“Cloud bursting” on SZTAKI Cloud Attila Csaba Marosi Cloud Computing Research Group MTA SZTAKI LPDS 1 Summer School on Grid and Cloud Workflows and Gateways 2013

Outline Terminology Recap: SZTAKI Cloud and LPDS Cloud Cloud-Manager Cloud bursting definition, scalability in general Scaling SZTAKI Cloud Summary Additional Reading and References 2 Summer School on Grid and Cloud Workflows and Gateways 2013

Terminology I. Based on deployment model: o Public Cloud – “The cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.” 3 o Private Cloud – “The cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on premise or off premise.” 3 o Hybrid Cloud – Environment created by the combination of public and private cloud offerings o (Community Cloud) 3 3 Summer School on Grid and Cloud Workflows and Gateways 2013

Terminology II. Based on location: o Internal Cloud – Subset of the Private Cloud model where it is offered by an IT organization to its own business 1 (“on premise” 3 ). o External Cloud – Not hosted by own organization and offered by a 3 rd party. It can be either public or private 1 (“off premise” 3 ). Point of view of architectural service layers o Software as a Service (SaaS) o Platform as a Service (PaaS) o Infrastructure as a Service (IaaS) – Cloud bursting (scaling) at this level 4 Summer School on Grid and Cloud Workflows and Gateways 2013

Recap SZTAKI Cloud * o Institutional IaaS Cloud service by SZTAKI (private, internal) o 7 nodes (7*64 Core, 7*256GB RAM), 2*32TB Storage o OpenNebula based o Quotas for users LPDS Cloud * o Similar, but smaller scale o Internal private cloud for LPDS Typically we use the LPDS Cloud for internal needs and scale out to SZTAKI Cloud when needed. 5 * Sándor Ács: “SZTAKI Cloud”. Monday, 1 st 12:00. Summer School on Grid and Cloud Workflows and Gateways 2013

Definition, scalability Cloud Bursting: o “Cloud bursting is an application deployment model in which an application runs in a private cloud or data center and bursts into a public cloud when the demand for computing capacity spikes.” 4 However more generally, cloud bursting is a subset of the general scaling out problem Can be split into 2 parts: 1.Capability to scale out to a cloud to maintain QoS requirements (e.g., for handling short term spikes in computing capacity demand). 2.making the decision of (a) when, (b) how much, (c) how long and (d) where to scale out. 6 Summer School on Grid and Cloud Workflows and Gateways 2013

The ability to scale out (to a cloud) + Making the decision Summer School on Grid and Cloud Workflows and Gateways Scaling out scenarios (with SZTAKI Cloud) In this talk Auto-scaling techniques “Cloud bursting from WS- PGRADE/ gUSE” Thursday, 11:00-11:30

Cloud-Manager Part of the FCM 5 (“Federated Cloud Management”) Architecture We’ll now focus on the Cloud-Manager o For FCM c.f., Attila Kertesz: “Cloud Federation Approaches” 11:00 Today Schedules service calls to VMs and manages VMs REST/SOAP Web service interface for service call and VM queues The Cloud Resource Manager (CRM) component is responsible for the scaling decision (when/ where/ … ) Initially it was intended for scaling services in a single cloud We use this component internally for different scaling (bursting) multi-cloud scenarios. 8 Cloud-Manager Q1Q1 Cloud a VMQ x Cloud a VMQ y Cloud a VM Handler VA x VA y VM x 1 VM x 2 VM x n Cloud a … VM y 1 VM y 2 VM y m … Generic Meta-Broker Service FCM Repository VA x..VA y Summer School on Grid and Cloud Workflows and Gateways 2013 Service Handler

Cloud-Manager 1.Single queue for incoming service calls (or tasks) 2.Multiple VM queues o Different one for each VA and resource combination o VM queues can be managed automatically (CRM) or manually 3.Manages VM lifecycle (EC2 REST API) 4.Performs the scheduling of service calls to resources (Q 1 →VM) 9 Cloud-Manager Q1Q1 Cloud a VMQ x Cloud a VMQ y Cloud a VM Handler VA x VA y VM x 1 VM x 2 VM x n Cloud a … VM y 1 VM y 2 VM y m … Summer School on Grid and Cloud Workflows and Gateways Service Handler

SZTAKI Destination / Source PublicPrivate Private→Public (Scenario A. – “Cloud bursting”) Private→Private (Scenario B.) Volunteer Volunteer→Public (Scenario C/1.) Volunteer→Private (Scenario C/2.) Source: Current infrastructure type (not necessarily cloud based!) Destination: target cloud infrastructure type 10 Summer School on Grid and Cloud Workflows and Gateways 2013

Destination / Source PublicPrivate Private→Public (Scenario A. – “Cloud bursting”) Private→Private (Scenario B.) Volunteer Volunteer→Public (Scenario C/1.) Volunteer→Private (Scenario C/2.) 11 Summer School on Grid and Cloud Workflows and Gateways 2013 Scenario A: Private → Public

Form a hybrid cloud: when local resources are insufficient allocate resources from a public cloud provider Real world example: Prezi.com o Uses private resources w/ Amazon EC2 to handle peak traffic o Batch processing of tasks Zip files for download, fetch images for presentations, conversion jobs o Prezi.com Scale Contest – Jobs 5 seconds max in queue, VMs 2 minute boot time, instances paid by the hour – minimize cost while honor requirements 12 Summer School on Grid and Cloud Workflows and Gateways 2013

Scenario A: Private → Public In SZTAKI We have the following possibilities for bursting: 1.OpenNebula based bursting 2.Cloud-Manager based bursting However we prefer to use private clouds over public ones – bursting to public clouds is set up as absolute last resort 13 Summer School on Grid and Cloud Workflows and Gateways 2013

OpenNebula: Building a Hybrid Cloud (Scenario A)* OpenNebula supports accessing multiple remote providers through the EC2 API – not necessarily just Amazon EC2 Remote provider appears as new host in OpenNebula 14 Summer School on Grid and Cloud Workflows and Gateways 2013 Resource limits by administrator for number and type of instances VMs can be started in EC2 or locally VM counterpart at remote provider – EC2 section in VM template Network connectivity via VPN * Sándor Ács: “OpenNebula”. Monday, 1 st 11:00.

On-demand Scaling of Computing Clusters E.g., elastic execution of a Condor computing cluster Dynamic growth of the number of worker nodes to meet demands using EC2 Private network with NIS and NFS EC2 worker nodes connect via VPN On-demand Scaling of Web Servers E.g., elastic execution of the NGinx web server The capacity of the elastic web application can be dynamically increased or decreased by adding or removing NGinx instances OpenNebula: Hybrid Cloud Use Cases* * Sándor Ács: “OpenNebula”. Monday, 1 st 11:00.

Cloud-Manager: multi-cloud (Scenario A) Cloud-Manager supports multiple providers through the EC2 REST/ SOAP API o OpenNebula, OpenStack, Eucalyptus and Amazon EC2 Primarily for scaling Distributed Computing Infrastructures (DCIs) Service calls are bound to VA’s o Each configured provider must have the counterpart (AMI-ID) Network connectivity via VPN when needed 16 Summer School on Grid and Cloud Workflows and Gateways 2013 Cloud-Manager Q1Q1 Cloud a VMQ x Cloud b VMQ x Cloud a Handler VA x VA y VM x 1 VM x 2 VM x n Cloud a … VM x 1 VM x 2 VM x m … Service Handler Cloud b Handler Cloud b

Destination / Source PublicPrivate Private→Public (Scenario A. – “Cloud bursting”) Private→Private (Scenario B.) Volunteer Volunteer→Public (Scenario C/1.) Volunteer→Private (Scenario C/2.) 17 Summer School on Grid and Cloud Workflows and Gateways 2013 Scenario B: Private → Private

Scale from a private infrastructure to another private infrastructure o E.g., scale from your local infrastructure (e.g., private internal) to another academic cloud (e.g., private external) Typical use case for us: scaling out from LPDS Cloud to SZTAKI Cloud (however both can be considered as internal clouds) 18 Summer School on Grid and Cloud Workflows and Gateways 2013

SZTAKI: Scenario B+A (1/2.) 19 Summer School on Grid and Cloud Workflows and Gateways 2013 We scale primarily computing clusters (Condor, BOINC) with Cloud-Manager 1.We use the LPDS Cloud (private) 2.Scale out to SZTAKI cloud (private) 3.As last resort scale out to Amazon EC2 (public)

SZTAKI: Scenario B+A (1/2.) 20 Summer School on Grid and Cloud Workflows and Gateways 2013 The master node (1) and the Cloud-Manager (2) are hosted usually on a dedicated resource VPN head (3) must be typically on a public IP node o We use a patched version on TINC with public key authentication The Cloud Resource Manager (4) is responsible for auto-scaling New VM instances are created and destroyed through the EC2 REST/SOAP API (5)

Example: Scaling a Condor cluster with Cloud-Manager 21 Summer School on Grid and Cloud Workflows and Gateways CM Service calls → Jobs for Condor Through REST/SOAP interface: (e.g., WS-PGRADE/ gUSE) 2.VPN Head on public IP 3.Manager node: Cloud-Manager and Condor Master VAs are deployed at LPDS, SZTAKI, Amazon EC2 Contextualization by Cloud-Manager: Key for VPN VPN Head public IP Condor Master IP on VPN

Summer School on Grid and Cloud Workflows and Gateways Example: Scaling a Condor cluster with Cloud-Manager

Destination / Source PublicPrivate Private→Public (Scenario A. – “Cloud bursting”) Private→Private (Scenario B.) Volunteer Volunteer→Public (Scenario C/1.) Volunteer→Private (Scenario C/2.) 23 Summer School on Grid and Cloud Workflows and Gateways 2013 Scenario C: Volunteer → {Public, Private}

LPDS runs multiple BOINC based volunteer computing projects – SZTAKI Desktop Grid, o People donate their computers’ idle computing cycles to science o We do not own the resources o We do not have any control over the resources These resources are “free” however not very reliable o Jobs might be returned late or gone missing We burst to clouds to provide reliable computing resources for problematic jobs when needed o LPDS → SZTAKI → Academic Clouds →Amazon EC2 C.f., Jozsef Kovacs: “Integrating clouds with grid systems – the SZTAKI-BOINC 11:30 24 Summer School on Grid and Cloud Workflows and Gateways 2013

Summary Bursting (scaling) consist of the capability + decision making In this presentation I showed some scenarios from SZTAKI: o Private → {Public, Private}; Volunteer → {Private, Public} o OpenNebula and Cloud-Manager based The decision making process (i.e., auto-scaling) will be the topic of my presentation on Thursday o “Cloud bursting from WS-PGRADE/ gUSE” – Thursday, 11:00-11:30 Summer School on Grid and Cloud Workflows and Gateways

References and Additional reading [1] Nair, S. K., Porwal, S., Dimitrakos, T., Ferrer, A. J., Tordsson, J., Sharif, T., Sheridan, C., Rajarajan, M. & Khan, A. U. (2010). Towards secure cloud bursting, brokerage and aggregation. Paper presented at the IEEE European conference on Web Services, 1 Dec 2010 – 3 Dec 2010, Cyprus. [2] D. McDysan: Cloud Bursting Use Case. IETF. sdnp-cloudbursting-usecase-00http://tools.ietf.org/html/draft-mcdysan- sdnp-cloudbursting-usecase-00 [3] National Institute of Standards and Technology (NIST): The NIST Definition of Cloud Computing. September, /SP pdfhttp://csrc.nist.gov/publications/nistpubs/ /SP pdf [4] SearchCloudComputing burstinghttp://searchcloudcomputing.techtarget.com/definition/cloud- bursting [5] A. Cs. Marosi, G. Kecskemeti, A. Kertesz and P. Kacsuk, FCM: an Architecture for Integrating IaaS Cloud Systems. In Proceedings of The Second International Conference on Cloud Computing, GRIDs, and Virtualization. Rome, Italy. September, Summer School on Grid and Cloud Workflows and Gateways 2013

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Summer School on Grid and Cloud Workflows and Gateways