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1 Identifier: Date: Activity: Authors: Status: Link: Cloud-pilot.doc SA1 Marcus Hardt, Marcin Plociennik, Ahmad Hammad, Bartek Palak E U F O R I A J O I N T A C T I O N ( S A 1, J R A 3 ) F I N A L R E P O R T ON T H E C L O U D P I L OT Abstract: This document describes the work that was done based following recommendations 3 and 4 of the 2010 review. Both, a commercial and a scientific cloud system were tried out. This report provides the lessons learnd.

2 Document Log Issue Date Comment Author 0.1 7/12/2010 Initial version created Marcus Hardt 0.2 8/12/2010 Extensions Bartek Palak 0.3 9/12/2010 Extensions Marcus Hardt FP DOCUMENT TYPE 2/11

3 C O N T E N T 1 Goal 4 2 Evaluation of cloud infrastructures 4 3 Architecture Virtual machine considerations: Usage scenario Monitoring Service Integration with Kepler 7 4 Cost Evaluation Reference use-case Amazon prices Theoretical cost model Summary of cost 10 5 Future Plans 10 FP DOCUMENT TYPE 3/11

4 1 Goal This cloud pilot project is based on the recommendations 3 and 4 of the secondyear project review, to try out a commercial cloud system if resources are available and to provide a note on the lessons learned. For this, funding was shifted from EPCC and Chalmers to PSNC and KIT, who all actively participated in the cloud-pilot project, led by SA1. The goal of the pilot project is understood to utilise an infrastructure provider (IAAS) to use this infrastructure for computational tasks. Higher level cloud services (e.g. PAAS, SAAS) where not in the scope of this pilotproject. Throughout the project, the expectations of the EUFORIA users became clearer to us. Some users expected the Grid to behave like HPC systems, especially when such systems were used before. This expectation was characterised by questions like Which compiler is installed on the grid?, which cannot be answered on heterogeneous distributed computing infrastructures. Users with specific requirements on preinstalled software (compilers, libraries, ) cannot easily be familiarised with the heterogeneous distributed nature of the grid. The key difference for our users is that the cloud infrastructure can be tailored to the specific needs of every individual user, whereas the grid infrastructure has to provide one platform suitable for all users. Furthermore, on the cloud we run clones of identical machines, whereas every grid site within EUFORIA lies within a different administrative domain and often in a different country, leading to differently configured compute notes. This increases the amount of time spent with the grid before it can be used productively and thereby creates a bad user experience. For this cloud pilot we decided to integrate the solution into the Kepler workflow environment. This is not required to use the cloud, but provides from the users point of view a painless integration into the experienced tool with minimal changes to existing workflows. Furthermore, this provides an ideal way to hide the technicalities of virtual machine handling and code placement from the user. The experience gathered with Kepler throughout EUFORIA was essential to providing the results as quick as we did. 2 Evaluation of cloud infrastructures We understood the recommendation to be targeted at trying out an Infrastructure as a Service (IaaS) provider, because platform and software (PaaS and SaaS) are likely too high-level for being integrated on the same level as grid and HPC. Choosing the cloud provider was not very easy, as many commercial cloud providers exist. Typically each provider requires the user to learn and user their specific interface. This might either be a web GUI but often application programmers interfaces (APIs) are provided. This leads to the so called vendor lock-in, which describes the situation that the user has implemented his access to the provider so specific that he cannot change the provider, without reinvesting in his interfaces. To avoid this, all vendors would have to use the same, standardised interface. Among others, the UK based ElasticHosts (EH) was actively pursuing the standardisation of the open, OGF-standardised OCCI protocol. However, we chose the US based IaaS provider Amazon Web Services (Amazon). Amazon is the biggest and most present IAAS provider. Thus, it serves as a defacto standard for cloud computing interfaces. Futhermore, any serious comparison has to be comparable with Amazon. FP DOCUMENT TYPE 4/11

5 Based on the previous evaluation of KIT of the Amazon-compatible OpenSource reimplementation Eucalyptus and also OpenNEbula, we have decided to use the Amazon interface of OpenNEbula as the reference development platform, which KIT provided. ONE is the open-source part of the EC funded RESERVOIR project. It provides three interfaces for IAAS: ONE-native, Amazon and OCCI. Using the Amazon interface, our implementation could directly be used for the commercial cloud provider. Furthermore, ONE was the ideal candidate for development, because only little money had to be spent on AWS resources during development 3 Architecture This section highlights the architecture considerations made for the cloud-pilot. They were made with respect to the limited resource availability and the limited amount of time left. We have therefore focussed on basic functionality and not on extra-features. We are convinced that the architecture is reasonably solid and extensible. However, this system is not being encouraged for production use. The Main components are: Kepler workflow orchestrator: This runs a user s workflow on a local workstation. Extensions were made by PSNC throughout EUFORIA to submit jobs to external computing resources, such as grid or HPC. For this, specific actors are provided. The coal of the cloud pilot project was provide an actor that executes a job on cloud resources. File storage: This is the place where input files and job outcome is stored. Currently a grid storage element is used for this, which requires to use the grid authentication from within a cloud VM to access storage. Cloud storage should be considered. Virtual Machines: VMs are copies of a typical glite WorkerNode. They are instantiated on Amazon or OpenNEbula resources. Monitoring Server: Keeps information about submitted jobs and their statuses. 3.1 Virtual machine considerations: IAAS provides an interface to start a virtual machine (VM) not more. We had to make the choice on how to run the users codes inside the virtual machines. Two possible options exist. The obvious solution is to follow the grid paradigm and to start a cluster of VMs. This cluster can be used with the existing grid interfaces to submit the jobs. The problem with this would be that an additional monitoring for the existing VMs would have to be implemented. We therefore chose the second option, to start a VM in such a way that it does not require further instructions during runtime. We pass a set of parameters to the virtual machine during start-up, including The code to run Parameters From where to get input files Where to store output We call this procedure contextualisation because it puts the generic clone of the master VM into the context in which it runs. The advantages are that no knowledge about the VM is required during runtime and that no network connections need to be made to it for successful execution. For monitoring the VM during development, we have installed a webservice which provides monitoring information when needed. FP DOCUMENT TYPE 5/11

6 The user can start many VMs with different parameters to run his computations. Currently only serial jobs for use cases like parameter scans are supported. For the development of the VM, we have created a master-vm, which runs constantly on the ONE cluster at KIT. This machine is provided for developers and code-owners to interactively log-in so that the EUFORIA developments and the users codes can be deployed and tested. When finished, the master VM holds an installation of the codes, libraries and tools required by the users. This setup enables us to start identical copies of the master VM on the ONE cluster at KIT and at AWS using the same interface, only using different credentials. Currently only one master VM is supported, on which all codes need to be installed. It is easily possible to add several master VMs, each of which can then be custom tailored to the requirements of the specific code. This would be required to utilise Infiniband-enabled cloud clusters with MPI, which are available at commercial providers. This was out of scope for the pilot project, but will be pursued in the future. At the end of a computation, after the code has stored the output at a specified output location, the VM is shut down. 3.2 Usage scenario The diagram presented below shows the flow of control and data between the main system components during the execution of a typical usage scenario: from specification of application input data and job submission to obtaining job outcome. Fig. 1 Usage scenario. 1. User starts a Kepler workflow on the local workstation. 2. Kepler actor uploads job input files to file storage. 3. Request for VM creation is sent by Kepler actor to the Monitoring Service. 4. Monitoring Service passes a request for VM creation to cloud. 5. Kepler actor starts querying Monitoring Service to track job status FP DOCUMENT TYPE 6/11

7 6. Virtual Machine is instantiated 7. Pre-installed scripts set up the job environment based on contextualization data passed while the VM was starting. 8. VM starts updating job status on Monitoring Service 9. VM downloads application input data 10. Job performs its execution 11. Job output is uploaded to file storage 12. VM calls Monitoring Service passing KILL ME signal 13. Monitoring Service shuts down Virtual Machine 14. Kepler actor downloads job outcome from file storage to predefined directory on local file system. 3.3 Monitoring Service The Monitoring Service is a simple web-service that stores and provides information about jobs, including current job statuses. The service intermediates between Kepler actors which run locally on the user s workstation and the VMs on the cloud infrastructures. This additional link in our call chain is necessary to work around several problems: Often, scientific compute providers block some ports by firewalls. In this case the Kepler actor cannot communicate with the VM. The VM, however, can still initiate outgoing connections. All monitoring information are pushed by VM, so communication is possible regardless of very restrictive rules defined on firewalls blocking access to cloud infrastructure. We do not have to keep a list of running cloud instances. Existing machines publish information about their existence. However, for increased robustness we keep such a list. Storing information about jobs and their statuses in a simple data base allows to shut-down VM immediately after job ends or failed. This releases resources (limited by some cloud providers) and reduces costs (running a VM on commercial cloud costs money!). 3.4 Integration with Kepler The Kepler 1 workflow orchestrator provides users with an efficient and user-friendly java based interface and workflow engine. This platform has been chosen as the main interface to project resources in Euforia. Within the cloud-pilot project, Kepler provides the framework for the creation of workflows that combine developed components in a coherent system. A set of Kepler actors allows the users to define and run their jobs on available cloud resources. To speed up the development process, some existing actors are re-used for user authentication, upload of input files and download of job outcome from file storage. Other actors, specific for cloud infrastructure, were implemented from scratch (this set of newly created components incorporates actors for handling job submission and job monitoring). 1 McPhillips, T. Bowers, S. Zinn, D. Ludaescher, B.: Scientific workflow design for mere mortals. Future Generation Computer Systems, Vol 25, 2008, pp FP DOCUMENT TYPE 7/11

8 Fig. 2 Kepler workflow Kepler hides the complexity of underlying environment, so the user can have a feeling that job are running locally. To start a particular job the user just specifies application parameters and input files (that could reside on local file system). After launching the workflow, application input is uploaded automatically. The user could track a job, watching job monitoring information displayed by Kepler. When workflow ends its execution, job outcome could be found in predefined local folder on user s workstation. 4 Cost Evaluation We wanted to get an understanding for the cost that could sum up, when using a commercial provider. For this we have defined a use case and calculate how much this would cost, when running at Amazon (AWS). We compare these prices with a theoretical model to get a good estimate for the cost and to better understand the amazon prices. 4.1 Reference use-case To keep the comparison reasonably simple, we decided to define a set of metrics and to compare these costs with available sources. We consider one job of a parameter scan use-case. These serial jobs are started very often, their output is small. The output is stored for some time until it can be retrieved and deleted. Number of Jobs 1000 Runtime per job 6 h, 1 CPU, (dual core) Output per job 100 MB Storage time of the output 6 months FP DOCUMENT TYPE 8/11

9 4.2 Amazon prices We use official pricing of the Amazon webpage 2. The Amazon Large Instance type costs $0.38 per hour and provides 2 CPU cores with 7.5 GB and 850 GB disk storage. The I/O performance is not specified, but labelled high (which is the second slowest). External storage (Amazon S3) required for storing results costs additional $0.09 per GB month and data transfer amounts to $0.15 per GB. Please note that we have simplified these figures, which comprise some initial free volumes, e.g. the first GB of data transfer is for free. This summary can be found in the table below (1 USD = 0,75 EUR): Amazon CPU Large instance type per hour Total 0,29 Disk Disk per GB month 0,07 Net Data transfer per GB 0, Theoretical cost model To get a better understanding on how the background of the pricing, we have built a model to understand the pricing for CPU and disk. For networking we simply adopted the Amazon pricing. For CPU we assume a price per core of 2000 EUR, relating to 4000 EUR for a dualcore machine, which is reasonable for a well performing large RAM configuration. We assume that one administrator can administer 1000 such machines, which is reasonable in a large datacentre with roughly 5000 machines. Regarding the electricity the estimate is based on a price of 0.12 euro/kwh resulting in 1 MEUR per year for electricity. The model assumes 2 MEUR`, because the net-price KIT pays cannot be assumed in general. Furthermore, the cost for electricity comprises cost for disk networking and CPUs, we simplified the calculation by assuming the power is used by only cores, therefore accepting a larger value for CPU cost. The same hold for the building which hosts the infrastructure. We calculate the building price only as a share of the CPUs and not the disks or network equipment. The assumption is that a building for 10 MEUR can host cores and all network and storage equipment. The building only lasts for 10 years, therefore again accepting a larger value for our CPU hours. The storage figure is based on the price of a disk as on the internet (1TB for 100 EUR) and the assumption that one admin is required for 200TB. This assumption is again based on the KIT data centre, where less than 10 people administer more than 8 Petabyte. Since network cost is difficult to estimate, we use the same cost that Amazon uses. Model EUR Per unit Total CPU Price per core 2.000,00 3 Years lifetime ,00 0,0761 Admin manpower per hour: 38,75 1 FTE 2 and FP DOCUMENT TYPE 9/11

10 Cores per admin 2.000,00 0,0194 Electricity ,00 For Cores 0,0228 includes disk EUR/CPU h Real estate ,00 For Cores 0,0114 Disk Disk per GB 0,10 and 10 years EUR/CPU h 0,13 1 Year lifetime 12,00 0,0083 Admin manpower per month: 6.200,00 1 FTE GB per admin ,00 EUR / GB month 0,04 Net Data transfer 0,11 Same as Amazon 0,1125 0, Summary of cost Summing up, we can see that Amazon is not really cheap. The theoretical model provides the low limit of the cost that is to be expected. Amazon Model Use-case multiplier Total AWS Total Model EUR per CPU hour 0,2850 0, ,00 778,35 EUR per GB month 0,0675 0, ,00 236,00 EUR GB transfer 0,1125 0, , , , ,35 During the research for the pricing we found one potentially interesting, but totally different figure for quantifying the value of computation. It uses the amount of generated papers per energy required to compute the results. NESRC in USA claims to have achieved 450 Publications per MW year in 2009 on their HPC infrastructure. We are not aware of any comparable figure in this metric, but believe this is ultimately correct. 5 Future Plans Given an adequate level of interest in this work, we suggest several improvements to the current prototype. Firstly the current storage concept is not fully satisfactory. It relies on grid technologies, which provide nice features, but are lacking stability. By adding support for cloud storage concepts, such as Amazon-S3 or the OpenSource reimplementation Walrus, it would be the choice of the user, to use one or the other. In order to transparently bridge the different authentication mechanisms required in cloud, grid and HPC, we suggest implementing an Authentication Proxy webservice. On behalf of the user, a job could use the provided credentials to translate them from one system to another. To further improve the user-experience one should exploit one of the key features of the cloud, which is given by the fact that individual VMs can be created for each FP DOCUMENT TYPE 10/11

11 and every required task. The current implementation does not allow this on the user friendly level we think it should be. Furthermore, assistance has to be given to the user to manage his VMs. Currently an experienced administrator is required to clone the Master Worker so that it can transparently be used at Amazon. This is also true for the management of the codes. Currently a new version of the Master Worker has to be created in order to update the installed code. This could be improved by supporting a distributed shared filesystem, or anything that behaves similar. FP DOCUMENT TYPE 11/11

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