DIET: New Developments and Recent Results

Transcription

1 A. Amar 1, R. Bolze 1, A. Bouteiller 1, A. Chis 1, Y. Caniou 1, E. Caron 1, P.K. Chouhan 1, G.L. Mahec 2, H. Dail 1, B. Depardon 1, F. Desprez 1, J. S. Gay 1, A. Su 1 LIP Laboratory (UMR CNRS, ENS Lyon, INRIA, UCBL 5668) / GRAAL Project LPC / PCSV (CNRS / IN2P3 UBP Clermont-Ferrand) 29 August 2006 CoreGrid Workshop 1/ 30

2 Outline 1 Introduction / 30

3 Outline 1 Introduction / 30

4 Why we need tools like DIET? To access large computational power and/or storage capacity through the Internet. Large scale computing over heterogeneous platforms was coined by Ian Foster in the mid-90 s - Grid. Implementation of RPC programming model over the Grid - GridRPC. Existing implementations: NetSolve, Ninf, DIET, XtremWeb, OmniRPC, 4/ 30

5 GridRPC Paradigm Client Agent(s) S1 S2 S3 S4 5/ 30

6 GridRPC Paradigm Client Request Agent(s) S1 S2 S3 S4 5/ 30

7 GridRPC Paradigm Client S2 Agent(s) S1 S2 S3 S4 5/ 30

8 GridRPC Paradigm Client S2 Agent(s) A,B,C S1 S2 S3 S4 5/ 30

9 GridRPC Paradigm Client S2 Agent(s) S1 S2 Op(C,A,B) S3 S4 5/ 30

10 GridRPC Paradigm Client S2 Agent(s) Answer(C) S1 S2 S3 S4 5/ 30

11 Why DIET? Hierarchical architecture for an improved scalability. 6/ 30

12 Why DIET? Hierarchical architecture for an improved scalability. Client MA MA Master Agent MA MA MA MA Local Agent Server 6/ 30

13 Outline Introduction 1 Introduction / 30

14 DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Client Servers () : Perform actual computation for client 8/ 30

15 DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Client MA Servers () : Perform actual computation for client 8/ 30

16 DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Client MA Servers () : Perform actual computation for client 8/ 30

17 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

18 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

19 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

20 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

21 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

22 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA 8/ 30

23 Introduction DIET Components Client : An application that uses DIET to solve problems Master Agent (MA) : Receives requests from clients Collects computational abilities from servers and selects Returns the reference of the chosen server to the client Local Agents () : Act as transmitter Share the workload of scheduling Servers () : Perform actual computation for client Client MA MA Master Agent MA MA MA MA Local Agent Server 8/ 30

24 Collector of Resource Information (CoRI) CoRI-Easy: provides basic measurements of the environment CoRI Manager: manages the use of different collectors 9/ 30

25 Plug-in schedulers Applications vary in terms of performance demands Performance Estimation Vector (PEV) is used for scheduling s sends PEV to MA as a response to a request Information tag starts with EST TCOMP LOADAVG FREEMEM NBCPU. Explanation the predicted time to solve a problem CPU load average amount of free memory (Mb) number of available processors. 10/ 3

26 Verification of plug-in schedulers Six heterogeneous servers Sequential and independent requests Inter-arrival time for the request is 1 minute Compare two scheduler Round Robin scheduler CPU scheduler 11/ 3

27 Round Robin Scheduler 12/ 3

28 CPU Scheduler 13/ 3

29 Batch Scheduler Management System dependent NFS: copy the code? MPI: M, MPICH? Batch system dependent No homogeneity Scheduler behaviour Information about the internal scheduling process Monitoring and performance prediction Sed_seq SGE MA _parallel Frontal NFS Sed_batch GLUE LSF PBS Loadleveler 14/ 3

30 Parallel and batch submissions Performance prediction: SIMBATCH Batch, cluster, parallel tasks simulator plugin for SimGrid Goal: test new grid schedulers more realistically and performance prediction Implement FIFO, Conservative BackFilling, More problems Asynchronous, long term production jobs Performance prediction How to decide number of processors for application? If reservation available, how to compute deadline? Co-scheduling? Data and job migration? 15/ 3

31 Workflow Management Workflow is represented as DAG Use different heuristic methods to solve scheduling problems Extensibility to address multi-workflows submission and large grid platform Manage heterogeneity and variability of environment 16/ 3

32 DIET workflow engine Client MA Client 17/ 3

33 DIET workflow engine Client MA Client 17/ 3

34 DIET workflow engine MADAG Client MA Client 17/ 3

35 DIET workflow engine MADAG Client MA Client 17/ 3

36 DIET workflow engine MADAG Client MA Client 17/ 3

37 Mapping of DIET s components on available resources. 18/ 3

38 Mapping of DIET s components on available resources. Homogeneous resources MA 18/ 3

39 Mapping of DIET s components on available resources. Homogeneous resources MA 18/ 3

40 Mapping of DIET s components on available resources Completed requests agent distributed scheduler centralized scheduler 36,307 requests 22,867 requests Time (seconds) 18/ 3

41 Mapping of DIET s components on available resources. Homogeneous resources Complete d-ary Spanning tree E.Caron, P.K.Chouhan,H.Dail and F.Vivien. Automatic Middleware Deployment Planning for Clusters. RR LIP,October / 3

42 Mapping of DIET s components on available resources. Heterogeneous resources 19/ 3

43 Mapping of DIET s components on available resources. Heterogeneous resources Find the best broadcast tree on a general graph, which is known to be NP-complete. 19/ 3

44 Mapping of DIET s components on available resources. Heterogeneous resources 200 Star Balanced Automatic 150 Requests/second Number of clients Thesis Automatic Deployment for Application Service Provider Environments by P. K. Chouhan 19/ 3

45 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

46 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

47 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

48 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

49 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

50 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes 20/ 3

51 GoDIET: DIET deployment tool XML file as Input Generate configuration files Launches services (name service, logging services) Launches DIET elements Destroy deployed platform Remote cleanup of launched processes E.Caron, P.K.Chouhan and H.Dail GoDIET: A Deployment Tool for Distributed Middleware on Grid In IEEE, editor, EXPGRID workshop, in conjunction with HPDC-15. pages / 3

52 Failure Detection in DIET Failure detection Detection time: time between failure and definitive suspicion Accuracy: probability of the observer to be true at random time 21/ 3

53 Failure Detection in DIET Failure detection Detection time: time between failure and definitive suspicion Accuracy: probability of the observer to be true at random time Chandra, Toueg and Aguilera Failure Detector 21/ 3

54 Failure Detection in DIET Failure detection Detection time: time between failure and definitive suspicion Accuracy: probability of the observer to be true at random time Chandra, Toueg and Aguilera Failure Detector p (observed) U Failure Hi Hi+1 Hi+2 Hi+3 Hi+4 Late q (observer) Fi 1 Freshness point Expected Arrival date Fi Fi+1 Fi+2 Fi+3 Fi+4 Fi+5 EAi V(D) TM TDu=U+E(D)+V(D) Suspect (Considering worst case Hi+4 not late) 21/ 3

55 Architecture Recovery MA Problem: Keep architecture connected Solution: Keep ancestors list tolerates up to f-1 simultaneous failures 22/ 3

56 Architecture Recovery MA Problem: Keep architecture connected Solution: Keep ancestors list tolerates up to f-1 simultaneous failures 22/ 3

57 Architecture Recovery MA Problem: Keep architecture connected Solution: Keep ancestors list tolerates up to f-1 simultaneous failures 22/ 3

58 Architecture Recovery MA Problem: Keep architecture connected Solution: Keep ancestors list tolerates up to f-1 simultaneous failures 22/ 3

59 Architecture Recovery MA Problem: Keep architecture connected Solution: Keep ancestors list tolerates up to f-1 simultaneous failures 22/ 3

60 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call JuxMeM JuxMem manages data persistence across failures by replicating it on nodes 23/ 3

61 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call JuxMeM JuxMem manages data persistence across failures by replicating it on nodes 23/ 3

62 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call Failure detection JuxMeM JuxMem manages data persistence across failures by replicating it on nodes 23/ 3

63 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call Failure detection Resubmit JuxMeM JuxMem manages data persistence across failures by replicating it on nodes 23/ 3

64 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call Failure detection Resubmit Ckpt JuxMeM JuxMem manages data persistence across failures by replicating it on nodes 23/ 3

65 Application Recovery Replication and checkpointing Replication divides the available computing by f Checkpointing takes periodic snapshot of process state, saving it to another place Client RPC call Failure detection Resubmit Ckpt JuxMeM JuxMem manages data persistence across failures by replicating it on nodes Retrieve 23/ 3

66 VizDIET: Tool Current view of the DIET platform Communication between agents State of s Scalability Available services Data persistency Name information CPU, memory and network load R.Bolze, E.Caron, F.Desprez, G.Hoesch, and C.Pontvieux. A Monitoring and Visualization Tool and Its Applications. Computational Science and Its Applications-ICCSA 2006, volume 3984 of LNCS, pages / 3

67 Screen-shot of VizDIET 25/ 3

68 Outline 1 Introduction / 3

69 Applications BST application using DIET Basic Local Alignment Search Tool. Compare biological sequences such as nucleotides sequences N-sequences versus one database Multi-request files are partitioned intro several smaller requests DIET to analyse Cosmological Results Study large scale structure and galaxy formation RAMSES collects raw data Galices analysis the data 27/ 3

70 Outline Introduction Conclusion Future Work 1 Introduction Conclusion Future Work 28/ 3

71 Conclusion Introduction Conclusion Future Work Scalable, open-source, and multi-application platform Concentration on several issues performance evaluation (CoRI, FAST ) scheduling (plugin schedulers, workflow management) deployment (planning and tool GoDIET) data management and replication (JuxMem) fault toleration (architecture and application) monitoring (LogService and VizDIET) Large scale validation on the Grid 5000 platform / 3

72 Future work Introduction Conclusion Future Work Need of implementation and validation of algorithms from the scheduling literature in real-life middleware infrastructures? Still room for fundamental research on algorithms Some problems waiting for solution Finding accurate models Large scale validation of algorithms (simulators, real grids?) Take a look at other applications 30/ 3