Private Cloud Deployment Model for Academic Institution · Private Cloud Deployment Model for...
Transcript of Private Cloud Deployment Model for Academic Institution · Private Cloud Deployment Model for...
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Private Cloud Deployment Model for Academic Institution
Thinn Thu Naing, Ph.D.
Pro-Rector University of Computer Studies, Taunggyi
10h December 2015
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MYANMAR
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MYANMAR
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University of Computer Studies, Taunggyi
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Cloud Infrastructure
Services Available on the Cloud
Back-End Data Center (Cloud Service Provider)
User Developer Developer
Software as a Service
Platform as a Service
Infrastructure as a Service
Data Storage as a Service
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Academic Cloud Deployment Model (Previous Work)
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Open Source Systems involved in Private Cloud System Deployment
(Infrastructure as a Service (IaaS))
(Data Storage and Parallel Data Processing Framework)
(Platform of Google App Engine implementation framework)
(Moodle Learning Management System (LMS) Application on Cloud Infrastructure)
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Open Source System for Private Cloud Infrastructure
Elastic Utility Computing Architecture Linking Your Programs To Useful Systems
Its origins are as a research project in the Computer Science department at the university of California, Santa
Barbara (UCSB)
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Developer(s) Eucalyptus Systems, Inc. Initial release 1.0 2008-05-29
Stable release 3.2.0 (December 19, 2012)
Written in Java, C Operating system
Linux, can host Linux and Windows VMs
Platform Hypervisors (Xen, KVM, VMware)
Type Private and hybrid cloud computing
License GPLv3 (only), with Proprietary relicensing
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Front- End Nodes Specification
Hardware Minimum Suggested Notes
CPU 1 GHz 2 x 2GHz For your cloud to scale it helps to have at least a dual core processor
Memory 512MB 2 GB Block storage serving benefits from the presence of memory for caching
Disk 5400rpm IDE 10000rpm SATA Block storage serving benefits from fast disk access
Disk Space 40GB 1TB Disk space will condition block stora capacity
2 Network interfaces 100Mbps 1000Mbps
Cluster will be subject to heavy traffic on a busy cloud setup as it will act as a router for all instances started on NC it Controls and will provide block storage (if needed) to them
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Back-End Node Specifications Hardware Minimum Suggested Notes
CPU VT extensions
VT, 64-bit, Multicore
64-bit can run both i386, and amd64 instances; by default, Eucalyptus will only run 1 VM per CPU core on a Node
Memory 1GB 4GB additional memory means more, and larger guests
Disk 5400rpm IDE 7200rpm SATA or SCSI
Eucalyptus nodes are disk-intensive; I/O wait will likely be the performance bottleneck
Disk Space 40GB 100GB images will be cached locally, Eucalyptus does not like to run out of disk space
Networking 100Mbps 1000Mbps machine images are hundreds of MB, and need to be copied over the network to nodes
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Physical Components of Private Cloud System with Eucalyptus
Front-end Servers Layer
Back-end Servers Layer
Virtualized Servers Layer
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Type of VMs instances in Eucalyptus Private Cloud
• Instruction Set VMs (Amazon EC2, Eucalyptus) – Ubuntu VMs
CentOS VMs
– Windows Server 2003 VMs
• Framework VM
– AppScale VMs
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? • Google’s MapReduce inspired Yahoo’s Hadoop. • Distributed large data computing framework
– For clusters of computers – Thousands of Compute Nodes
• Fault tolerant and scalable storage of very large datasets across machines in a cluster.
• Now part of Apache group • Consists of two components
– File Store (Hadoop Distributed File System(HDFS)) – A Distributed Processing System (Map/Reduce
Model)
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Hadoop Storage System with PCs
• Specification • Name node
– 4GB (RAM) – 320 GB (HDD) – 2 Cores CPU
• Data node – 128 MB (RAM)
or low – 80 GB (HDD) or
low
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Google App Eingine (GAE) Overview
• Write user application in Python or Java • Test locally • Deploy on Google public cloud infrastructure • Automatic Scaling • Pay-as-you-go
Free for limited quotas Pay for additional scale: CPU, Bandwidth, Data Storage
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AppScale
• Open source GAE Implementation framework on private cloud
• Distributed and scalable API implementations • Infrastructure
– KVM/Xen – Eucalyptus – Amazon EC2
• Design and implement by UCSB
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Architecture of AppScale Cloud and Hadoop Storage System Integration
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Moodle System
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Moodle Servers in Different Locations
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Virtualized Moodle Servers on Private Cloud
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Academic Cloud Deployment Model with CloudStack
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THE PROJECT FOR DEPLOYING PRIVATE CLOUD SYSTEM FOR VIRTUALIZED LABORATORYS FOR
ICT HIGHER EDUCATION
Project Description
Title HRD Programme for Exchange of ICT Researchers and Engineers 2013
Granter Organization
APT (Asia Pacific Telecommunity)
Partner Organizations
Waseda University and KDDI Foundation, Japan
Period June 2014 to April 2015
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Private Cloud System
• Private Cloud Computing Lab underlying University Network backbone
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Logical Design of Private Cloud System
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Design Consideration for Cloud Deployment Model
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CloudStack Servers Specification Computer Node and Primary
Storage Server Secondary Storage Server
Brand PowerEdge R920 Brand Synology NAS (8 Bays)
Professor 2 CPU x Intel Xeon E7-4809 v2 Processor 1.9GHz, 12M Cache, 6.4 GT/s QPI, No Turbo, 6 Core, 105W
Professor Dual Core 2.13GHz CPU
Memory 128GB (8 X 16GB RDIMM, 1600 MHz, Low Volt, Dual Rank, x4)
Memory 2 x 4 GB DDR3 RAM
HDD 10Nos.x 1TB 7.2K RPM, 6Gbps Near Line SAS 2.5" Hard Drive - HotPlug
HDD 4 x Western Digital 2 TB ( Red ) Hard Disk
Controller PERC H730P Adapter RAID Controller, 2GB NV Cache
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Software Design and Deployment Plan Server
Component
Operating System and Softwares Remarks
Management Server CloudDB Server Compute Node
Operating System : CentOS 6.5 x86 Other Server : MySQL Server Cloud Server : CloudStack version 4.4.1 Virtualization : KVM hypervisor Server
(1) Configure the network
(2) Host name (3) SELinux (4) NTP (5) Configuring the
CloudStack Package Repository
Primary Storage Server Secondary Storage Server
Operating System : CentOS 6.5 x86 File Server : NFS Server (NFSv4)
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Implementation
• Phase I (Installation) • Install CloudStack components on the back end servers. • Provide IaaS (Infrastructure as a Service) and virtualized
computing resources including virtual machines, virtual switches, and virtual storage and so on.
• Phase 2(Testing) • To establish virtualized labs which can be involved to
provide computing services • Phase 3 (Implementing and Evaluation)
– This phase will evaluate the efficiency and effectiveness of academic cloud system and virtualized labs.
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Disaster in Myanmar (Cyclone Nargis in 2008)
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Disaster in Myanmar (Flooding in Myanmar 2015)
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Thank you very much