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Workflow management within DIET Raphaël Bolze LIP ENS Lyon, CNRS INRIA Rhône-Alpes, GRAAL project

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Presentation on theme: "Workflow management within DIET Raphaël Bolze LIP ENS Lyon, CNRS INRIA Rhône-Alpes, GRAAL project"— Presentation transcript:

1 Workflow management within DIET Raphaël Bolze LIP ENS Lyon, CNRS INRIA Rhône-Alpes, GRAAL project

2 R. Bolze – 19 oct 2006 Edinburgh 2 Introduction Distributed Interactive Engineering Toolbox RPC and grid-computing : gridRPC DIET goals DIET environment & architecture Request management Research topics & features DIET and workflow management Needs Language Architectures Scheduling propose Target applications PipeAlign Docking Robinson Cosmology Current works

3 D istributed I nteractive E ngineering T oolbox

4 R. Bolze – 19 oct 2006 Edinburgh 4 RPC and Grid-Computing: GridRPC One simple idea One simple (and efficient) paradigm for grid computing: offering (or leasing) computational power and/or storage capacity through the Internet One simple solution: implementing the RPC programming model over the Grid –Using resources accessible through the network –Mixed parallelism model (data-parallel model at server level and task parallelism between servers) Features needed –Load-balancing (resource localization and performance evaluation, scheduling), –Data and replica management, –Security, –Fault-tolerance, –Interoperability with other systems, –… Design of a standard interface – within the GGF/OGF (GridRPC WG, C. Lee) –www.ogf.org, forge.gridforum.org/projects/gridrpc-wgwww.ogf.org – Existing implementations: GridSolve, Ninf, DIET, XtremWeb

5 R. Bolze – 19 oct 2006 Edinburgh 5 RPC and Grid Computing: Grid RPC AGENT(s) S1S2 S3 S4 A, B, C Answer (C) S2 ! Request Op(C, A, B) Client

6 R. Bolze – 19 oct 2006 Edinburgh DIETs Goals Our goals To develop a toolbox for the deployment of environments using the Application Service Provider (ASP) paradigm with different applications Use as much as possible public domain and standard software To obtain a high performance and scalable environment Implement and validate our more theoretical results Scheduling for heterogeneous platforms, data (re)distribution and replication, performance evaluation, algorithmic for heterogeneous and distributed platforms, … Based on CORBA, NWS, LDAP, and our own software developments CoRI for performance evaluation, FAST CoRI-easy LogService for monitoring, VizDIET for the visualization, GoDIET for the deployment Several applications in different fields (simulation, bioinformatic, cosmological application…) Release 2.1 available on the web Release 2.2 coming soon

7 R. Bolze – 19 oct 2006 Edinburgh 7 DIET Environment CLIENT Sequential Application Data management Application Parallel Application C C C C C C C C C A A A SSS A SSS A A A A A A A

8 R. Bolze – 19 oct 2006 Edinburgh 8 DIET Architecture LA MA LA ServerDeamons Master Agent Local Agent Client LA

9 R. Bolze – 19 oct 2006 Edinburgh 9 Requests Management agent server estimate() { predExecTime(…); } FindServer() Aggregate() { min(…); } Aggregate() { min(…); } bestServer = S3 runService(…);

10 R. Bolze – 19 oct 2006 Edinburgh 10 Research Topics Scheduling Distributed scheduling Plug-in schedulers Data-management Scheduling of computation requests and links with data-management Replication, data prefetching Deployment Mapping components on available (selected) resources Software platform deployment with or without dynamic connections between components Performance evaluation Application modeling Dynamic information about the platform (network, clusters) Fault Tolerance Failure Detection Application recovery …

11 Scheduling

12 R. Bolze – 19 oct 2006 Edinburgh 12 DIET Scheduling SeD level Performance estimation function Estimation metric vector (estVector_t) - dynamic collection of performance estimation values Performance measures available through DIET FAST-NWS performance metrics Time elapsed since the last execution CoRI (Collector of Resource Information) Developer defined values Standard estimation tags for accessing the fields of an estVector_t EST_FREEMEM EST_TCOMP EST_TIMESINCELASTSOLVE EST_FREECPU Aggregation Methods Defining mechanism how to sort SeD responses: associated with the service and defined at SeD level Tunable comparison/aggregation routines for scheduling Priority Scheduler Performs pairwise server estimation comparisons returning a sorted list of server responses; Can minimize or maximize based on SeD estimations and taking into consideration the order in which the request for those performance estimations was specified at SeD level.

13 R. Bolze – 19 oct 2006 Edinburgh 13 DIET Scheduling Collector of Resource Information (CoRI) CoRI-Easy – provides basic measurements of the environment CoRI Manager – manage the use of different collectors CoRI-Easy Collector FAST Collector CoRI Manager Other Collectors like Ganglia FAST Software

14 Data management

15 R. Bolze – 19 oct 2006 Edinburgh 15 Data/replica management Two needs Keep the data in place to reduce the overhead of communications between clients and servers Replicate data whenever possible Two approaches for DIET DTM (LIFC, Besançon) Hierarchy similar to the DIETs one Distributed data manager Redistribution between servers JuxMem (Paris, Rennes) P2P data cache Work done within the GridRPC Working Group (OGF) Relations with workflow management Client A F G Y Server 1 Server 2 X B B B

16 R. Bolze – 19 oct 2006 Edinburgh 16 Data management with DTM within DIET Persistence at the server level To avoid useless data transfers Intermediate results Between clients and servers Between servers transparent for the client Data Manager/Loc Manager Hierarchy mapped on the DIET one modularity Proposition to the Grid-RPC WG (OGF) Data handles Persistence flag Data management functions

17 R. Bolze – 19 oct 2006 Edinburgh 17 JUXMEM A peer-to-peer architecture for a data-sharing service in memory Persistence and data coherency mechanism Transparent data localization PARIS project, IRISA, France Peer Firewall Peer TCP/IP HTTP Peer ID Firewall Toolbox for the development of P2P applications Set of protocols One peer Unique ID Several communication protocols (TCP, HTTP, …)

18 Deployment and visualization

19 R. Bolze – 19 oct 2006 Edinburgh 19 Deployment Management XML: - Resources - Machines - Storage - DIET hierarchy Distributed deployment of DIET LogServiceGoDIETVizDIET DIET Administration Traces Trace subset Trace Subset

20 R. Bolze – 19 oct 2006 Edinburgh 20 VizDIET

21 Workflow management

22 R. Bolze – 19 oct 2006 Edinburgh 22 Workflow Management : needs ? Workflow representation : Direct Acyclic Graph (DAG) Each vertex is a tasks Each directed edge represents communication between tasks Questions : Ordering problem ? Mapping problem ?

23 R. Bolze – 19 oct 2006 Edinburgh 23 Workflow Management : goals Goals Build and execute workflow Use different heuristic methods to solve scheduling problems Extensibility to address mutli-workflows submission and large grid platform Manage heterogeneity and variability of environment

24 R. Bolze – 19 oct 2006 Edinburgh 24 Workflow Management : existing languages ? Workflows languages: No standard (XML, scripts) Exemples : Condor DAGman : script Pegasus : DAX (xml) Taverna : XScuffl (xml) 2 levels of description : Abstract : application description Concrete : execution description

25 R. Bolze – 19 oct 2006 Edinburgh 25 Workflow Management Workflow description in DIET Xml format DIET profile : problem (id), parameters (in, inout,out) Description of tasks and data dependency

26 R. Bolze – 19 oct 2006 Edinburgh 26 Workflow Management : architecture 2 Architectures : Meta scheduler in the client side Meta scheduler distributed in the client and in the MA-DAG

27 R. Bolze – 19 oct 2006 Edinburgh 27 Workflow Management : Meta scheduler : client Architecture 1 : Meta scheduler in the client side Client MA LA SeD

28 R. Bolze – 19 oct 2006 Edinburgh 28 Workflow management : Meta scheduler : client Disadvantages : No coordination between the different clients Depends on client capability Benefits : More flexible for evolution : Client can use his own algorithm. More scalable, depends on client capability.

29 R. Bolze – 19 oct 2006 Edinburgh 29 Workflow management Architecture 2 : Meta scheduler distributed in the client and in the MA-DAG Client MA LA SeD MA DAG

30 R. Bolze – 19 oct 2006 Edinburgh 30 Workflow management - Meta scheduler Base Scheduler : No ranking, respect the topological order of the DAG HEFT heuristic Flexibility : Architecture 1 : Client can have his own schedule No needs to re-build the platform Architecture 2 : Schedulers are define at the compile time. Needs to re-build the platform if some decide the change. Abstract Workflow Scheduler Virtual void execute(); Virtual void reSchedule(); User defined Scheduler Virtual void execute(); Virtual void reSchedule();

31 Target applications

32 R. Bolze – 19 oct 2006 Edinburgh 32 Docking Application Detection of protein-protein and protein-DNA interactions. Screening a database containing thousands of proteins for functional sites involved in binding to other proteins, DNA or ligand targets. docking merge params docking

33 R. Bolze – 19 oct 2006 Edinburgh 33 PipeAlign Application The sequence-to-function relationship can be understood through the analysis of conserved patterns and evolution of protein organization mainly based on amino acid sequence comparisons in the context of the multiple alignments. blastall ballast filtering clustalw normd rascal normd leon normd

34 R. Bolze – 19 oct 2006 Edinburgh 34 Robinson application This application annotate human genes according to their expression in neurological or muscular tissues, but also to the expression of their homolog other species. extract Build DB blastall

35 R. Bolze – 19 oct 2006 Edinburgh 35 Cosmology application rollWhiteNoise Grapfic1 Grapfic2 Ramses3D HaloMaker TreeMaker + GalaxyMaker HaloMaker Simulate the evolution of dark matter particles during time to compare it to the real observation. Centre de Recherche en Astronomie de Lyon

36 Current Work

37 R. Bolze – 19 oct 2006 Edinburgh 37 Multi-Workflow Deal with multiple workflow submission On-line scheduling, different submission time Implements fair scheduling strategies Implements specific scheduling heuristics Distribute the workflow management ? grid

38 R. Bolze – 19 oct 2006 Edinburgh 38 Multi-Workflow Simulations Real experiments on Grid5000

39 R. Bolze – 19 oct 2006 Edinburgh 39 Conclusion DIET Workflow enabled Data management : DTM, JuXMEM Performance information : CoRI, FAST Plugin schedulers Multi-Applications

40 Questions ?


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