Nefeli: Hint-based Execution of Workloads in Clouds

Nefeli: Hint-based Execution of Workloads in Clouds Konstantinos Tsakalozos # , Mema Roussopoulos # , Vangelis Floros ∗ and Alex Delis # # Department

of Informatics and Telecommunications, University of Athens, 15748, Greece ∗ Greek Research & Technology Network, Athens, 11527, Greece

February 8, 2011

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Introduction Nefeli Nefeli approach Task-flows Multiple task-flows Implementation details Evaluation Simulation Real application Conclusion

Outline

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The Cloud

What is Cloud Computing “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction”. Delivery Models:

Introduction

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Software as a Service (SaaS)

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Platform as a Service (PaaS) - Google App Engine

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Infrastructure as a Service (IaaS) - Amazon EC2

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Deployment models of Clouds

Introduction

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Public Cloud: available, not definitely free, user’s data not publicly visible

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Private Cloud: data and processes are managed within an organization

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Community Cloud: controlled by a group of organizations that have shared interests

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Hybrid Cloud: a combination of public and private clouds that interoperate

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Cloud characteristics

Introduction

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Rapid Elasticity: Scale up/downwards.

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Measured Service: Monitored by the cloud provider

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On-Demand Self-Service: Without any human interaction

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Ubiquitous Network Access: Not necessarily accessible through the Internet

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Resource Pooling: Use a multi-tenant model. Physical resources are reassigned.

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Infrastructure as a Service (IaaS)

The consumer has full control over:

Introduction

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processing power

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storage

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networking components (like firewalls)

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software stack ( including the operating system and deployed applications)

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Infrastructure as a Service (IaaS)

The consumer has full control over: I

processing power

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storage

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networking components (like firewalls)

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software stack ( including the operating system and deployed applications)

The consumer has no information/control over the cloud infrastructure beneath Containment in a virtual infrastructure (virtual machines, virtual networks)

Introduction

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Infrastructure as a Service Cloud

Introduction

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Users request Virtual Machines (VMs). VMs build virtual infrastructures.

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The cloud providers collect requirements in the form of SLAs, utility functions.

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The cloud middleware ([OpenNebula][Eucalyptus][Nimbus][VMWare]) considers the requirements and does the placement of VMs to physical hosting nodes.

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As VMs consume resources (CPU, network) SLAs may fail.

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VMs are migrated.

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Related work

Autonomic systems [Kephart03][Wang07][Van09]: 1. Monitor the cloud’s and virtual infrastructure’s performance. 2. Analyze results. 3. Plan SLA satisfaction/exploit utility functions [Kephart07]. 4. Execute VM migration. I

Introduction

React to SLA failures. Hamper top performance

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Problem Statement

Introduction

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Our goal: effective Virtual Machine deployment within the physical infrastructure of an Infrastructure as a Service Cloud.

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With Nefeli users pass deployment hints while the physical infrastructure remains “cloudy”.

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Nefeli approach

Virtual infrastructures serve “task-flows”: I

Flows of data.

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Task dependencies.

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Task parallelization.

If we were given the “task-flows”, we could make educated decisions on Virtual Machine deployment?

Nefeli

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Nefeli approach - Task flow examples

Parallel VMs simultaneously consuming the same resource (e.g CPU): Better to deploy on separate hosting nodes.

Sequential VMs producing excessive network traffic: Better to be placed on the same hosting node.

Nefeli

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Nefeli approach - Task flow examples

Parallel VMs simultaneously consuming the same resource (e.g CPU): Better to deploy on separate hosting nodes.

Sequential VMs producing excessive network traffic: Better to be placed on the same hosting node.

The challenge: preserve the cloud abstractions The user must describe the virtual infrastructure without any reference or knowledge of the physical hosting nodes.

Nefeli

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Task-flows Task-flow graph I

Each node is a Virtual Machine.

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Edges indicate data and/or control flows.

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2

4

5

Nefeli

1 512 VM1.img 2 512 VM2.img 3 3 512 VM3.img 4 512 VM4.img 5 512 VM5.img .............

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User constraints on Task-flows

Constraints are predefined, utility functions. The user knows their semantics. The cloud administration knows how to exploit it. Commonly used constraints:

Nefeli

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FavorVM: Try to reserve a single hosting node for a specific VM.

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MinTraf: Deploy on the same host a set of VMs so as to minimize traffic over physical network connections.

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ParVMs: Try to deploy a set of VMs in separate physical nodes so as not to compete over the same resources

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Task-flow example User/Consumer uses hard or weighted (soft) constraints to provide deployment hints

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4

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Nefeli

............. 1 2 3 3 2 4 1 1 0.4 2 0.3

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Administration Constraints User provided XML

Small Note

Specifications of Virtual Machine

Constraints

Constraints set by the cloud administration: I

PowerSave: Reduce the number of hosting nodes used for VM deployment

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EmptyNode: Offload a specific physical node

id: Object

id: Object

Deployment Patterns

Administration Constraints

Nefeli

Hardware Specifications Deployment Pofile Edit layers 0 and 1 as needed

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Deployment Profile I

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A VM-to-host mapping is termed deployment profile. The output of Nefeli. Each constraint I I

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is realized as a utility function evaluates a deployment profile

Each deployment profile m is assigned a score: X Score(m) = wi Consti (m), Const i ∈Cs

where Cs is the set of all constraints and w the respective weights. I

Nefeli

The goal of Nefeli is to find the profile with the highest score. We employ Simulated Annealing [Kirkpatrik83].

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Handling multiple task-flows on-the-fly

Nefeli

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One deployment profile, for many task-flows. The set of constraints to be considered is the union of all constraints

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A transition between deployment profiles involves VM migrations. No live migration causes down time of the virtual infrastructures.

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Given an initial deployment profile ms , we first produce k high scoring profiles then pick the one whose transition from ms requires migrating fewer VMs.

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We trade profile quality for swifter transitions.

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Event handling

Multiple task-flows within the same virtual infrastructure. The transition between deployment profiles is triggered by Events. Taxonomy of Events:

Nefeli

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Direct human intervention: submission or removal of task-flows, activation of constraints like PowerSave, EmptyHost

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Monitoring activity: in the context of the cluster, the virtual infrastructure or an authorized third party component

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Events - Example

Nefeli

...... 1 1 0.9 2 0.0 2 1 0.0 2 0.9 1 1000 2 2324 Change 1 2 1 2 1 2

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Events - Example

Nefeli

...... 1 1 0.9 2 0.0 2 1 0.0 2 0.9 1 1000 2 2324 Change 1 2 1 2 1 2

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Nefeli - Implementation Overview Note Note Note

User

Task−flows

Nefeli

Event Queue

Cloud API

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Implemented as a thin layer between a cloud middleware and the user.

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Cloud middleware connect aims to add support for any cloud middleware.

Cloud Middleware Connector Extra Functionalities

Cloud Middleware

VM

VM

VM

...

Monitoring Tools

VM

.....

Hosting Nodes

VM

VM

... Nefeli

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Nefeli - Implementation Internals Consumer

Note

Specifications of Virtual Machines

Constraints

User−monitored Events

Solver

Deployer

Deployment Profiles

Notification Mechanism

Provider

Nefeli − The Cloud Gateway Planner

Cloud Middleware Connector

Administration Constraints

Infrastructure Monitoring Physical nodes

Nefeli

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Nefeli - Evaluation Evaluation of Nefeli running an a simulated infrastructure. Deployment policies: I

Random

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Balanced, round robin

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Power save, use as few hosting nodes as possible

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Nefeli

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Nefeli with power save

Measure throughput as we increase CPU performance and cloud size.

Nefeli on a real cloud infrastructure Nefeli Vs OpenNebula, measuring execution time.

Evaluation

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Nefeli - Montage workflow “Montage: An Astronomical Image Mosaic Engine”, California Institute of Technology, http://montage.ipac.caltech.edu Constraints: 2

1

5

4

3

8

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6

9

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ParVMs on VMs {1, 2, 3, 4}, w = 0.30

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ParVMs on VMs {5, 6, 7, 8, 9, 10}, w = 0.30

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ParVMs on VMs {13, 14, 15, 16}, w = 0.30

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MinTraf on VMs {17, 18, 19, 20}, w = 0.50

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PowerSave, w = 0.80 Only for Nefeli+PowerSave

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Evaluation

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Nefeli - Montage evaluation Measure throughput of the trailing node.

Throughput (KBytes/Sec)

700 600 500 Random Power Balance Nefeli

400 300 200 100 0 x10

x40

x70 x100 x130 x160 CPU performance scale

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Increasing CPU performance Evaluation

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Throughput (KBytes/Sec)

40 Random Power Balance Nefeli Nefeli-power

35 30 25 20 15 10 5 3

4

5 6 7 8 Number of hosting nodes

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Throughput per active node (KBytes/Sec)

Nefeli - Montage evaluation

9 Random Power Balance Nefeli Nefeli-power

8 7 6 5 4 3 2 1 3

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5 6 7 8 Number of hosting nodes

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Increasing physical hosts number

Evaluation

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Nefeli - Real application evaluation

Application Input: I

a) A video

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b) Encoding (DVD, SVCD, VCD)

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c) Region (NTSC, PAL)

Video and audio encoding performed on different nodes. The application has 6 different deployment profiles, one per encoding+region. At run time the application informs Nefeli on which profile to follow.

Evaluation

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Nefeli - Real application evaluation

Evaluation details:

Evaluation

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3 nodes each with 8 GB of RAM and an Intel(R) Core(TM)2 CPU 6600 at 2.40 GHz CPU

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connected through a 1 GBps Ethernet switch

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Live migration not available

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VM hypervisor Xen 3.2-1

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Cloud middleware is OpenNebula v.1.2.0

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VMs use 512 MB of RAM and face no restriction on the CPU resource usage.

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Nefeli - Real application evaluation

1200

Split Transcode Merge

Time (Sec)

1000 800 600 400 200 0 ONE

Nefeli

17% improvement.

Evaluation

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Conclusions

Conclusion

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Nefeli enhances the interaction of users with IaaS clouds.

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We plan to integrate Nefeli with already existing monitoring mechanisms add support for Eucalyptus and Nimbus

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Investigate alternative scheduling options

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Organize and better manage virtual resources for applications that necessitate the use of massive data sets

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Related work - Autonomic Computing Keph. 03 J. O. Kephart and D. M. Chess, “The Vision of Autonomic Computing,” IEEE Computer, vol. 36, no. 1, pp. 41-50, 2003. Keph. 07 J. O. Kephart and R. Das, “Achieving Self-Management via Utility Functions,” IEEE Internet Computing, vol. 11, no. 1, pp. 40-48, 2007. Wang 07 X. Wang, D. Lan, G. Wang, X. Fang, M. Ye, Y. Chen and Q. Q.B. Wang, “Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center,” in Proc. of the 4th Int. Conf. on Autonomic Computing, Washington, DC, 2007, p. 29. Van s09 H. N. Van, F. D. Tran and J. M. Menaud, “Autonomic Virtual Resource Management for Service Hosting Platforms,” in Proc. of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, Vancouver, BC, Canada, 2009, pp. 1-8 Conclusion

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References - Virtual Infrastructures

ONE “OpenNebula”, http://www.opennebula.org, May 2009. Eucal. D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman,L. Youseff, and D. Zagorodnov, “The Eucalyptus Open-Source Cloud-Computing System,” in 9th IEEE/ACM Int. Symposium on ClusterComputing and the Grid (CCGRID), Shanghai, China, May 2009, pp.124-131. Nimbus “Nimbus,” http://workspace.globus.org/, Nov. 2009. VMWare VMware, “vSphere,” http://www.vmware.com/products/vsphere/, Nov. 2009.

Conclusion

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Related work - Other

Kirkp.83 S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671-680, 1983. Xen 03 P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, “Xen and the Art of Virtualization,” in Proc. of the 19th ACM Symposium on Operating Systems Principles. Lake George, NY: ACM, October 2003, pp. 164-177. Mont. 09 “Montage: An Astronomical Image Mosaic Engine”, California Institute of Technology, http://montage.ipac.caltech.edu, Pasadena, CA, 2009

Conclusion

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Nefeli - Montage workflow details Two ratios for each node: a) Input-to-Output, b) Cycles-to-Output Input-to-Output: Cycles-to-Output: I VMs {1, 2, 3, 4}, 0.5070 I VMs {1, 2, 3, 4}, 0.5830 I VMs: {5, 6, 7, 8, 9, 10}, I VMs: {5, 6, 7, 8, 9, 10}, 0.0500 29.4070 I VMs: 11, 0.0014 I VMs: 11, 1.5000 I

VMs: 12, 30.0000

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VMs: 12, 9.1910

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VMs: {13, 14, 15, 16}, 1.0010

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VMs: {13, 14, 15, 16}, 0.7390

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VMs: 17, 0.0200

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VMs: 17, 33.4210

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VMs: 18, 1.0790

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VMs: 18, 10.9050

VMs: 19, 25.0650

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VMs: 19, 0.5370

VMs: 20, 20.6250

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VMs: 20, 0.0290

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Conclusion

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