Distributed Energy Efficient Clouds over Core …...• Dropbox, Google Drive, Skydrive, iCloud, and...

Distributed Energy Efficient Clouds over Core Optical Networks

Ahmed Q. Lawey, Taisir El-Gorashi and Jaafar M. H. Elmirghani

School of Electronic and Electrical Engineering, University of Leeds, United Kingdom

Introduction • Cloud computing is expected to be the main factor that will dominate the future

Internet service model by offering the users network based rather than desktop based applications.

• Cloud computing elastic management and economic advantages come at the cost of increased concerns regarding privacy, availability and power consumption.

• Designing future energy efficient clouds requires the co-optimization of both external network and internal clouds resources.

• In this work we introduce a framework for designing energy efficient cloud computing services over IP/WDM core networks considering three cloud services: (i) cloud content delivery, (ii) storage as a service (StaaS), and (iii) virtual machines (VMs) applications.

• External network related factors: • Number of clouds • Location of clouds

• Internal cloud related factors: • Number of servers • Number of switches • Number of routers • Storage size

• Energy Efficient Cloud Content Delivery

• Distributed vs. Centralised Content Delivery

• IP/WDM Power Consumption & Cloud Power Consumption

• Content Delivery MILP Model & Results

• DEER-CD Heuristic & Results

• Energy Efficient Storage as a Service (StaaS)

• StaaS Characteristics

• StaaS Model & Results

• Virtual Machine (VM) Placement for Energy Efficiency

• VM Placement Model & Results

• DEER-VM Heuristic & Results

• Summary

Outline

→Energy Efficient Cloud Content Delivery











• Summary

Outline

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Distributed vs. Centralised Content Delivery Energy Efficiency

We develop a MILP model for cloud content delivery in IP/WDM networks to answer whether centralised or distributed content delivery is the most energy efficient solution.

Given a particular client requests/demands, the model responds by deciding the optimum number of clouds that should be built and their location in the network as well as the capability of each cloud so that the total energy consumption is minimised.

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Definitions • Popularity group: Content requested with similar frequency by users is placed in a

popularity group.

• Zipf distribution: The Zipf popularity distribution for a stored object i is given by

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Popu

larit

y

Popularity Group ID

Zipf Distribution

𝑃 𝑖 = 𝜑𝑖�

where 𝑃 𝑖 is the relative popularity of object i and

𝜑 = �𝑖𝑁

𝑖=1

−1

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IP/WDM Power Consumption: Non-Bypass

Mux/Demux Amplifier Transponder

IP Layer

Optical Layer

Virtual Link

Physical Link

Optical Switch

IP Router

The total IP/WDM network power consumption is composed of:

IP/WDM Network

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Cloud Power Consumption

The total cloud power consumption is composed of:

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MILP Model for Content Delivery Objective: Minimize

Subject to (Including):

Traffic between Popularity Groups and Users

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Popularity group location

Cloud Location

MILP Model for Content Delivery

Scenarios

Forcing Max Number of Clouds: Full Replication (MFR) No Replication (MNR) Popularity Based Replication (MPR)

Optimal Number of Clouds: Full Replication (OFR) No Replication (ONR) Popularity Based Replication (OPR)

Forcing Single Cloud: No Power Management (SNPM) Using Power Management (SPM)

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MILP Model for Content Delivery

Power consumption of a router port Prp 1000 W Power consumption of transponder Pt 73 W

Power consumption of an optical switch in node i POi

85 W

Power consumption of EDFA Pe 8 W Power consumption of a Mux/Demux Pmd 16 W

No. of Wavelengths in a fiber W 16 Bit rate of each Wavelenght B 40 Gbps Span distance between EDFAs S 80 km Average client download rate Drate 5 Mbps Content Server Capacity CS_C 1.8 Gbps Content Server energy per bit CS_EPB 211.1W/Gbps Storage capacity S_C 378TB Storage power consumption S_PC 4.9KW Storage Utilization S_Utl 50%

Redundancy Red 2 Cloud switch power consumption Sw_PC 3.8KW Cloud switch capacity Sw_C 320Gbps Cloud router power consumption R_PC 5.1KW Cloud router capacity R_C 660Gbps

Cloud power usage effectiveness PUE_c 2.5 IP/WDM power usage effectiveness PUE_n 1.5

Number of popularity groups PGN 50

Input Parameters

• We assume number of users fluctuates between 200k and 1200k users in a day in our analysis.

• We analyse 50 popularity groups where the cloud storage is equally divided among the groups

Models Power Consumption

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MFRMNRMPROFRONROPRSNPMSPM

Scenario Total Savings

Network Saving

OPR 40% 72%

MPR 40% 72%

OFR 37.5% 56.5%

SPM 36.5% 37%

ONR 36.5% 37%

MNR 36.4% 36.5%

MFR 25.5% 99.5%

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Network Power Consumption Cloud Power Consumption

Total power consumption

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Popularity Based Content Replication (OPR)

OPR Content Replication Scheme

Node ID

Popularity Group ID

t=6 t=22

Object replicated here

Object not replicated

02468

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t=6t=22

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0100200300400500600700800900

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t=6t=22

DEER-CD Heuristic Phase 1:

Phase 2:

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Scenario Total Savings

Network Saving

OPR 40% 72%

DEER-CD 40% 65%

DEER-CD Heuristic Power Consumption

Network Cloud







→Energy Efficient Storage as a Service (StaaS)






• Summary

Outline

Energy Efficient Storage as a Service (StaaS)

StaaS Characteristics

• A special case of the content delivery service where only the owner or a very limited number of users can access the stored content.

• Dropbox, Google Drive, Skydrive, iCloud, and Box are examples of cloud based storage.

• Upon registration for StaaS, users are granted a certain size of free

storage. A DropBox for instance grants its users 2GB.

• Different users might have different levels of utilization of their StaaS drives

• Different users might have different documents access frequency.

• StaaS should achieve trade-off between serving content owners

directly from the central cloud and building clouds near to content owners.

StaaS Modelling Subject to (Including):

Clouds locations

Serving users decision variable

Satisfying nodes demands Central cloud to users traffic

Clouds storage capacity

Inter-Clouds Traffic

Total traffic between nodes:

• 1200 k users uniformly distributed among network nodes

• We compare three schemes: • Single Cloud • Optimal Clouds • 14 Clouds

• A two file sizes of 22.5 MB and 45 MB • A user storage quota of 2 GB • Users’ storage utilization is uniformly distributed

between 0.1 and 1. • Range of access frequencies considered (10 to

130 downloads per hour) • Optimal cloud scenario with the 45MB file

size saves about 48% of network power consumption compared to the single cloud scenario.

StaaS Results


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Power-on clouds storage size versus content access frequency (45 MB) file size

Power-on clouds storage size versus content access frequency (22.5 MB) file size

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→Virtual Machine (VM) Placement for Energy Efficiency



• Summary

Outline

• We develop a MILP model to optimise VM placement in IP/WDM networks.

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Virtual Machine (VM) Placement for Energy Efficiency

• We study different schemes for VM placement: • Single cloud • Migration • Replication • Slicing

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MILP Model for VM Placement Objective: Minimize

Subject to (Including):

Virtual Machines demand

Assumptions: • We do not include the storage power consumption • VMs have workload proportional power consumption

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MILP Model & Results for VM Placement

Virtual Machines location

Cloud Location

Cloud Total Workload

VMs Distribution Scheme at t=06:00

VMs Distribution Scheme at t=22:00

Input Parameters


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• The VM slicing scheme yields 25% and 76% total and network power savings.



DEER-VM Heuristic

DEER-VM Heuristic Pseudo-code

DEER-VM Heuristic

Scenario Total Savings Network Saving

VM-Slice-MILP 25% 76%

DEER-VM 24% 60%














→Summary

Outline

Summary • We have introduced three energy efficient cloud services, namely; Content Delivery, Storage as a

Service (StaaS) and Virtual Machines based applications. • A Mixed Integer Linear Programming (MILP) model was developed for this purpose to study network

related factors including the number and location of clouds in the network and the impact of demand, popularity and access frequency on the clouds placement, and cloud capability factors including the number of servers, switches and routers and amount of storage required at each cloud.

• Optimizing the cloud content delivery revels that replicating content into multiple clouds based on

content popularity is the optimum scheme to place content in core networks, resulting in 72% and 40% network and total power savings, respectively, compared to a power un-aware centralised content delivery scenario.

• We have developed an energy efficient content delivery heuristic, DEER-CD, based on the model

insights. Comparable power savings are achieved by the heuristic. • Results of the StaaS model show that migrating content according to its access frequency to serve

users locally saves 48% and 2% of the network and total power consumption respectively compared to serving content from a single central cloud for an average file size of 45MB. Limited total power savings are obtained for smaller file sizes.

• Optimizing the placement of VMs shows that slicing the VMs into smaller machines and placing them

near their users saves 76% and 25% of the network and total power, respectively compared to a single virtualised cloud scenario. Comparable power savings are obtained by placing VMs using a heuristic developed to mimic the model behaviour in real time (DEER-VM).

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