© university of ottawa, on, canada1 chapter 11: energy-efficient cloud computing: a green migration...

© University of Ottawa, ON, Canada 1

Chapter 11: Energy-Efficient Cloud Computing:

A green migration of the traditional IT

Hussein T. Mouftah and Burak Kantarci

School of Electrical Eng. and Computer ScienceUniversity of Ottawa

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS


Outline

Introduction to Cloud Computing Green ICT and Energy-Efficient Cloud Computing

Motivation for Green Data Centers Motivation for Green ICT

Energy-Efficient Storage and Processing in Cloud Computing Energy-Efficient Processing in Data Centers Energy-Efficient Storage Monitoring Thermal Activity in Data Centers

Optimal Data Center Placement Energy-Efficient Transport of Cloud Services Summary and Challenges


Introduction to Cloud Computing (1/4)

Cloud computing infrastructure of business enterprises Software as a Service

(SaaS)• e.g., web services, e.g., web services,

multimedia, etc.multimedia, etc.

Platform as a Service (PaaS)

• e.g., software framework, e.g., software framework, storage, etc.storage, etc.

Infrastructure as a Service (IaaS)

• e.g., Virtual machinee.g., Virtual machine



Various deployment models existPublic clouds serve through the Internet backbone and operate based on the pay-as-you-go fashion

Private clouds are dedicated to an organization where the files and tasks are hosted within the corresponding organization



Various deployment models exist (Cont ‘d )

Community clouds enable several organizations to access a shared pool of cloud services forming a community of a special interest

Hybrid clouds are a combination of the public, private and the community clouds with the objective of overcoming the limitations of each model



Challenges in cloud computing Security Privacy Reliability Virtual Machine Migration Automated Service Provisioning Energy Management


Green ICT and Energy-Efficient Computing (1/3)

Motivation for Green ICT ICT consumes 4% of the electricity and expected

to be doubled (8%) Telecommunication networks contribute a big

portion of the CO2 emissions of ICTs• GHG emission contribution of the telecom networksGHG emission contribution of the telecom networks

– PCs and monitors (40%)– Servers (23%)– Fixed Line Telecommunications (15%)– Mobile Telecommunications (9%)– LAN and Office Communications (7%)– Printers (6%)



Motivation for Green Data Centers Cloud Computing infrastructure is housed in Data

Centers US Data Centers consume 1.7%~2.2% of the total

electricity consumed in the country (61 billion kWh in 2006, doubled in 2007)

Worldwide data centers consume 1.1%~1.5% of all electricity consumed in the world

Proper Power Management in the data centers can lead to significant energy savings • Virtualization of computing resourcesVirtualization of computing resources• Sleep schedulingSleep scheduling

Shared Servers and Storage Units Energy savings possible if users migrate IT services

towards remote resources Increase in the network traffic and the associated network

energy J. Baliga et al., “Green Cloud Computing: Balancing Energy in Processing, Storage, and

Transport”, Proceedings of the IEEE, vol. 99, issue-1, pp. 149-167, 2011.



Motivation for Green Data Centers (Cont ‘d) A key metric to evaluate how “green” is a data

center Power Usage Efficiency (PUE)

Data Center Efficiency (DCE)

• A good DCE is 0.625A good DCE is 0.625• A reasonable DCE target is 0.5A reasonable DCE target is 0.5

C. Belady, ”The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE”,Whitepaper, [Online] http://www.thegreengrid.org/, 2008.

process

coolprocess

P

PPPUE

CenterData

EquipmentIT

P

PDCE

“Google-Data Center Efficiency”,” [Online] http://www.google.com/about/datacenters/inside/efficiency/powerusage. html, accessed in Oct. 20011.


Energy-Efficient Processing in Cloud Computing (1/6)

Thermal-Aware Workload PlacementMinimum Heat Recirculation (minHR) Objective: Minimizing the heat recirculation

(δQ) in the data center with n servers

Specific heat of air [Watt-s/Kg K]

Air flow rate at server-i [kg/s]

Inlet temperature of server-i

Temperature supplied by CRAC

Distribute the power (Pi) proportional to the ratio of the heat produced (Qi) to the heat recirculated (δQi) at server-i.



minHR Set a reference heat value (Qref) and a reference heat

recirculation value (δQi) Group of adjacent servers are considered to be a pod,

e.g., p pods in the data center Determine the power level in each pod by p iterations

Set the power level of servers in pod-j to maximum Calculate Heat Recirculation Factor for pod-j (HRFj)

Calculate power level for pod-j

Adjust CRAC supply tepmperature (Tadj)

J. Moore et al., “Making Scheduling Cool:Temperature-Aware Workload Placement in Data Centers,” inUsenix Ann. Technical Conf., 2005, pp. 61–74.



Cooling and thermal-aware workload placement

No cooling and thermal-No cooling and thermal-awarenessawareness

Cooling and thermal-aware Cooling and thermal-aware job managementjob management



Cooling and thermal-aware workload placement

A. Banerjee et al., “Integrating cooling awareness with thermal aware workload placement for HPC data centers,” Sustainable Computing: Informatics and Systems, vol. 1(2), pp. 134–150, June. 2011.

Temporal Job SchedulingFirst Come First Serve (FCFS)Earliest Deadline First (EDF)

Spatial Job SchedulingThermal-Aware Job Scheduling

Minimum Re-circulated Heat (MRH)

Cooling-aware Job SchedulingHighest Thermostat Setting (HTS)



Highest Thermostat Setting (HTS): A Cooling and Thermal-Aware Workload Placement scheme Temporally schedule the jobs

EDF / FCFS Server Ranking

According to the requirement of thermostat set temperature to meet the redline for 100% utilization

Spatial scheduling Place jobs to the available servers with the lowest

rank Obtain power distribution vector Ph

Set thermostat setting to the highest possible value (Tth

high) For details of the determination process see:A. Banerjee et al., “Integrating cooling awareness with thermal

aware workload placement for HPC data centers,” Sustainable Computing: Informatics and Systems, vol. 1(2), pp. 134–150, June. 2011.



A. Banerjee et al., “Integrating cooling awareness with thermal aware workload placement for HPC data centers,” Sustainable Computing: Informatics and Systems, vol. 1(2), pp. 134–150, June. 2011.

Benefits of coordinated workload scheduling

Without turning off idle servers With turning off idle servers


Energy-Efficient Storage in Cloud Computing (1/2)

Solid State Disks (SSDs) A storage device consisting of NAND flash memory and a

controller. An alternative to the conventional hard disk drives (HDDs) due

to: Being light weight, having small form factor, having no moving

mechanical parts and lower power consumption Issues to be addressed

Write reliability A single level SSD cell bit introduces write penalty after 100,000 writes

Cost / GB $1.80/GB for an SSD while a HDD costs approximately $0.11/GB (as of

2011)

Massive Arrays of idle Disks (MAIDs) Large amount of hard disk drives that are used for nearline

storage a hard disk drive spins up whenever an access request arrives

for the data stored in it and the rest of the storage consists of a large number of spun down disks.

trade-off between energy-efficiency and performance spinning up takes more time than data access does.


Energy-Efficient Storage in Cloud Computing (2/2)

Storage Virtualization A logical storage pool which

is independent of the physical location of the disks

Unused storage segments can be consolidated in logical storage units increasing the storage efficiency.

Servers are connected to the physical resources through SAN switches; hence a global storage pool is available to each server.

Whenever a storage block is required to be allocated, a logical unit number is assigned to the allocated virtual space

D. Barrett and G. Kipper, “Virtualization Challenges,” in Virtualization and Forensics, pp. 175 – 195. Syngress, Boston, 2010.


Monitoring Thermal Activity in Data Centers (1/2)

Project Genome

J. Liu et al., “Project Genome: Wireless Sensor Network for Data Center Cooling”, The Architecture Journal, Microsoft, vol 18, pp. 28-34, 2008.

RACNet: Wireless Sensor Networks (WSNs) in Data Centers Wireless sensor network

developed for Microsoft Research Data Center Genome project

Provides fine-grained and real-time visibility to data center cooling behaviour

~700 sensors deployed in a MMW data center

Hierarchical topology• Master and slave sensor nodesMaster and slave sensor nodes

Large-scale sensor network• Multiple slave sensors for Multiple slave sensors for

collecting temperature, humidity collecting temperature, humidity • Several master sensors providing Several master sensors providing

connectivityconnectivity Uses IEEE 802.15.4 radios


Monitoring Thermal Activity in Data Centers (2/2)

J. Liu et al., “Project Genome: Wireless Sensor Network for Data Center Cooling”, The Architecture Journal, Microsoft, vol 18, pp. 28-34, 2008.

Project Genome (Cont ‘d) RACNet Challenges

• High BER of IEEE 802.15.4 radioHigh BER of IEEE 802.15.4 radio• Tough RF environment in the Data Center due to high Tough RF environment in the Data Center due to high

metal contents of servers, racks, cables, railings, etc.metal contents of servers, racks, cables, railings, etc.• High density of wireless nodes in RACNet increases the High density of wireless nodes in RACNet increases the

likelihood of packet collisions. likelihood of packet collisions. Solution

• reliable Data Collection Protocol (rDCP)reliable Data Collection Protocol (rDCP)– Uses Three Key Technologies

• Channel Diversity: rDCP coordinates among multiple base stations to use 16 Channel Diversity: rDCP coordinates among multiple base stations to use 16 concurrent channels in the 2.4 GHz ISM band concurrentlyconcurrent channels in the 2.4 GHz ISM band concurrently

• Adaptive bidirectional collection tree: On each wireless channel, a collection tree is Adaptive bidirectional collection tree: On each wireless channel, a collection tree is built to adapt to link quality changes.built to adapt to link quality changes.

• Coordinated data retrieval: Data is polled by the base station. Only one data retrieval Coordinated data retrieval: Data is polled by the base station. Only one data retrieval stream on an active channel at a given timestream on an active channel at a given time


Optimal Data Center Placement (1/3)

X. Dong, T. El-Gorashi, and J. M. H. Elmirghani, “Green IP over WDM with Data Centers,” IEEE/OSAJournal of Lightwave Technology, vol. 29/12, pp. 1861–1880, June 2011.

Location of data centers has significant impact on the transport energy of cloud services in the Internet backbone.

Objectives Minimum power consumption Utilize renewable resources, e.g., solar panels, wind farms,

etc. IP-over-WDM network at the backbone

Employ optical-bypass Power consuming components

IP router ports Transponders EDFAs OXCs (De)multiplexers




Constraints Constraints of energy-minimized design of IP over

WDM network are inherited Additional constraints

Renewable energy consumption at the router ports, transponders., cooling and computing equipments in a data center cannot exceed the power supplied by a single wind farm.

Solar power that is available to a backbone node sets an upper bound for the renewable energy consumed by the router ports and the transponders of the corresponding node.

total renewable power consumption of all data centers is constrained to the total power supply of the wind farms.




optimal placement of data centers by incorporating non-bypass routing power savings between 4.4%

and 12.7%

multi-hop bypass routing-based design power savings between 1.7%

and 6.3%.

multi-hop bypass routing already consumes less power when compared to non-bypass routing

Optimal placement is a critical design parameter when number of data centers is limited and/or the backbone topology is irregular


Energy-Efficient Transport of Cloud Services (1/7)

Conventional network services Unicast (s, d)(s, d) Multicast (s, D)(s, D)

Cloud computing services Anycast

• Energy-Delay Optimal Routing (EDOR) Algorithm X. Dong, et al., “Green IP over WDM with Data Centers,” IEEE/OSA Journal

of

Lightwave Technology, vol. 29/12, pp. 1861–1880, June 2011.

Manycast

Evolutionary Algorithm for Green Light-Tree Establishment (EAGLE)

• B. Kantarci and H. T. Mouftah, “Energy-efficient cloud services over wavelength-routed optical transport networks,” in in Proc. of IEEE GLOBECOM, Dec. 2011.

),( Dds i

),( DDs k



Cloud-over-NSFNET Problem:Energy-Efficient Light-Tree

(EELT) selection

B. Kantarci and H. T. Mouftah, “Energy-efficient cloud services over wavelength-routed optical transport networks,” in in Proc. of IEEE GLOBECOM, pp. SAC06.6.1–SAC06.6.5, Dec. 2011.

(Considering manycast)




Objective

Maximize the number of sleeping nodes

Energymin Minimize total energy consumption

Minimize maximum resource (channel) consumption

Solving a manycast-based ILP model may lead to significantly long runtimes.Any faster solution?Heuristics:

Evolutionary Algorithm for Green Light-tree Establishment (EAGLE)



NO

Sort the manycast demands in

decreasing order

Find an initial solution space ISolution Space P

= I

Select two candidate

solutions in P w.r.t fitness

proportionate

Crossover on two solutions. Obtain

new two individuals

End conditio

n reached

?Channel

assignment on the new

individuals

New solutions valid?

Mutate new individuals with probability of γ

Compute a fitness

function for each solution

in PAdd

solutions to P

Age Solutions in PNO

YES

YES

• Evolutionary Algorithm for Green Light-tree Establishment Evolutionary Algorithm for Green Light-tree Establishment (EAGLE)(EAGLE)




Crossover in EAGLE




Fitness Functions in EAGLEMaximize the number of sleeping

nodes

Minimize the maximum channel index

Minimize the total consumed energy

β of the idle power is

consumed in the sleep mode




Cloud service demands arrive in four time zones • EST, CST, MST, PSTEST, CST, MST, PST

Size of the destination set : {3,4} Crossover prob. 0.20 Mutation ratio: 0.01 Solution space: 100 solutions

Energy consumption of EAGLE throughout the day

PST MST

CSTEST


Summary

Energy Efficient cloud computing Balance between process, storage and transport Processing and Storage

• Workload placementWorkload placement– Thermal-aware– Cooling-aware– Thermal-and-cooling-aware highest thermostat

setting• Thermal activity monitoring of data centers by WSNsThermal activity monitoring of data centers by WSNs

– rDCP for data collection by the WSN inside the data center

Transport– Energy-efficient anycasting/manycasting of cloud

service demands


Research Directions

Energy-Efficient Cloud Computing Processing and Storage

• Enhanced Data Center Monitoring Enhanced Data Center Monitoring • Further reduction in PUE of the data centersFurther reduction in PUE of the data centers• PUE enhancement vs SLA guaranteesPUE enhancement vs SLA guarantees• Solutions for public and private cloudsSolutions for public and private clouds• Transaction specific energy consumptionTransaction specific energy consumption

Holistic solutions considering

servers, storage and non-IT equipment


Thanks for your attention!

[email protected], [email protected]

© university of ottawa, on, canada1 chapter 11: energy-efficient cloud computing: a green migration...

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