Dynamische Erweiterung lokaler Batch Systeme durch Cloud Resourcen

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Jasmin Brinkerhoff
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1 Dynamische Erweiterung lokaler Batch Systeme durch Cloud Resourcen, Thomas Hauth, Armin Scheurer, Oliver Oberst, Günter Quast, Marcel Kunze INSTITUT FÜR EXPERIMENTELLE KERNPHYSIK (EKP) FAKULTÄT FÜR PHYSIK KIT Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft

2 What is Cloud Computing? Software as a Service Increasing Abstraction (SaaS) 2 the Application Platform as a Service (PaaS) the Runtime Environment Infrastructure as a Service (IaaS) the Virtual Machine

3 Infrastructure as a Service (IaaS) User supplied Machine Image(s) Cloud Controller boot / shutdown.. SL 5 Win2k3.. Debian 5 Node Controller Virtualized Ubuntu Node Controller Virtualized Virtualized Node Controller pause / migrate <any OS>.. <any OS> There are Public Clouds (commercial, e.g. Amazon Web Services) and Private Clouds (non-commercial, e.g. Cloud installations at Steinbuch Centre for Computing (SCC), KIT). Most Public Cloud Provider offer a pay-as-you-go model used. Network bandwidth is still expensive for I/O intense HEP data analyses. Only pay for services actually Public Clouds are currently not an affordable solution A Private Cloud Interface can be set on top of already existing machines. 3

4 Why do we need to extend to the Cloud? Typical scenario during HEP analyses: During periods of high demand (before conferences, finishing publications, etc.) local clusters are often completely occupied. Buying a new Cluster is expensive and most of the time it will be underutilized. Solution: Cloud burst. Elasticity of Cloud Providers allows to dynamically add / remove Cloud resources and thus to handle peak loads. Constraints in Hard- and Software setup (e.g. Scientific Linux) prevent usage of many shared resources by HEP community. Through virtualization these resources can be made available. Installation and maintenance of HEP and Grid specific tools is a time consuming task Management can be simplified by using prepacked images (e.g. CernVM and CernVMFS) 4

5 Cloud SCC OpenNebula Cloud: 2x Xeon E5520 (Nehalem) 2.27 GHz 60 nodes, each with 8 cores 500 cores 36 GB RAM 2 TB HD Eucalyptus Cloud 2x Xeon 5400 series 2,33 GHz 6 nodes, each with 8 cores 50 cores 16 GB RAM 100 GB HD 5

6 The ROCED Concept Responsive on-demand Cloud Enabled Deployment (ROCED) Scheduler for integrating cloud resources into local Batch Systems Written in Python 2.6 Features a modular design: Every part is implemented as an Adapter. New Adapters can be added easily. 6

VM 3) add node execd Virtual Machine 1) Passive monitoring of one or more batch server queues.

7 ROCED Workflow (1) Batch Server cloud.q 1) monitor queue Cloud Provider 5) remove & shutdown Requirement networking and virtualization ROCED Integration 4) execute job Site 2) boot VM 3) add node execd Virtual Machine 1) Passive monitoring of one or more batch server queues. 2) Depending on queue size ROCED decides how many machines are needed. 3) The needed virtual machines are started by ROCED. 4) After startup, the new hosts are added, and become available for job processing. 5) After job execution, the VMs are automaticaly removed from the batch system and shut down. 7

8 ROCED Workflow (2) number of running cloud instances jobs in queue scaled by 0.02 number of registered batch nodes Stress test of ROCED (here with Amazon/Torque) 8

9 Setup ROCED runs on the same machine as the GridEngine. GridEngine communicates with its nodes and users via TCP. Commands to the OpenNebula host are sent via XMLRPC call. The Communication between the Cloud nodes, ROCED and the OpenNebula Server are done via SSH. No modifications to the firewall (besides VPN tunnel) needed. 9

10 Outlook ROCED is currently running in parallel to EKP production cluster. Due to a dedicated cloud queue, selected users can submit jobs to the cloud instances. Projects for the near future: Evaluate performance on MC production (CernVM + CernVMFS) on the cloud and possibility to use public clouds for this purpose as well. Evaluate scalability & performance using I/O intense HEP jobs on private clouds with fast network access to read and write larger amounts of data. Include multiple Cloud Sites on the same batch system. Optimize already existing cost algorithms for automatic deployment on cheapest Cloud Provider. Write documentation. ROCED has still prototype status release is under discussion. Thank you for your attention! 10

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