing performance No strongly or moderately well supported recommendations were made during this panel discussion.

9 Panel 7. Extending, customizing, packaging, and distributing Kepler There need to be well-defined, stable versions of the Kepler kernel and standard extensions that other projects can conveniently build against The Kepler development environment must facilitate the development of actors and extensions. The copyrights and licensing that apply to any packaging or build of Kepler should be apparent. It needs to be clear what project and individual engineers are responsible for developing and maintaining each part of Kepler and each extension stored in the Kepler source code repository 28 9 Development overhead must be reduced Kepler/CORE should put most of its effort into making it easy (ideally, "fun") to extend the system IX. Integration of general priorities with specific recommendations We observe that the assessment of general priorities by the stakeholders (section II) complements their evaluation of specific recommendations (sections III-VIII). Both need to be taken into account when prioritizing and steering Kepler/CORE development. For example, none of the specific recommendations made during the panel discussion on distributed workflow execution and data management were strongly supported by the meeting participants. On the other hand, distributed workflow execution was voted the second most important general priority for Kepler/CORE development. We conclude that while Kepler/CORE must in fact address the Kepler community s needs for distributed workflow execution, there currently is no consensus on how these needs should be addressed. Additional research and discussion is needed before deciding how to support distributed workflow execution. In contrast, packaging, distribution, and build was voted the single most important general priority for Kepler/CORE, and five specific recommendations during the panel discussion, extending, customizing, packaging, and distributing Kepler were strongly supported. Similarly, Web-based user interface rated high as a general priority, and four concrete recommendations made during the moving beyond the desktop environment panel were strongly supported. In these cases, Kepler/CORE can move more quickly towards addressing the Kepler community s needs. X. More information about these results An Excel spreadsheet listing all of the recommendations made during the stakeholders meeting, the and s for each, and the analyses summarized above, is available in the Kepler project repository.

10 Appendix. Additional recommendations The recommendations below were not strongly supported by either metric defined in section IV. The recommendations are listed in order of decreasing weighted Policies should be established for contributing tests and for fixing and resolving test failures. Provide a way to easily restrict the number of actors available in a Kepler instance Standardize a core part of the Kepler provenance model Kar files should include small tests/examples Need to broadcast priorities and status other than through bugzilla 25 7 Kepler should include support for storing and organizing project data, workflow definitions, and provenance between runs Is code coverage by good tests important? 24 6 Nightly tests should be sent to the people developing the code. Demo workflows should have "smoke tests" Need to send a quarterly report, with project updates and what is coming soon Out-of-the-box web execution framework (e.g., scientist designs workflow in Kepler, deploys to web server) Better support for designing actors/workflows with interactive execution, e.g., conditional execution based on user input Distributed workflow execution and distributed job execution are separate issues. Kepler should provide capabilities for both within the same workflow New features in Kepler should not increase the execution time of workflows, according to a defined set of Kepler benchmarks Kepler should allow workflows to be deployed as services Kepler should support workflow checkpoint/restart so that the workflow can be restarted if it fails, or if we are incrementally developing a workflow and want to test it. Web based user interfaces should accommodate a range of possible users from ones who want a simple interface to those who want to modify parameters. Need two channels of communication -- one with technical details and one as a digest. Need a better way to share/search for actors using a more intuitive organization that includes local repositories as well as global and project-based repositories. Need token system to support transfer of data both by value and by reference. Ideally, actors do not have to know if a received token is by value or by reference.

11 by value or by reference Parameter sweep functionality should be independent of particular Grid engines (e.g., it should work equally well with Sun Grid Engine, Globus, etc.) 20 6 Provide recommendations (at least) for best practices from different approaches throughout the system to monitor I/O, remote tasks, data transfer, synchronization, etc Kepler should provide easy-to-use, runtime type system extension points to better support scientific data formats and streaming data Actor repositories should be slanted toward sharing packages of actors meant to be used together along with documentation of the actor suite, sample workflows, etc Extension points for provenance support must be provided for distributed data management and workflow execution Extend an actor API for provenance observables Adopt/adapt/support a customizable web app that ships with Kepler Provide a mechanism to package actors (KAR+) that can be exported/imported; actor and documentation KAR files should be signable; and kepler should support execution sandboxes for security Kepler should provide a transparent (and fault-tolerant) data movement operation to transfer data between two actors in the workflow without explicit data transfer actor in between (like in job-oriented workflow systems) Run management should include the concept of "scenarios" where a run represents a particular parameterization and data binding, and can include scenario metadata, e.g., the "IPCC High scenario" Need a quarterly telecom among project PIs Kepler needs a remote Kepler service for executing headless workflows, which makes it easy to share Kepler CPU cycles amongst diverse groups Parameter sweeps should be able to be run on individual composite actors that represent a chunk of work to be parameterized and run (i.e., not whole workflows) Kepler should simplify the management of authentication credentials and authorization requests Support for setting checkpoints on data types for debugging Kar files should have a stability rating system A separate run panel needs to be redesigned as a dashboard with monitoring and run related features, that can be separated from editor Provide a mechanism for customizing the runtime window Need abstraction of Grid middleware: (1) remote execution services; and (2) distributed data access.

12 15 6 Generate UI interfaces automatically from parameter sets A catalog of named user-defined data types, specified, e.g., within the Kepler GUI It should be clear for "non-insiders" how to extend and modify the Kepler/Ptolemy GUI Workflow packaging and sharing should allow for the packaging and sharing of portions of a workflow, as well as the entire workflow 14 6 Kepler should have "traditional" debugging capabilities, like: (1) stop workflow / check wf. parameters and variables of java actors; (2) change variables/parameters and continue; (3) set breakpoint (conditional breakpoint) on ports, on java code of actor An interface that can be used to define a template for a workflow, that, e.g., a wizard could use to build a concrete workflow from Support separate design and execution phases, e.g., "dry run" The current stepping mechanism should be extended to domains other than SDF Need capabilities to handle/process real-time data streams Kepler needs a user identification mechanism Third-party data transfer as part of serial analysis data created by one actor moved elsewhere for another actor/workflow to perform analysis Provide mechanisms for automatic deployment of all or parts of a workflow as a batch job or an online service Kepler should support caching of remote data Kepler should include/provide a web-based authoring tool Need a developers forum Support web applications targeting different user roles Kepler should not rely on local names for data, and should support data versioning using existing standards, particularly LSID + DOI Kepler should provide a run history view showing flow of data through workflow runs Need resource brokering services to abstract remote execution services Support automatic staging and unstaging of data/files Better Eclipse and/or NetBeans setup (for debugging) Standard mechanism for constructing at least simple workflows in a programmatic manner (e.g., Kepler script) Need third party file transport facilities between remote resources Define a single, scalable data model for collecting provenance information and information needed for monitoring and faulttolerance. 9 6 Kepler should support non-actor-based data access/storage mechanisms. A model for getting data into and out of the system other than through actors.

13 9 4 Need Kepler Service Provider to avoid ad-hoc actor/workflow execution. 8 4 Provide an actor authoring wizard. 8 3 Kepler code should be formatted to a standard style. 8 0 Consolidation of configurable provenance capabilities is needed. 7 3 A type definition browser. 7 3 Kepler should support failover and load balancing when externally executing workflows. 7 2 Kepler should have a fault-tolerant data movement operation between two resources that automatically discovers what tools can be used for the transfer (scp, gridftp, srm, srb...). 7 2 Provide an interface for hiding features you don't want. 7 1 Develop a plug-in model to create custom user interfaces for assigning parameters and parameter sets. 7 1 The current highlighting mechanism should be extended to domains other than SDF. 6 3 Kepler should optimize the speed of a (state-less) actor in a pipeline using multiple threads/jobs/services if it is the bottleneck in the execution. 6 3 Alternative (Web based?) monitoring interfaces with provenance query & visualization, blogging and annotation features. 4 2 Kepler should support property-based parameter sweeps, where lists of parameters are given in a property list or table. 4 1 Meta-kepler/workflow to monitor/manage a kepler workflow. 3 2 Have a way to initialize Kepler with a data context, define and/or initialize Kepler with provenance context. 2 1 Perform marshalling and unmarshalling of data within a link (channel) Kepler/CORE should create and maintain, e.g., Amazon Elastic Compute Cloud machine images and/or virtual machine images (VMWare or similar) so that organizations and researchers can fire up extra instances and run workflows as needed Need messaging interface for inter-kepler service communication -5 2 Kepler should support virtual subworkflows (reverse composite actor) -5 0 Support a collaborative environment where multiple users would connect to a shared workspace GUI and allow creating/editing of the workflow (e.g., Cmap Server collaboration) Information about common port relationships derived from real workflows stored in repositories would be useful for workflow authors. E.g., that port A of actor X is often connected to port B of actor Z.

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