Data center modeling, and energy efficient server management

Transcription

1 Data center modeling, and energy efficient server management Satoshi Itoh National Institute of Advanced Industrial Science and Technology (AIST) 1

2 Contents Virtualization Energy-saving scenario Data Center and Cloud computing Key technologies for Green Cloud Virtual Cluster system AIST 1 sec Live Migration Storage Live Migration Server Modeling Summary 2

3 Green Cloud Two major trends related to data center Virtualization and Grid are essential technologies to realize both Cloud, Green Data Center and Green Cloud Data Center Energy-saving Low carbon Service Utility Green Data Center Virtualization / Grid Cloud Green Cloud 3

4 Energy-saving scenario Pack the application (Service) into fewer physical servers Power off the unused servers Service A Service B Service C Service A Service B Service C Server 1 Server 2 Server 3 Server 1 Server 2 Server 3 4

5 Opportunities in Coarser grain Find opportunities in Module, Room, and Data Center levels Power off air conditioner and power supply Contribute significant energy-saving (doubles) Multi-site Room 1 Room 2 Room 1 Room 2 Data center 1 Data center 2 Data center 1 Data center 2

6 MAPE Loop for energy MAPE loop : General concept of optimal management It is used to optimize utilization of IT equipment It can be applied for power aware computing and optimal energy management Alert of energy Monitor Analyze Keyword Counter Action Energy-saving by virtualization Execute Plan 6

7 Technologies to support Green Cloud Data Center 2 Data Center 1 AP2 AP4 AP3 AP4 7 Server1 Server2 Server3 Green Cloud management system Virtual Cluster System Server4 Cluster of virtual servers (Xen, KVM, VLAN) Multi-site Cluster (VPN) On demand storage (iscsi)

8 Technologies to support Green Cloud Monitoring of CPU load, power consumption Data Center 2 Data Center 1 AP3 AP2 Server1 Server2 Server3 Server4 Not only CPU load But also power consumption 8 Green Cloud management system

9 Technologies to support Green Cloud Data Center 2 Data Center 1 AP2 AP3 AP2 AP2 AP2 AP3 AP2 Server1 Server2 Server3 Server4 AP2 Modeling of server and services Green Cloud management system Optimal assignment planning Estimate power consumption using the models of server and services. Choose the plan that makes lower power consumption 9

10 Technologies to support Green Cloud Live migration Data Center 2 Data Center 1 AP2 AP3 AP2 AP2 AP2 AP3 AP2 Server1 Server2 Server3 Server4 10 Green Cloud management system Optimal assignment planning Some of services finish Planning optimal assignment again Migrate the service without stopping

11 Technologies to support Green Cloud Data Center 2 Data Center 1 AP2 AP2 AP3 AP3 AP3 AP2 11 Server1 Remote power Server2 Server3 control Green Cloud management system Optimal assignment planning Server4 Shutdown unused server Reduce the power consumption Turn it on, when needed

12 Technologies to support Green Cloud Data Center 2 Data Center 1 AP2 AP2 AP2 AP3 Storage Live Migration AP3 AP3 12 Server1 Server2 Server3 Green Cloud management system Optimal assignment planning Cooperation of Server4 IT equipment and facilities Some of services finish again Migrate the service to another site without stopping Storage data is also copied Shutdown unused room/dc Reduce huge power consumption

13 Technologies to support Green Cloud Data Center 1 Monitoring of CPU load, power consumption AP2 AP2 AP3 Live migration AP2 AP3 AP3 Data Center 2 Storage Live Migration Remote power Server1 control Server2 Server3 Modeling of server and services Virtual Cluster System Cooperation Server4 of IT equipment and facilities 13 Green Cloud management system Optimal assignment planning AIST Research target

14 Virtual cluster system Reservation of required resource for virtual cluster via portal OS and required software are automatically installed at the reserved time NPACI Rocks is a base Virtual Cluster is produced using three types of virtualization technologies Server VMware Server / Xen / KVM Network VLAN and VPN Storage iscsi and LVM Prototype system is available Access from internet Software and data provisioning Site A Site B

15 Live Migration The movement of a service from one physical machine to another while continuously waked-up. Some of production software (VMware, Xen, KVM) can do, if those machines share disk in a single domain At least 10 seconds are needed with 1GB memory to switch the host (It takes much more, if application updates frequently memory pages) Service VM Copy VM states faster than updates VM 1. Copy all memory pages to destination 2. Copy again updated memory pages during the previous copy 3. Repeat the 2 nd step until the rest of memory pages are enough small Server1 Shared Disk Server2 4. Stop VM 5. Copy CPU registers, device states, and the rest of memory pages. 6. Resume VM at destination 15

16 AIST 1sec Live Migration Switch the execution host only in 1 second! Copy VM memory after relocation STOP Service VM VM 1. Stop VM 2. Copy CPU and device states to destination 3. Resume VM at destination 4. Copy memory pages on demand Server1 Server2 Shared Disk 16

17 AIST 1sec Live Migration Switch the execution host only in 1 second! Copy VM memory after relocation Service VM VM 1. Stop VM 2. Copy CPU and device states to destination 3. Resume VM at destination 4. Copy memory pages on demand Server1 Server2 Shared Disk 17

18 AIST 1sec Live Migration Switch the execution host only in 1 second! Copy VM memory after relocation Server1 Service VM Resume Service VM Server2 1. Stop VM 2. Copy CPU and device states to destination 3. Resume VM at destination 4. Copy memory pages on demand Shared Disk 18

19 AIST 1sec Live Migration Switch the execution host only in 1 second! Copy VM memory after relocation Trivial modification to VMM: Only add 200 lines to KVM Transparent Simple and Stable No special drivers and programs in VM Production ready quality Support any guest operating systems Service VM Service VM 1. Stop VM 2. Copy CPU and device states to destination 3. Resume VM at destination 4. Copy memory pages on demand Server1 Server2 Shared Disk Copy accessed memory pages 19

20 Network Throughput (Mbytes/s) Verification of the efficiency Number of Responses Relocate Web server of SPECWeb2005(Banking) Existing method (Pre-copy) Migration was never completed Update of memory pages is faster than data transfer Proposed method (Post-copy) Host is switched in 1 sec Response down for a while, but it resumed in about 10 seconds Environment 1 VCPU 1GB RAM Intel Core2 Duo E6305 4GB RAM VM (httpd) Live Migration SPECWeb Back End SPECWeb Client GbE GbE Shared Sstorage Pre-copy Post-copy 20 Time (s)

21 Multi-site storage live migration The movement of a service from one physical machine to another in a different site while continuously waked-up. Copy Memory and disk images Copy memory image and activate the remote site virtual server Service accesses the disk in the original site and write it to the local disk Whole of data is copied to the remote site and finally the service runs at the remote site. Original site Remote site VM VM Access Server 1 and Server 2 copy Remote disk WAN Local disk 21

22 Combination of server and service Server consists of several components and has characteristics CPU, HDD, power supply, fan, High density blade, Low-power HDD/processor, water cooling, SSD,.. Different application creates different workload of these components Mail server, Web server, database server, Server changes energy consumption according to the application on it Mail Server Low power HDD-PC DB Server Blade Server? DB Server Low power HDD-PC Mail Server Blade Server 22

23 Modeling of server and service Modeling of Service (Software) De-composite to elemental processing Mail Server:CPU load ~ 30% Disk write ~ 70% DB Server:CPU load ~ 70% Disk write ~ 80% Modeling of Server (Hardware) De-composite to power consumption of components CPU load power in CPU Store and access power in Disk Data send and receive power in NIC and CPU Service (Software) Computing CPU load CPU Disk Write disk Circuit board Read disk Server (Hardware) PSU Data transfer Fan 23

24 Power consumption in 1U server DELL PowerEdge R300 CPU: Dual CoreIntel Core 2Duo E6305 Clock: GHz Memory:9GB HDD:SATAⅡ 80GB (7200RPM) x 1 Power consumption Idle state:~76w Disk access: ~4W LINPACK: ~132W (12GFlops) Fan(high room temperature):~13w Fan (high room temp.) Floating point CPU in use Idle state (Static) Fan normal HDD no access Memory PSU loss etc. ~13W ~9W ~64W ~4W ~76W HDD Access Measured at AIST 24

25 Power consumption in blade server DELL PowerEdge M1000e (Enclosure) 16 Blades per chassis 6 Fans (12V5.6A) 6 PSUs Power consumption (idle):~ 213W DELL PowerEdge M600 (Blade) CPU: Quad Core Intel Xeon E5420 x 2 Clock:2.5GHz Memory:8GB HDD:SATAⅡ 80GB (7200RPM) (2.5 ) Power consumption Idle state:~124w Disk access: ~4W LINPACK:~288W(70GFlops) Blade part Enclosure part ~4600W (16 枚 ) ~288W (1 blade) ~213W ~20W ~144W ~124W Floating point CPU in use Idle state (Static) HDD no access etc. 25 Measured at AIST

26 power consumption Power consumption in HDD Capability of storage: Store and Access HDD consumes power when ~ Disk spin Store capability Read and Write processing Access capability Head seek Access capability data access capability power consumption while data access ~4W power consumption / Access speed (W/Gbps) Energy consumption / Access size (Ws/GB) Time data store capability power consumption while no data access ~8W power consumption / Disk size (W/GB) 26 Measured at AIST

27 Power consumption in storage DELL EMC AX4-5F Disk array enclosure(dae) 12 HDD in an enclosure Power consumption 390 VA, 360 W (maximum) Idle state with 12 HDD : 300W 8W / HDD 12 8W=96W 204W for enclosure Access 4W/HDD Store (Idle state) ~300W 8-12W / HDD Enclosure part ~204W 27 Measured at AIST

28 Power consumption in Switch Force10 C300 Size:13U Capacity: Tbps Line card slots: 8 48 GbE ports 8 10GbE ports Power consumption Traffic part Enclosure:305W (maximum 3%) RPM(Routing Processor Module) x 2 Fan x 6 PSU (Max 1.4KW)) x 2 48 GbE Line Card: 103W / slot 48 ports wire-rate traffic: +4W Full install (48GbE x 8=384 GbE) 1100W Idle state (no traffic) 32W 1129 W 103~ 107W 305W Line card part Enclosure part 48ポート 103W, ~2W / port 28 Measured at AIST

29 Model and Metric Power consumption changes by combination of service and server Estimate power consumption before assigning Mail Server DB Server DB Server Mail Server PSU Disk CPU PSU Disk CPU PSU Disk CPU PSU Disk CPU Low power HDD-PC Blade Server Low power HDD-PC Blade Server Metric for optimal assignment in the sense of power consumption Power (Energy) consumption / performance Simple case: LINPACK using 1U server and Blade server LINPACK Power cnsumption Power / Performance (Energy / Work) 1U Server 12GFlops 132W 11 W/GFlops(Ws/GFlop) Blade Server 70GFlops 301W 4.3 W/GFlops(Ws/GFlop) 29

30 User s responsibility(green SLA) Responsibility for low carbon society extend from provider side to user side. Users have to recognize power consumption what they use They pay for it and use the data for carbon footprint Cloud Pay for service ( can include power cost in it ) Power consumption of physical servers can be monitored Modeling of VM for power consumption is necessary Green SLA Contribute to energy-saving even though performance of the service drops SLA needs to include Green items (with performance, reliability, security etc. ) 30

31 Topics for standard Necessity of new metrics How much does use of SaaS, ASP and Cloud contribute to energy-saving? energy consumption / service performance How greener is your data center than competitors / last year s record? energy consumption / performance of data center How much can service migration reduce energy consumption? energy consumption / performance of server Green SLA Contribute to energy-saving even though performance of the service drops 31

32 Summary Virtualization technologies can be used to save TCO and Energy in data center. Energy-saving scenario in the multiple levels of data center, server, rack, room, whole data center AIST is developing technology and middleware for power aware management in Green Cloud Virtual Cluster system Live (Storage) migration Optimal assignment Modeling of server and data center Green SLA document and software are here 32