DIET Overview and some recent work A middleware for the large scale de ployment of applications over the Grid

1. DIET Overview and some recent workA middleware for the large scale deployment of applications over the Grid Frédéric Desprez LIP ENS Lyon / INRIA GRAAL Research Team Join work withN. Bard, R. Bolze, B. Depardon, Y Caniou, E. Caron, B. Depardon, D. Loureiro, G. Le Mahec, A. Muresan, V. Pichon, … Distributed Interactive Engineering Toolbox

2. Introduction • Transparency and simplicity represent the holy grail for Grids (maybe even before performance) ! • Scheduling tunability to take into account the characteristics of specific application classes • Several applications ready (and not only number crunching ones !) • Many incarnations of the Grid (metacomputing, cluster computing, global computing, peer-to-peer systems, Web Services, …) • Many research projects around the world • Significant technology base • Do not forget good ol’ time research on scheduling and distributed systems ! • Most scheduling problems are very difficult to solve even in their simplistic form … • … but simple solutions often lead to better performance results in real life

3. Introduction, cont • One long term idea for the gridoffering (or renting) computational power and/or storage through the Internet • Very high potential • Need of Problem Solving and Application Service Provider Environments • More performance, storage capacity • Installation difficulty for some libraries and applications • Some libraries or codes need to stay where they have been developed • Some data need to stay in place for security reasons • Using computational servers through a simple interface

4. RPC and Grid-Computing: GridRPC • One simple idea • Implementing the RPC programming model over the grid • Using resources accessible through the network • Mixed parallelism model (data-parallel model at the server level and task parallelism between the servers) • Features needed • Load-balancing (resource localization and performance evaluation, scheduling), • IDL, • Data and replica management, • Security, • Fault-tolerance, • Interoperability with other systems, • … • Design of a standard interface • within the OGF (GridRPC and SAGA WG) • Both computation requests and data management • Existing implementations: NetSolve, Ninf, DIET, OmniRPC

5. RPC and Grid Computing: Grid RPC Request S2 ! A, B, C Answer (C) AGENT(s) Client Op(C, A, B) S4 S3 S1 S2

6. DIET’s 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 • FAST for performance evaluation, • LogService for monitoring, • VizDIET for the visualization, • GoDIET for the deployment • Several applications in different fields (simulation, bioinformatics, …) • Release 2.2 available on the web • ACI Grid ASP, TLSE, ACI MD GDS, RNTL GASP, ANR LEGO, Gwendia, COOP, Grid’5000 DIET Dashboard http://graal.ens-lyon.fr/DIET/

7. DIET Architecture MA MA JXTA MA MA LA Client Master Agent MA Server front end LA LA LA Local Agent

8. Client and server interface • Client side • So easy … • Multi-interface (C, C++, Fortran, Java, Scilab, Web, etc.) • Grid-RPC compliant • Server side • Install and submit new server to agent (LA) • Problem and parameter description • Client IDL transfer from server • Dynamic services • new service • new version • security update • outdated service • Etc. • Grid-RPC compliant

9. Data/replica management Client A Server 1 B F B B X Server 2 G Y Client • Two needs • Keep the data in place to reduce the overhead of communications between clients and servers • Replicate data whenever possible • Three approaches for DIET • DTM (LIFC, Besançon) • Hierarchy similar to the DIET’s one • Distributed data manager • Redistribution between servers • JuxMem (Paris, Rennes) • P2P data cache • DAGDA (IN2P3, Clermont-Ferrand) • Joining task scheduling and data management • Work done within the GridRPC Working Group (OGF) • Relations with workflow management

10. DAGDA Data Arrangement for Grid and Distributed Applications • A new data manager for the DIET middleware providing • Explicit data replication: Using the API. • Implicit data replication: The data are replicated on the selected SeDs. • Direct data get/put through the API. • Automatic data management: Using a selected data replacement algorithm when necessary. • LRU: The Least Recently Used data is deleted. • LFU: The Least Frequently Used data is deleted. • FIFO: The « oldest » data is deleted. • Transfer optimization by selecting the more convenient source. • Using statistics on previous transfers. • Storage resources usage management. • The space reserved for the data is configured by the user. • Data status backup/restoration. • Allowing to stop and restart DIET, saving the data status on each node.

11. DAGDA • Transfer model • Uses the pull model. • The data are sent independently of the service call. • The data can be sent in several parts. 1: The client send a request for a service. 2: DIET selects some SeDs according to the chosen scheduler. 3: The client sends its request to the SeD. 4: The SeD download the data from the client and/or from other nodes of DIET. 5: The SeD performs the call. 6: The persistent data are updated.

12. DAGDA • DAGDA architecture • Each data is associated to one unique identifier • DAGDA control the disk and memory space limits. If necessary, it uses a data replacement algorithm. • The CORBA interface is used to communicate between the DAGDA nodes. • The users can access to the data and perform replications using the API.

13. DIET Scheduling • Collector of Resource Information (CoRI) • Interface to gather performance information • Functional requirements • Set of basic metrics • One single access interface • Non-functional requirements • Extensibility • Accuracy and latency • Non-Intrusiveness • Currently 2 modules available • CoRI Easy • Fast • Extension possibilities: Ganglia, NagiosR-GMA, Hawkeye, INCA, MDS, … CoRI Manager Other Collectors like Ganglia CoRI-Easy Collector FAST Collector FAST Software

14. FAST: Fast Agent’s System Timer Client application FAST FAST API Static DataAcquisition Dynamic DataAcquisition LDAP BDB NWS ... Low level software • Performance evaluation of platform enables to find an efficient server (redistribution and computation costs) without testing every configuration  performance database for the scheduler • Based on NWS (Network Weather Service) • Computer • Memory amount • CPU Speed • Batch system • Computer • Status (up or down) • Load • Memory • Batch queue status • Network • Bandwidths • Latencies • Topology • Protocols • Network • Bandwidths • Latencies • Computation • Feasibility • Execution time on a given architecture

15. Plugin Schedulers • “First” version of DIET performance management • Each SeD answers a profile (COMP_TIME, COMM_TIME, TOTAL_TIME, AVAILABLE_MEMORY) for each request • Profile is filled by FAST • Local Agents sort the results by execution time and send them back up to the Master Agent • Limitations • Limited availability of FAST/NWS • Hard to install and configure • Priority of FAST-enabled servers • Extension hard to handle • Non-standard application- and platform-specific performance measures • Firewall problems with some performance evaluation tools • No use of integrated performance estimator (i.e. Ganglia)

16. DIET Plug-in Schedulers • 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.

17. Workflow Management (ANR Gwendia) • Workflow representation • Direct Acyclic Graph (DAG) • Each vertex is a task • Each directed edge represents communication between tasks • Goals • Build and execute workflows • Use different heuristics to solve scheduling problems • Extensibility to address multi-workflows submission and large grid platform • Manage heterogeneity and variability of environment

18. Architecture with MA DAG • Specific agent for workflow management (MA DAG) • Two modes: • MA DAG defines a complete scheduling of the workflow (ordering and mapping) • MA DAG defines only an ordering for the workflow execution, the mapping is done in the next step by the client which pass by the Master Agent to find the server where execute the workflow services.

19. Workflow Designer • Applications viewed as services within DIET • Compose services to get a complete application workflow in a drag’&’drop fashion

20. DIET: Batch System interface • A parallel world • Grid resources are parallel (parallel machines or clusters of compute nodes) • Applications/services can be parallel • Problem • many types of Batch Systems exist, each having its own behavior and user interface • Solution • Use a layer of intermediary meta variables • Use an abstract BatchSystem factory

21. Grid’5000 Grid’5000 • Building a nation wide experimental platform for • Grid & P2P researches (like a particle accelerator for the computer scientists) • 9 geographically distributed sites hosting clusters with 256 CPUs to 1K CPUs) • All sites are connected by RENATER (French Res. and Edu. Net.) • RENATER hosts probes to trace network load conditions • Design and develop a system/middleware environment for safely test and repeat experiments • 2) Use the platform for Grid experiments in real life conditions • Address critical issues of Grid system/middleware: • Programming, Scalability, Fault Tolerance, Scheduling • Address critical issues of Grid Networking • High performance transport protocols,Qos • Port and test applications • Investigate original mechanisms • P2P resources discovery, desktop Grids • 4 main features: • A high security for Grid’5000 and the Internet, despite the deep reconfiguration feature • A software infrastructure allowing users to access Grid’5000 from any Grid’5000 site and have home dir in every site • A reservation/scheduling tools allowing users to select node sets and schedule experiments • A user toolkit to reconfigure the nodes and monitor experiments

22. Goals and Protocol of the Experiment Grid’5000 • Orsay : 40 s • Lyon : 38 s • Toulouse : 33 s • Sophia : 40 s • Parasol : 33 s • Bordeaux : 33 s • Paraci : 11 s • Lilles : 34 s • Paravent : 9 s • Validation of the DIET architecture at large scale over different administrative domains • Protocol • DIET deployment over a maximum of processors • Large number of clients • Comparison of the DIET execution times with average local execution times • 1 MA, 8 LA, 540 SeDs • 2 requests/SeD • 1120 clients on 140 machines • DGEMM requests (2000x2000 matrices) • Simple round-robin scheduling usingtime_since_last_solve

23. Grid’5000 Results

24. Deployment example: Décrypthon platform Master Agent Orsay Decrypthon1 Web Interface Project Users Sed = Server Daemon, installed on any server running Loadleveler. Note that we can define rescue SeD. MA = master agent, coordinates Jobs. We can define rescue or multiple Master Agent. WN = worker node DIET Décrypthon LILLE Orsay Decrypthon2 SeD LoadLeveler SeD LoadLeveler BORDEAUX JUSSIEU SeD LoadLeveler SeD LoadLeveler ORSAY CRIHAN DB2 IBM WII Data manager Interface Lyon BD AFM Cliniques

25. Eucalyptus – the Open Source Cloud • Eucalyptus is: • A research project of a team from the University of California, Santa Barbara • An Open Source Project • An IaaS Cloud Platform • Base principles • A collection of Web Services on each node • Virtualization to host user images (Xen technology) • Virtual networks to provide security • Implement the Amazon EC2 interface • Systems / Tools built for EC2 are usable • “Turing test” for Eucalyptus • Uses commonly-known and available Linux technologies • http://open.eucalyptus.com/

26. Eucalyptus platform

27. DIETCloud architecture NC MA CC + NC = LA LA CLC NC SeD SeD SeD CC DIET Eucalyptus NC

28. DIETCloud Architecture • Several solutions that differ by how much of the architectures of both systems overlap or are included one in the other • DIET is completely included in Eucalyptus • DIET is completely outside of Eucalyptus • …and all the possibilities in between

29. DIET completely included in Eucalyptus Eucalyptus CLC CC CC NC SeD NC LA NC MA DIET The DIET platform is virtualized inside Eucalyptus Very flexible and scalable as DIET nodes can be launched when needed Scheduling is more complex

30. DIET completely outside of Eucalyptus MA LA Eucalyptus CLC SeD SeD DIET CC NC NC SeD requests resources to Eucalyptus SeD works directly with the Virtual Machines Useful when Eucalyptus is a 3-rd party resource

31. 1. Obtain the requested virtual machines by a SOAP call to Eucalyptus • 2. Execute the service on the instantiated virtual machines, bypassing the Eucalyptus controllers Implemented Architecture • 3. Terminating the virtual machines by a SOAP call to Eucalyptus • We have considered the architecture taking benefits of DIET design • when DIET is completely outside of Eucalyptus • Eucalyptus is treated as a new Batch System • Easy and natural way of use in DIET • DIET is designed to easily add a new batch scheduler • Provide a new implementation for the BatchSystem abstract class • Handling of a service call is done in three steps:

32. DIETCloud: a new DIET architecture Eucalyptus Batch System Eucalyptus Amazon EC2

33. Some thoughs about DIET and Clouds • The door to using Cloud platforms through DIET has been opened • The first DIET Cloud architecture was designed • The current work serves as a proof of concept of using the DIET Grid-RPC middleware on top of the Eucalyptus Cloud system to demonstrate general purpose computing using Cloud platforms • Possible ways of connecting the two architectures have been studied • Several issues still remain to be solved • Instance startup time needs to be taken into account • A new scheduling strategy is needed for more complex architectures • The performance of such a system needs to be measured

34. Conclusions and future work • GridRPC • Interesting approach for several applications • Simple, flexible, and efficient • Many interesting research issues (scheduling, data management, resource discovery and reservation, deployment, fault-tolerance, …) • DIET • Scalable, open-source, and multi-application platform • Concentration on several issues like resource discovery, scheduling (distributed scheduling and plugin schedulers), deployment (GoDIET and GRUDU), performance evaluation (CoRI), monitoring (LogService and VizDIET), data management and replication (DTM, JuxMem and DAGDA) • Large scale validation on the Grid5000 platform • A middleware designed and tunable for an application given • And now … • Client/server DIET for Décrypthon applications • Deployment and validation on execution • Duplicate and check requests from UD • Validation using SeD_batch (Loadleveler version) • Data management optimization • Scheduling optimization • More information and statistics for users • Fault tolerance mechanisms http://graal.ens-lyon.fr/DIET http://www.grid5000.org/

35. Research Topics • Scheduling • Distributed scheduling • Software platform deployment with or without dynamic connections between components • Plug-in schedulers • Multiple (parallel) workflows scheduling • Links with batch schedulers • Many tasks scheduling • Data-management • Scheduling of computation requests and links with data-management • Replication, data prefetching • Workflow scheduling • Performance evaluation • Application modeling • Dynamic information about the platform (network, clusters)

36. Questions ? http://graal.ens-lyon.fr/DIET