University of Luxembourg

Distributed systems and middleware

Claudio Fiandrinoclaudio.fiandrino@uni.lu

November 17, 2014

Agenda

Mobile Cloud Computing

Data Center Network Architectures

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Scenario

Mobile Operator Network

CloudInternetP-GWS-GW

MME

HSS PCRFeNodeB

IP Network

WiFi AP

WiFi APUEs

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Outline

Mobile Cloud ComputingIntroductionMobile DevicesMobile NetworksDistributed Application Processing

Data Center Network Architectures

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Mobile Cloud Computing

Mobile Cloud Computing vs. Mobile ComputingI In Mobile Cloud Computing the devices run cloud-based appsI In Mobile Computing the devices run native apps

In Mobile Cloud Computing users have to access remotely storedapplications and their associated data over the Internet

Mobile Cloud Computing: Common UnderstandingAccess to cloud computing services through mobile devices

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Mobile Cloud Computing

Mobile Cloud Computing vs. Mobile ComputingI In Mobile Cloud Computing the devices run cloud-based appsI In Mobile Computing the devices run native apps

In Mobile Cloud Computing users have to access remotely storedapplications and their associated data over the Internet

Mobile Cloud Computing: Common UnderstandingAccess to cloud computing services through mobile devices

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Mobile Cloud Computing: Definition

DefinitionAn integration of cloud computing technology with mobile devices tomake the mobile devices resource-full in terms of computational power,memory, storage, energy, and context awareness.

[1] Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing ApplicationModels," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014doi: 10.1109/SURV.2013.062613.00160

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Mobile Cloud Computing: Definition (II)

Definition (II)Mobile cloud computing is a model for transparent elastic augmentationof mobile device capabilities via ubiquitous wireless access to cloudstorage and computing resource, with context-aware dynamic adjustingof offloading in respect to change in operating conditions, whilepreserving available sensing and interactivity capabilities of mobiledevices.

[1] Dejan Kovachev, Yiwei Cao, Ralf Klamma: Mobile Cloud Computing: A Comparison of ApplicationModels. CoRR, abs/1107.4940, 2011.(http://arxiv.org/abs/1107.4940v1)

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Keywords

I Mobile devicesI Cloud ComputingI AugmentationI Preserving capabilitiesI Resource-full

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Key Elements

MobileCloud

Computing

MobileDevices

Network

CloudApplications

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Outline

Mobile Cloud ComputingIntroductionMobile DevicesMobile NetworksDistributed Application Processing

Data Center Network Architectures

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Type of Devices

I Not only smartphones and tablets

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Issues of mobile devices

I Resource scarcenessI Battery constrainedI Low connectivity

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Solution

Offloading to the CloudI ComputationI Data

Outcome: Application partitioningI Cope with mobile device issuesI Gather more dataI Use idle processing power

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Outline

Mobile Cloud ComputingIntroductionMobile DevicesMobile NetworksDistributed Application Processing

Data Center Network Architectures

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Technologies

I Cellular (3G/4G)I WiFiI Bluetooth

Comparison

TECHNOLOGY MAX DATA RATE ENERGY RANGE SPECTRUM

Cellular Up to 300 Mb/s1 High More than 10 km (1 km)2 LicensedWiFi (802.11n) Up to 100 Mb/s Medium Up to 250 m (120 m) Unlicensed

Bluetooth Up to 3 Mb/s Low Up to 100 m (20-30 m) Unlicensed

1. Considering 4x4 MIMO, 20 MHz channel2. Macro cell radius and typical urban cell radius

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Cellular Coverage

I http://opensignal.com/

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Health State of the Global Mobile Traffic

I 4.4 billion people will use mobile cloud applications by 2017I Mobile data traffic will reach 15 EB (1018) per month in 2018I 22 million wearable devices generated 1.7 PB (1015) per month traffic

in 2013

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What about Energy?

The importance of the technologies“Our calculations show that, in 2015, the wireless networks we use toaccess cloud services will command around 90% of the energy neededto power the entire wireless cloud services ecosystem. By comparison,data centres will account for only 9% or less. Industry needs to focus onthe real issues with wireless network technologies if it wants to solve thisproblem.”

CEET (Centre for Energy-Efficient Telecommunications), Massive energy cost hidden in wireless cloudboom, 2013(http://www.ceet.unimelb.edu.au/news/media/2013-04-09.html)

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What about Energy?

Cloud Wireless Access Burns More Energy Than Data Centres – Report(http://www.techweekeurope.co.uk/workspace/cloud-wireless-energy-use-data-centre-113227)

The importance of the technologies“Our calculations show that, in 2015, the wireless networks we use toaccess cloud services will command around 90% of the energy neededto power the entire wireless cloud services ecosystem. By comparison,data centres will account for only 9% or less. Industry needs to focus onthe real issues with wireless network technologies if it wants to solve thisproblem.”

CEET (Centre for Energy-Efficient Telecommunications), Massive energy cost hidden in wireless cloudboom, 2013(http://www.ceet.unimelb.edu.au/news/media/2013-04-09.html)

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What about Energy? (II)

Interesting Statistics

I One Google search is equivalent to about 0.2 grams of CO2(http://googleblog.blogspot.com/2009/01/powering-google-search.html)

I Raffi (Twitter API team) says: “One tweet consumes around 100 J”http://goo.gl/uMnV0f

http://www.slideshare.net/raffikrikorian/energy-tweet

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Diagnosis

I Networking can not be neglectedI Communications may impact dramatically on performanceI Carefully select proper technologies

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Outline

Mobile Cloud ComputingIntroductionMobile DevicesMobile NetworksDistributed Application Processing

Data Center Network Architectures

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Application Partitioning: Objectives

I Enhance mobile devices’ performanceI Energy saving

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High level list of applications

I CommerceI HealthcareI GamingI Searching (Keyword, Voice, Image, Location, Tag)

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Application Partitioning Classification

Static PartitioningA set of task is always run remotely

Dynamic PartitioningTasks are run where it is most convenient

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Where?

Mobile Operator Network

CloudInternetP-GWS-GW

MME

HSS PCRFeNodeB

IP Network

WiFi AP

WiFi APUEs

I Remote CloudsI Local CloudsI Other mobile devices

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Where?

Mobile Operator Network

CloudInternetP-GWS-GW

MME

HSS PCRFeNodeB

IP Network

WiFi AP

WiFi APUEs

I Remote CloudsI Local CloudsI Other mobile devices

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A practical example: Face Recognition

FlowI Detection on pictureI Determine match on database

Partitioning1. Local detection/remote recognition2. Remote detection/remote recognition

Beware: what is remote?

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List of Approaches

I CloudLetsI CloneCloudI MAUII MobiCloudI Odessa

I CuckooI HyraxI µCloudI ThinkAirI eXCloud

[1] Khan, A.R.; Othman, M.; Madani, S.A.; Khan, S.U., “A Survey of Mobile Cloud Computing ApplicationModels," IEEE Communications Surveys & Tutorials, vol.16, no.1, pp.393,413, First Quarter 2014doi: 10.1109/SURV.2013.062613.00160[2] Shiraz, M.; Gani, A.; Khokhar, R.H.; Buyya, R., “A Review on Distributed Application ProcessingFrameworks in Smart Mobile Devices for Mobile Cloud Computing,” IEEE Communications Surveys &Tutorials, vol.15, no.3, pp.1294,1313, Third Quarter 2013doi: 10.1109/SURV.2012.111412.00045

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Take-home messages

Mobile Cloud ComputingI Augmenting devices capabilitiesI Heterogeneity (applications/technologies/user behaviour)I Application Partitioning

To keep in mindNot simply “Access to cloud computing services through mobile devices.”

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Outline

Mobile Cloud Computing

Data Center Network ArchitecturesDefinition and ClassificationArchitectures AnalysisEnergy Consumption and Communication Latency Evaluation

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Data Center Definitions

Data Center InfrastructureThe data center is home to the computational power, storage,management and dissemination of data and information necessary to aparticular body of knowledge or pertaining to a particular business.

Data Center Network ArchitectureThe data center network architecture is the set of network nodes andlinks that characterize the interconnectivity among the computingservers and to the external world.

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Design Criteria

I ScalabilityI FlexibilityI ResiliencyI Maintenance

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A brief list of Architectures

I Fat-TreeI “Al-Fares”I PortlandI Hedera

I Clos (VL2)I HypercubeI BCubeI DCell

I DPillarI FiConnI FlatNetI HeliosI C-ThroughI PetabitI GreenCloud (Not the Simulator)

[1] Ali Hammadi, Lotfi Mhamdi, A survey on architectures and energy efficiency in Data CenterNetworks, Computer Communications, Volume 40, 1 March 2014, Pages 1-21, ISSN 0140-3664,http://dx.doi.org/10.1016/j.comcom.2013.11.005.(http://www.sciencedirect.com/science/article/pii/S0140366413002727)[2] Bari, M.F.; Boutaba, R.; Esteves, R.; Granville, L.Z.; Podlesny, M.; Rabbani, M.G.; Qi Zhang; Zhani,M.F., “Data Center Network Virtualization: A Survey," IEEE Communications Surveys & Tutorials, vol.15,no.2, pp.909,928, Second Quarter 2013 doi: 10.1109/SURV.2012.090512.00043

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Classifications

I Electronic/OpticalI Which technology is used in the forwarding?

I Switch/Server CentricI Who performs the forwarding?

Ali Hammadi, Lotfi Mhamdi, A survey on architectures and energy efficiency in Data Center Net-works, Computer Communications, Volume 40, 1 March 2014, Pages 1-21, ISSN 0140-3664,http://dx.doi.org/10.1016/j.comcom.2013.11.005.(http://www.sciencedirect.com/science/article/pii/S0140366413002727)

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Electronic vs Optical

I Fully electronicI Fully opticalI Hybrid

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Swich Centric vs Server Centric

I Switch Centric: switches are the key components in forwardingI Switches are complexI Servers no forwarding functionalitiesI Bandwidth oversubscription

I Server Centric: servers are the key components in forwardingI Switches are very simpleI Servers with forwarding functionalitiesI No bandwidth oversubscription

Trade offI Servers devoted to computational purposes onlyI Exploiting full link capacity

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Outline

Mobile Cloud Computing

Data Center Network ArchitecturesDefinition and ClassificationArchitectures AnalysisEnergy Consumption and Communication Latency Evaluation

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Three-tier

I Hierarchical structureI Bandwidth oversubscription

Computing Servers

Access Layer

Aggregation Layer

Core Layer

Gateway Router

Internet

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BCube

I Main parameters:I n: number of ports per switchI k + 1: number of ports per server

I Number of servers: nk+1

I Number of switches: (k + 1) · nk

I Example with n = 4 and k = 1

Internet

Computing Servers

Commodity SwitchesLevel 0

Commodity SwitchesLevel k + 1

Load Balancers

Gateway Router

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DCell

I Main parameters:

I n: number of servers per DCell0I k : number of DCell levels, server

degree of connectivity (k + 1)

I Number of servers:tj+1 = tj · (tj + 1), t0 = n

I Number of switches (#DCell0):gj = tj−1 + 1 or #servers/n

I Example with n = 3 and k = 1Internet

Computing Servers

Commodity Switches

Load Balancers

Gateway Router

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Outline

Mobile Cloud Computing

Data Center Network ArchitecturesDefinition and ClassificationArchitectures AnalysisEnergy Consumption and Communication Latency Evaluation

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Evaluation Criteria: Computing Servers

I Power consumption (DVFS)

P(l) = Pidle +Ppeak − Pidle

2· (1 + l − e−(

lτ ))

I l server loadI τ in the range [0.5, 0.8]

I Key idea:

Load= 0

Servers

Access Layer

Aggregation Layer

Core Layer

→ →

Increasing Load

→ →

Load= 1

Idle link; Active link; Idle device; Active device;

I Number of computing servers: 4096I Idle and peak power from Dell PowerEdge R720, Huawei Tecal

RH2288H V2 and IBM System x3500 M4

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Evaluation Criteria: Network

I Three-tier:I 128 racksI 16 aggregation switchesI 8 core switches

I BCube:I n = 8, k = 3I 2048 switches

I DCell:I n = 8I 2 < k < 3

I Other parameters:I 1 Gbits link (Three-Tier, BCube and

DCell)I 10 Gbits link (Three-Tier)I Test packets of 40 B and 1500 B

I MethodologyI One way delayI Transmission and Propagation

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Energy: Results

0 0.2 0.4 0.6 0.8 10

1

2

3

4

·105

Load (l)

Pow

erCon

sumption

(W)

Dcell Three-tier BCube

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Communication: Results

PERFORMANCE INDEXARCHITECTURES

Three-tier BCube DCell

Latency (40 B) 1.98 µs 3.93 µs 4.73 µsLatency (1500 B) 28.34 µs 73.72 µs 93.92 µs

Hop distance 5.78 7.00 8.94Server Degree Connectivity 1 4 2.79

Beware: we counted as 2 the number of links between any pair of servers within a DCell0 and in BCube.

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Take-home messages

Data Center Network ArchitecturesI Different Architectures have:

I Different impact on energy costsI Different impact on communication processes

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Thank You!Thank You!