Transcript Introduction to Cloud computing
DESIGN CONSIDERATIONS OF A GEOGRAPHICALLY DISTRIBUTED IAAS CLOUD ARCHITECTURE
CS 595
LECTURE 10 3/20/2015
“Computation may someday be organized as a public utility.”
- John McCarthy, 1961 2
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OUTLINE
Cloud Computing Introduction • Local Cloud Architectures (IaaS) • Current Research Topics • Reducing costs of Owning/Operating Private Clouds • • • Deployment Power Aware Load Consolidation Power Aware Storage Consolidation • Virtual Machine Migration • Conclusion • Future Work 3
INTRODUCTION
CLOUD DEFINITION Cloud computing is a set of service-oriented architectures, which allow users to access a number of resources in a way that is elastic, cost-efficient, and on-demand.
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INTRODUCTION
CLOUD DEFINITION • Scalable resource allocation • Tailored services • • • Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) • Billed like a utility • public clouds 5
INTRODUCTION
CLIENT/SERVER VS. CLOUD ARCHITECTURE Storage Server Cloud Interface Switch/ Router Cloud Admin Network Network Client Client Client Client/Server Architecture Client Client Client Cloud Architecture 6 Compute Node Compute Node Storage Node
LOCAL CLOUD ARCHITECTURES
IAAS • Local Cloud?
• Small to medium sized • What resources would we need to do this?
• • • • • Compute Servers Persistent Storage Servers VM Image Server(s) Cloud Administrative Server(s) Network Infrastructure • Copper 7
LOCAL CLOUD ARCHITECTURES
IAAS • Compute Servers • CPU, RAM, Local disk (magnetic, SSD) resources given to the user.
• In the form of virtual machines.
• Hosts virtual machines using a hypervisor • Xen, KVM, ESXi • Grid VGX • Hybrid approach to hypervisor selection is common.
• • • Windows Linux Mac OS X 8
LOCAL CLOUD ARCHITECTURES
IAAS • Persistent Storage Servers • What they are: • VMs hosted on the compute servers are stateless.
• What they do: • Used for long term storage of data.
• Virtual Machine Image Server • Modified Persistent Storage Server.
• Repository of available VM images.
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CURRENT RESEARCH
• Private cloud architectures are great!
• But how can we expand while staying cost efficient?
• Current Research Areas: • • Deployment with limited networking resources.
Power aware cloud resource control.
• • Power Aware Load Consolidation (PALC) Power Aware Storage Consolidation (PASC) • Virtualization: Migration, Performance, and Costs 10
CURRENT RESEARCH
IAAS ARCHITECTURE 11
CURRENT RESEARCH
INTERFACE - RELATED • Lonea, et al. [2012] • Interfaces for Eucalyptus • Hashimoto, et al. [2012] • User Interface of Overlay Networks for Clouds • • Elasticfox [2013] Amazon AWS Web Interface, Firefox plugin • • Hybridfox [2013] Eucalyptus Web Interface, Firefox plugin 12
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CURRENT RESEARCH
INTERFACE Great! Now how do I interface with all of these cloud resources?
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CURRENT RESEARCH
INTERFACE 14
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CURRENT RESEARCH
INTERFACE Now that we have the cloud infrastructure, time to start using the resources!
• One NAT router, one public IP address.
• Problem?
• Only the cloud resources are behind the NAT router.
• Solutions • VPN for client devices • Extra layer of complexity for user • NAT port forwarding • • •
Uses DB to maintain protocols currently used by clients Update IP tables Hidden from user
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CURRENT RESEARCH
INTERFACE 16
CURRENT RESEARCH
INTERFACE 17
CURRENT RESEARCH
INTERFACE 18
CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE
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• Nielsen, Hacker 2010 • Using VPN for connecting VM based HPC Systems RELATED • Nimbula 2013 • Oracle based cloud infrastructure management • Wu, et al. 2011 • Plume, distributed cloud network for task dissemination • We provide an architecture for users to connect to a geographically distributed private IaaS cloud.
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CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE 20
CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE 21
CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE Distributed: • Main Cloud Cluster • • • Many auxiliary cloud clusters All private, behind NAT routers Connected using EoiP • Advantages • Pooling of resources • Issues?
• • Network traffic between cloud clusters Clients – use which cloud cluster?
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CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE 23
CURRENT RESEARCH
GEOGRAPHICALLY DISTRIBUTED PRIVATE CLOUD ARCHITECTURE • Example of Availability Zone’s in different cloud clusters: * Eucalyptus 2.1
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CURRENT RESEARCH
POWER CONSUMPTION • Reducing costs by applying active power aware strategies.
• • Power Aware Load Consolidation Power Aware Storage Consolidation • Advantages • lowers operational costs of cloud resources • Issues • • Resource availability power state transitions 26
CURRENT RESEARCH
POWER CONSUMPTION – PALC - RELATED • • Van, et al. 2011 • Power vs Performance in Clouds • Barroso, Holzle 2012 • Energy-Proportional Computing Hu, et al. 2011 • Scheduler for Load Balancing in Cloud Computing • We provide a power aware strategy for consolidating virtual machine requests on compute nodes.
• Power down unused compute nodes 27
CURRENT RESEARCH
POWER CONSUMPTION - PALC • PALC • Placement of virtual machines on as few compute servers as possible.
• • Other compute servers in low power state Able to convert hot/cold depending on user demand 28
CURRENT RESEARCH
POWER CONSUMPTION - PALC 29
CURRENT RESEARCH
POWER CONSUMPTION - PALC
Algorithm PALC consolidate: for all active compute nodes j
∈
[m] do n j end for if all n j end if
current utilization of compute node j > D t utilization //all available nodes are active boot vm on most underutilized compute node else boot vm on most utilized compute node end else upscale: if for all n > D end if end if t utilization if number of active compute nodes < m boot next available compute node downscale: if vm i idle > 6 hours or user initiated shutdown shutdown vm i end if if n j has no active vm shutdown active compute node end if
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CURRENT RESEARCH
POWER CONSUMPTION - PALC • VM request schedule for PALC experiments: 31
CURRENT RESEARCH
POWER CONSUMPTION - PALC
nodes Experiments ran on the local IaaS cloud architecture S20 S30 L20 L30 XL20 XL30 Seq20 Seq30 5 10 15 20
11 5.6
3.8
2.8
21.8
11.2
7.5
5.7
26.2
15.1
10.3
7.8
44.2
32.3
22.3
19.6
28.5
27.4
23.9
19.6
41.6
43.3
41.6
38.2
27.4
17.2
11.7
8.9
45.2
36 25.8
19.7
Power Consumption – PALC vs. Round robin 32
CURRENT RESEARCH
POWER CONSUMPTION – PASC - RELATED • Hasebe, et al. 2010 • Power-Savings in large scale storage systems • Prada, et al. 2012 • Power-Aware Storage Architecture for HPC • We provide macro management of storage devices based on current user utilization.
• Power down storage nodes during low utilization 33
CURRENT RESEARCH
POWER CONSUMPTION - PASC • PASC • Persistent storage is necessary in cloud environments • • VMs are inherently stateless.
How can we decrease the power consumption of storing persistent data without affecting availability?
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Algorithm PASC Active: if user j becomes active: locate data j on cold storage node HS a if HS
a current utilization of hot storage node can accommodate data j a +(Quota j – data j ): power on cold storage node transfer data j
hot storage node a delete data j from cold storage node power off cold storage node else: //no hot storage node can accommodate data j perform cold-hot conversion In-Active: if user j CS a becomes in-active:
if CS a current utilization of cold storage node a can accommodate data j : power on cold storage node a transfer data delete data j j
cold storage node a from hot storage node power off cold storage node a Cold-Hot Conversion: CS a
current utilization of cold storage node a for CS a ε [m] do: minUtil
(if CS a utilization < minUtil) update CS a with minUtil to hot storage node perform In-active on CS a to transfer unused data to cold storage nodes Hot-Cold Conversion: for CS a ε [m] do: if ((CS a + CS a+1 utilization) < Threshold): transfer data CS a
CS a+1 delete data from CS a update CS a to cold storage node Backup: if day < 7: incremental backup else: full backup
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CURRENT RESEARCH
POWER CONSUMPTION - PASC Typical Job Schedule (# of VM requests) 36
CURRENT RESEARCH
POWER CONSUMPTION - PASC • Experiment setup: • 2 job schedules • • Typical Random • 3 job types • Web Server • Network Intensive • Virtual Cluster • CPU Intensive • Database Server • Disk I/O Intensive 37
CURRENT RESEARCH
POWER CONSUMPTION – PASC Efficiency: PASC vs. Always On 38 Efficiency: PASC vs. Always on (Random Schedule)
CURRENT RESEARCH
POWER CONSUMPTION – PASC Power Consumed: PASC vs. Always On Power Consumed: PASC vs. Always on (Random Schedule) 39
CURRENT RESEARCH
POWER CONSUMPTION - PASC
Number of VMs 10 20 30 60 120 Web 13.2
14.8
14.3
21.3
36.5
Cluster
22 24.8
23.9
35.5
60.9
Database
18.4
21.4
19.3
29.8
51.7
Power Consumption – PASC vs. Always on 40
CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION - RELATED • Wu et al.
• Performance Models of VM live migration • Li et al.
• VM live migration based on performance predictions • Kuno et al.
• VM performance during migration 41
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CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION Live migrations allow vm’s to be relocated Performance/Issues with migrating virtual machines: • • • How does the vm perform during migration?
How well does the cloud architecture perform the vm migration?
How much power is consumed during migration?
• Live Virtual Machine Migration • Three phases • • • Push phase Pre-copy termination phase Pull-and-terminate phase 42
CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION 43
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CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION Experiments covering the following three areas: • • • Performance of Cloud Resources during Live Migration Performance of VM during Live Migration Power Consumption of VM Live Migration Setup: • 3 VM types: • Web: Apache Web server with PHP • Network Intensive • Cluster: Compute Pi to N th place • CPU Intensive • DB: MySQL Database • Disk I/O Intensive • 3 VM sizes 1.
256MB RAM, 2GB Disk, 1CPU core 2.
3.
512MB RAM, 5GB Disk, 1 CPU core 1024MB RAM, 10GB Disk, 2 CPU cores 44
CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION – CLOUD PERFORMANCE 45
CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION – VM PERFORMANCE 46
CURRENT RESEARCH
VIRTUAL MACHINE LIVE MIGRATION – POWER CONSUMPTION 47
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CONCLUSION
Local cloud architectures are a viable alternative for organizations.
• Hosting a local cloud architecture can be expensive • • • Need to reduce costs of owning/operating.
Need to make resources easily available.
Geographically distributed IaaS architecture.
• Low public networking resources • • • • Web based client/administrative interface.
PALC PASC Reduce cost by actively migrating virtual machines 48