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le middleware pour gds http graal ens lyon fr diet n.
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ACI MD GDS

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  1. Le middleware pour GDS http://graal.ens-lyon.fr/~diet ACI MD GDS

  2. Plan • Réservation de ressource dans un ASP hiérarchique • Déploiement automatique • DIET en P2P • DIET vs NetSolve • VizDIET • Communications dans DIET • Une application pour GDS

  3. Context • One long term idea for Grid computing: renting computational power and memory capacity over the Internet • Very high potential • Need of Problem Solving Environments (PSEs) • Applications need more and more memory capacity and computational power • Some proprietary libraries or environments need to stay in place • Difficulty of installation for some libraries or applications • Some confidential data must not circulate over the net • Use of computational servers accessible through a simple interface • Need of schedulers • Moreover … • Still difficult to use for non-specialists • Almost no transparency • Security and accounting issues usually not addressed • Often application dependent PSEs • Lack of standards • (CORBA, JAVA/JINI, sockets, …) to build the computational servers

  4. RPC and Grid-computing  GridRPC • A simple idea • RPC programming model for the Grid • Use of distributed collections of heterogeneous platforms on the Internet • For applications require memory capacity and/or computational power • Task parallelism programming model (synchronous/asynchronous) + data parallelism on servers  mixed parallelism • Needed functionality • Load balancing • resource discovery • performance evaluation • Scheduling • Fault tolerance, • Data redistribution, • Security, • Interoperability, …

  5. Request S2 ! A, B, C Answer (C) GridRPC Client AGENT(s) Op(C, A, B) S1 S3 S4 S2

  6. GridRPC (con’t) • 5 main components: • Client • submits problems to servers • Gives users’ interfaces • Server • solves problems sent by clients • Runs software • Database • contains dynamic and static information about software and hardware resources • Scheduler • chooses an appropriate server depending of • the problem sent • the information contained in the database • Monitor • gets information about the status of the computational resources

  7. LA DIET - Distributed Interactive Engineering Toolbox - • Hierarchical architecture for an improved scalability • Distributed information in the tree • Plug-in schedulers MA MA MA Master Agent MA MA Server front end A Direct connection LA LA

  8. C A B FAST - Fast Agent’s System Timer - • NWS-based (Network Weather Service, UCSB) • Computational performance • Load, memory capacity, and performance of batch queues (dynamic) • Benchmarks and modeling of available libraries (static) • Communication performance • To be able to guess the data redistribution cost between two servers (or between clients and servers) as a function of the network architecture and dynamic information • Bandwidth and latency (hierarchical)

  9. PIF - Propagate Information Feedback - • Algorithm from distributed system research • PIF : Propagate Information Feedback • Two steps • First phase: broadcast phase • Broadcast one message through the tree • Second phase: feedback phase • When the leaf has no descendant feedback message is sent to their parent • When the parent receives the feedback messages from all its descendants, it sends a feedback message to its own parent, and so on

  10. A A LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 PIF and DIET - broadcast phase - MA 1. Broadcast the client’s request 2. Sequential FAST interrogation for each LA 3. Resource reservation

  11. A A S12 S15 S4 S7 LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 PIF and DIET - feedback phase - MA 1. chooses the identity of the most (or list of) ``appropriate'' server(s) 2. unused resources are released

  12. S12 S4 A A LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 PIF and DIET - feedback phase - MA 1. chooses the identity of the most (or list of) ``appropriate'' server(s) 2. unused resources are released

  13. A A LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 PIF and DIET - feedback phase - S12 MA 1. chooses the identity of the most (or list of) ``appropriate'' server(s) 2. unused resources are released

  14. Server failure and reactivity • Take into account server failure and increase the DIET reactivity • Time out at the LA level : • Dead Line 1 = ß1* Call_FAST_time + ß2 * nb_server MA A A S2 [DEAD LINE 1] S12 S15 S2 S7 LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16

  15. Hierarchical fault tolerance • No answer after dead line 1 • Dead Line 2 = ß3* level_tree MA S7 [DEAD LINE 2] S12 A A S12 S15 S7 LA LA LA LA S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16

  16. Simulation: SimGRID2 • Real experiments or simulations are often used to test or to compare heuristics. • Designed for distributed heterogeneous platforms • Simulations can enable reproducible scenarios • Simgrid: a distributed application simulator for scheduling algorithm evaluation purposes. • Event-driven simulation • Fixed or According to a trace values to characterize SimGrid resources • Processors • Network links • Simgrid2 : A simulator built using SG. This layer implements realistic simulations based on the foundational SG and is more application-oriented. • Simulations are built in terms of communicating agents.

  17. The DIET SimGRID2 simulator

  18. Evaluation of the PIF scheduler

  19. Conclusion and future work • Conclusion • Benefit from distributed system research • Fault tolerance into DIET • Server failure • Branch failure • Resource reservation performs a good QoS for client requests • DIET SimGRID2 simulator • Can be reused to validate other algorithms • Future work • Implementation of some tools to guarantee the resource reservation • Integrate NWS trace into the simulator • How to fix deadlines on a given heterogeneous platform ?

  20. Plan • Réservation de ressource dans un ASP hiérarchique • Déploiement automatique • DIET en P2P • DIET vs NetSolve • VizDIET • Communications dans DIET • Une application pour GDS

  21. Automatic deployment • Problem : Take the right number of components (resources) and place them in right way, to increase the overall performance of the platform. • Motivation : “how to deploy DIET on the grid ?” • Foundation : Idea given in the article “Scheduling strategies for master-slave tasking on heterogeneous processor grids” by C.Banino, O.Beaumont, A. Legrand and Y.Robert.

  22. Introduction • Solution : • Generate a new structure by arranging the resources according to the graph, which gives best throughput. • For homogeneous platform, the resources should be arranged in a binary tree type structure. • For heterogeneous platform, more resources should be added by checking the bottleneck in the structure.

  23. Deployment • Architectural model -

  24. Deployment • wi(Mflp/s): Computing power of node Pi • bij: capacite of link (links are symmetric and bidirectional) • Sini: size of incomming request from client • Souti: size of outgoing request (response) • alphaini: fraction of time for the computation of incomming request • alphaouti: fraction of time taken for the computaion of outgoing resuest

  25. Pi bij Pj Operations in steady state • Calculation of the throughput of a node

  26. Calculation of the throughput of a graph

  27. 7 20 Min(9,3+4) Min(7,6+5) 12 9 7 5 6 4 3 Example: Calculation of throughput of graph 12 Min(20,12) Min(12,7+7)

  28. Homogeneous Structures All nodes have same computing power and bandwidth link Star Graph 2 Depth Star Graph Binary Tree 2 Chain Graph Chain Graph

  29. Homogeneous Structures • Simulation results (with 8nodes) -

  30. Homogeneous Structures • Simulation results (with 32 nodes) -

  31. Homogeneous Structures • Simulation results (for Binary graph) -

  32. Heterogeneous Networks

  33. 24 17.88 19 15 0.31 0.052 29 0.88 Throughput of network 25 R = 2 30 25 25 30 25 25 30 1200 140 115

  34. 1.05 0.1167 0.1167 120 1100 1100 1.05 120 Throughput of network by adding LAs 24 R = 2 R = 2.2 R = 2.65 17.88 19 15 0.11 0.052 29 0.88 140 1200 115

  35. Heterogeneous Network

  36. Experimental results • 1 client with n requests • no steady state (MA performed good) • n clients with n requests • no steady state (MA performed good) • Pipeline effect • not enough nodes (clients) • Buffered the requests at MA • a new client implementation to make an effect of steady state • MA failed with 960 requests (due to memory problem)

  37. Experimental results

  38. Conclusion • Select best structure • Improve the throughput of the network • Predict the performance of the structure • Can find the effects on performance if different changes are done in the structure configuration • Bottleneck is not caused at MA

  39. Conclusion • Homogeneous : • Binary tree type structure is best • Number of nodes is proportionate to number of servers • Star graph type structure, when nodes are less and servers are more than 60 • Heterogeneous : • Find the bottleneck • Improve the throughput • Modelizing the DIET

  40. Future work • Calculate the throughput of structures with multi-client and multi-master agents. • Dynamic updating with the use of package GRAS • Timer addition into the tool to get real value for CORBA implementation of DIET • Check the LA and SeD as the cause of bottleneck • Combine scheduling and deployment to increase the performance • Validation of work by real deployment.

  41. <?xml version="1.0" standalone="yes"?> <launch> <masteragent name = "MA1"> <IP>193.253.175.223</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/ma1</binary> <localagent name = "LA1"> <IP>193.253.175.224</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/LA1</binary> <server name = "SeD1"> <IP>193.253.175.226</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/SeD1</binary> <service>all</service> </server> <server name = "SeD2"> <IP>193.253.175.227</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/SeD2</binary> <service>T:MatSUM</service> </server> </localagent> <localagent name = "LA2"> <IP>193.253.175.225</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/LA2</binary> <server name = "SeD3"> <IP>193.253.175.228</IP> <binary>/home/ckochhof/work/diet/src/dmat_manips/bin/SeD3</binary> <service>T:MatSUM</service> </server> </localagent> </masteragent> </launch> #!/usr/bin/perl -w use strict; use XML::DOM; my $parser = XML::DOM::Parser->new(); my $file = ‘launch.xml'; my $doc = $parser->parsefile ($file); package MasterAgent; sub MasterAgent::new { my $class = shift; my $self = {};$self->{type} = undef;$self->{name}= undef;$self->{IP}= undef; $self->{localAgents}= {};$self->{server} = {};bless $self,$class;$self; } sub MasterAgent::funtion{ my $self = shift; my $agt = shift; $self->{name}= $agt->getAttribute('name');; $self->{type} = "MasterAgent"; my @child = $agt->getChildNodes; for(my $i=0;$i<$#child;$i++){ if ($child[$i]->getNodeName == 'IP'){ $self->{IP} = $child[$i]->getData;} if ($child[$i]->getNodeName == 'localagent'){ my $local_name = $child[$i]->getData; my $child_pass = $child[$i]; my $obj_la_ma = new LocalAgent; $self->{localAgents}->{$local_name} = $obj_la_ma->funtion($child_pass,$self);} if ($child[$i]->getNodeName == 'server'){ my $server_name =$child[$i]->getData; my $child_pass = $child[$i]; my $obj_ser_ma = new Server; $self->{servers}->{server_name}= $obj_ser_ma->funtion($child_pass,$self);} } return $self; } traceLevel = 1 agentType = DIET_LOCAL_AGENT name = LA1 Parent name = MA1 fastUse = 1 ldapUse = 0 nwsUse = 1 Master Agent : . Name = MA1 . Binary = /home/ckochhof/work/diet/src/dmat_manips/bin/ma1 . IP = 193.253.175.223 . Local agent = LA1 LA2 Local Agent : . Name = LA1 . Parent agent= MA1 . Binary = /home/ckochhof/work/diet/src/dmat_manips/bin/LA1 . IP = 193.253.175.224 . servers = SeD1 SeD2 Server : . Name = SeD1 . Parent agent = LA1 . binary = /home/ckochhof/work/diet/src/dmat_manips/bin/SeD1 . IP = 193.253.175.226 . services = all Server : . Name = SeD2 . Parent agent = LA1 . Binary = /home/ckochhof/work/diet/src/dmat_manips/bin/SeD2 . IP = 193.253.175.227 . services = T:MatSUM Local Agent : . Name = LA2 . Parent agent= MA1 . Binary = /home/ckochhof/work/diet/src/dmat_manips/bin/LA2 . IP = 193.253.175.225 . servers = SeD3 Server : . Name = SeD3 . Parent agent = LA2 . Binary = /home/ckochhof/work/diet/src/dmat_manips/bin/SeD3 . IP = 193.253.175.228 . services = T:MatSUM LA1.cfg To Do • Script to launch the agents. • Simulations to check the parser • Practical implementation Automatic Deployment : first tool

  42. Plan • Réservation de ressource dans un ASP hiérarchique • Déploiement automatique • DIET en P2P • DIET vs NetSolve • VizDIET • Communications dans DIET • Une application pour GDS

  43. DIET en P2P • Existant • Multi-MA disponible avec connection en JXTA • Docs disponibles • Archive disponible : diet-0.7_beta-dev-jxta.tgz • TODO list • Evaluer les performances • Vérifier le respect des coding standard • Intégration au CVS DIET • Briser la contrainte 1 composant JXTApour 1 composant DIET • Algorithmes “intelligents” pour le parcours des MA ? MA MA MA Connexions JXTA MA MA A LA LA LA

  44. Plan • Réservation de ressource dans un ASP hiérarchique • Déploiement automatique • DIET en P2P • DIET vs NetSolve • VizDIET • Communications dans DIET • Une application pour GDS

  45. Scripts de déploiement. Utilisation de CVS pour mettre à jour les fichiers de configuration. gateway_vthd DIET vs NetSolve clients paraski agents + servers sunlabs clients ls

  46. DIET vs NetSolve

  47. DIET vs NetSolve • TODO List • Tests avec API asynchrone. • 'Multithreader' le client. • Amélioration des statistiques (indice de dispersion). • Amélioration des scripts de déploiements (fichiers de configuration DIET et omniORB). • Expliquer les résultats de NetSolve • Expliquer le problème des 40 clients de DIET • Tests sur les SPARC • Tests icluster2 ?

  48. Plan • Réservation de ressource dans un ASP hiérarchique • Déploiement automatique • DIET en P2P • DIET vs NetSolve • VizDIET • Communications dans DIET • Une application pour GDS

  49. Chaque LogManager collecte les infos de son agent et les envoie au LogCentral situé en dehors de la structure DIET. VizDiet : outil de visualisation en Java Interraction sur la plate-forme VizDIET

  50. VizDIET 1.0 • Intégration de LogService (LogManager/LogCentral) dansles agents DIET • Transfert de messages depuis l'agent par l'intermédiaire du LogManager • pas de stockage sur disque • Etude vizPerf vs vizDIET • Conclusion : vizPerf trop éloigné de la structure DIET