on analyzing elasticity relationships of cloud services

Daniel Moldovan, Georgiana Copil,

Hong-Linh Truong, Schahram Dustdar

On Analyzing Elasticity Relationships

of

Cloud Services

Distributed Systems Group (http://dsg.tuwien.ac.at/)Vienna University of Technology (http://www.tuwien.ac.at/)

[email protected]://www.infosys.tuwien.ac.at/staff/dmoldovan/

CloudCom 2014

http://dsg.tuwien.ac.at/

http://www.tuwien.ac.at/

mailto:[email protected]

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MotivationElasticity

2

Start with an initial lighter configuration

NOTE:

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Scale/reconfigure service unit when load increases

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Scale back service unit when load decreases

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Scale/reconfigure different service units if situation

demands it

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MotivationDeveloping intelligent controller: a little prediction

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Enforce Elasticity Capability

(scale in/out, reconfigure)

Effect/Impact w.r.t.

requirements on ALL UNITS ?

Change in Load

Understand how the elasticity of a service unit is influenced by another unit

i.e. if one unit “moves” towards violating its requirements, what happens with the

other units of the service

NOTE:

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Communication

dependencies

Elasticity control

processes

Elasticity RelationshipsCauses and Importance

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e.g., when adding a Data Node instance, the Data

Controller must be informed, to execute data balancing

tasks

e.g., if Event Processing is bottleneck, and thus is

scaled out, the bottleneck might transfer to the Data

end

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Elasticity RelationshipsTypes

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Direct

relationships

Indirect

relationshipse.g., throughput on Event Processing is dependent on

response time

e.g., CPU usage on Data End is dependent on

throughput on Event Processing which is dependent

on response time

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Elasticity RelationshipsDefinition

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𝑬𝒍𝑹𝒆𝒍𝒂𝒕𝒊𝒐𝒏𝒔𝒉𝒊𝒑:𝑪𝒉𝒂𝒏𝒈𝒆𝑭𝒄𝒕, 𝑫𝒆𝒍𝒂𝒚𝑭𝒄𝒕, 𝑨𝒕𝒕𝒆𝒏𝒖𝒂𝒕𝒊𝒐𝒏𝑭𝒄𝒕

Behavior i

Behavior j

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Elasticity RelationshipsStakeholders

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Different users might care about different types of relationships

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Aim

Objective

• Determine relationships influencing the service’s elasticity

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Approach

• Abstract and analyze service behavior w.r.t. elasticity requirements

Challenges

• How to abstract service behavior

• Lack of complete requirements

• Inadequate monitoring information

Scale IN/OUT Reconfigure

Elasticity Controller Elasticity Behavior Analysis MonitoringElastic Cloud Service Elasticity Relationships Analysis

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BackgroundElastic cloud service

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Abstracting Service BehaviorElasticity Energy

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El. Boundaryu

El. Boundaryl

If we determine relationships between concrete values, in case of elastic

services which scale in/out at run-time, those absolute values might not hold

after such elasticity capabilities are enforced

Thus, we abstract service behavior with respect to its requirements, which

give us for each metric collected from the service a lower and upper boundary

NOTE:

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Abstracting Service BehaviorElasticity Energy

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El. Boundaryu

El. Boundaryl

Initial El Energy =𝐸𝑙𝐵𝑜𝑢𝑛𝑑𝑎𝑟𝑦𝑢 − 𝐸𝑙𝐵𝑜𝑢𝑑𝑎𝑟𝑦𝑙

ElWork=𝑥∗𝐿𝑜𝑎𝑑𝑈𝑛𝑖𝑡(%)

𝐼𝐸𝑙𝐸𝑛𝑒𝑟𝑔𝑦

ElEnergy = 100− 𝐸𝑙𝑊𝑜𝑟𝑘𝑙𝑜𝑎𝑑 − 𝐸𝑙𝑊𝑜𝑟𝑘𝑖𝑑𝑙𝑒

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BackgroundStructuring monitoring information

15

Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, MELA: Monitoring and Analyzing Elasticity of Cloud Services, 5'th International

Conference on Cloud Computing, CloudCom. Bristol, UK, 2-5 December, 2013, http://dx.doi.org/10.1109/CloudCom.2013.18

VM 10.0.0.i

mi

VM 10.0.0.j

mi mj mk…

VM 10.0.0.k

mi mj mk……

Data Node Unit

Data Controller

Unit

Data End

Topology

…

Event Processing

Topology

Elastic

DaaS

…

VM 10.0.0.i

mi mj mk…

VM 10.0.0.j

mi

Service Unit Instance

Service Unit Instance

mi

mi

Custom aggregation

<rule> := operation "=>" metric

<operation>:= operator "(" operand { "," operand } ")"

<operator> := "+"|"-"|"*"|"/"|"AVG"|"MAX"|"MIN“

|"CONCAT"|"FIRST"|"LAST"|"SET"

<operand> := metric | number | string

Collect only relevant monitoring information,

and logically associate it to the service structure

NOTE:

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BackgroundDetermining complete requirements

16

Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, MELA: Monitoring and Analyzing Elasticity of Cloud Services, 5'th International

Conference on Cloud Computing, CloudCom. Bristol, UK, 2-5 December, 2013, http://dx.doi.org/10.1109/CloudCom.2013.18

Data Node Unit

Data Controller

Unit

Data End

Topology

…

Event Processing

Topology

Elastic

DaaS

mt1

ms

mi

mj

mk

…

ml

mp

…

mt2

User-Defined Elasticity Requirements

ms <= x mt1 > y

for each metric

when REQUIREMENTS are fulfilled

record Upper and Lower encountered values

…

Upper Boundary

Lower Boundary

Upper Boundary

Lower Boundary

? ?

?

?

?

?

Starting from a reduced set of initially known requirements, bridge requirements

knowledge gap by determining requirements for all service metrics

NOTE:

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Elasticity Relationships AnalysisSingle relationship analysis

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Depending on the Elasticity

Capability to be enforced,

determine relationships between

the metrics influenced by the

capability and other service

metrics

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Elasticity Relationships AnalysisComplete service relationships analysis

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Traverse structure tree and analyze relationships between:

• El. Metrics from same Elastic Element with same Level in same Sub-Tree

• El. Metrics from different Elastic Elements with same Level in same Sub-Tree

• El. Metrics from different Elastic Elements with same Level in different Sub-Tree

• El. Metrics from different Elastic Elements with different Level and same/different Sub-Tree

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Elasticity Relationships Analysis Framework

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Elasticity Relationships AnalysisPrototype

• Elasticity Relationships Analysis R-based Prototype

• Change Function

• Linear dependency function

• Delay Function

• Lag between time series

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𝑚𝑒𝑡𝑟𝑖𝑐(𝑡) ∶ 𝑒𝑙𝑒𝑚𝑒𝑛𝑡 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝑐𝑜𝑒𝑓𝑓𝑖 ∗ 𝑚𝑒𝑡𝑟𝑖𝑐𝑖 (𝑡) ∶ 𝑒𝑙𝑒𝑚𝑒𝑛𝑡𝑥 + …

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Elasticity Relationships AnalysisScenario

• Elastic Data-as-a-Service

• Runs in private cloud

• Bursts in public cloud

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Event Processing Topology Data End Topology

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responseTime:EventProcessing ≤ 100 ms cpuUsage:DataNode ≤ 90 %

Elasticity Relationships Analysis4 Experimental scenarios

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Elasticity Relationships AnalysisScenario 1: DaaS on private cloud

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A user would look at monitoring information and select what relationships s/he

would like to analyze

NOTE:

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From the determined relationship,

one can estimate what is the

maximum throughput achievable

before CPU usage on data end

becomes a bottleneck (e.g, 80-

90%), and design a predictive

controller accordingly

NOTE:

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Elasticity Relationships AnalysisScenario 2: DaaS on public cloud

Almost identical relationship also

discovered on the public cloud,

meaning the relationship is due to the

service implementation

NOTE:

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Elasticity Relationships AnalysisScenario 3: DaaS on multiple clouds

In the multi-cloud scenario, the

relationship is attenuated (i.e., 0.18 as

multiplicative constant instead of 0.29),

indicating that CPU usage is less of a

bottleneck (increases more slowly with

throughput)

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Elasticity Relationships AnalysisScenario 4: DaaS with elasticity controller


responseTime:EventProcessing ≤ 100 ms

cpuUsage:DataNode ≤ 90 %

+

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Elasticity Relationships AnalysisScenario 4: DaaS with elasticity controller


responseTime:EventProcessing ≤ 100 ms

cpuUsage:DataNode ≤ 90 %

+

Depending on individual goals, a user might be interested in other determined

relationships, such as that when a particular elasticity controller is used, the achieved

throughput is only half the connection rate in 0.54% of the cases (Adjusted r),

indicating a potential bottleneck in the service

NOTE:

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Conclusions• Concepts

• Elasticity Relationship

• Elasticity Energy/ Work

• Mechanisms

• Abstracting behavior of elastic cloud services w.r.t. requirements

• Analyzing elasticity relationships

• Framework/Tools

• MELA: Monitoring and analyzing elasticity of cloud services

(http://tuwiendsg.github.io/MELA/)

Work partially supported by the European Commission in terms of the CELAR FP7 project

(http://www.celarcloud.eu/)

Distributed Systems Group (http://dsg.tuwien.ac.at/)

Vienna University of Technology (http://www.tuwien.ac.at/)

35

http://tuwiendsg.github.io/MELA/

http://www.celarcloud.eu/

http://dsg.tuwien.ac.at/

http://www.tuwien.ac.at/

on analyzing elasticity relationships of cloud services

Education

services elasticity

elasticity of cloud

elasticity capabilities

data controller

cloud computing

event processing

data endcloudcom

data balancing tasks