A UML KPI Proﬁle for Energy Aware Design

and Monitoring of Cloud Services

Christophe Ponsard and Jean-Christophe Deprez

CETIC Research Centre, rue des Fr

eres Wright 29/3, Charleroi, Belgium

Keywords:

Energy Efﬁciency, Cloud, Sustainability, Green-IT, UML.

Abstract:

ICT energy efﬁciency is a growing concern. Large effort has already been spent making hardware energy

aware and improving hardware energy efﬁciency. Although effort is devoted to speciﬁc software areas like

embedded/mobile systems, much remains to be done at software level, especially for applications deployed

in the Cloud. In order to help Cloud application developers to learn to reason about how much energy is

consumed by their application on the server-side, we propose a framework composed of (1) a Goal-Question-

Metric analysis of energy goals, (2) a UML proﬁle for relating energy requirements and associated KPI metrics

to application design and deployment elements, and (3) an automated Cloud deployment of energy probes able

to monitor those KPI and aggregate them back to questions and goals. The focus of this short paper is on the

development of the UML proﬁle. We detail the proﬁle metamodel design and its implementation based on the

Open Source Papyrus modeller. We also report about the application of our proﬁle to a case study.

1 INTRODUCTION

ICT expansion both at professional and personal lev-

els induces increasingly larger amounts of data ex-

changed (high resolution pictures, videos) and pro-

cessed (Big Data), increasing connectivity of all de-

vices (mobile devices, Internet of Things) and higher

penetration (on business domains, emerging coun-

tries). This evolution raises the energy required to run

ICT to a level that would be dramatic if ICT energy ef-

ﬁciency was not improving simultaneously. Between

2007 and 2012, global ICT consumption raised from

3.4% to 4.6% of the overall energy consumption and

the ratio for data centres also grew from 1% to 1.3%

of the global energy consumption hence a 30% in-

crease (Internet Science NoE, 2013)

A reason for the slower increase between 2007

and 2012 is the use of more efﬁcient hardware. Virtu-

alisation techniques also enable data centres to oper-

ate hardware at higher load. The average Power Us-

age Effectiveness (PUE) metric of data centers cur-

rently ranges from from 1.7 to 1.1 for the most ef-

ﬁcient ones. This metric compares the amount of

energy spent by servers against the overall energy

consumed by the whole infrastructure, thus the best

theoretical PUE measurement is 1.0. In order to

reach another level of energy saving, it is now re-

quired to consider the software layer. While sev-

eral initiatives have already studied how to reduce en-

ergy consumption of mobile or embedded devices, lit-

tle has been done for improving the energy perfor-

mance of the service-side computation part of appli-

cation, in particular, in the context of Cloud comput-

ing. To bootstrap the process of energy based pric-

ing model for Cloud service at infrastructure, plat-

form or application level, it is necessary to develop

a Cloud stack capable to record energy consumption

at each layer and to facilitate negotiations between a

customer and a provider where energy consumption

is one of the factors. In a second step, self-adaptation

capabilities in the Cloud stack middleware or in the

Cloud applications can then enable dynamic energy

savings. Tools are also needed to help development

teams to learn how their applications consumes en-

ergy and how to refactor these applications to achieve

additional energy savings. These development tools

must therefore encompass all development phases in-

cluding requirements, design, workload testing, and

deployment. Work in this direction is actively pro-

gressing in the scope of several EU projects, e.g.

(ASCETIC, 2013), (ECO2Cloud, 2012), or (ENTRA,

2013).

Assuming such an energy-aware stack is avail-

able, it is necessary to help developers to learn how

much energy is consumed by their application on

the server-side. Unlike certain performance or se-

432

Ponsard C. and Deprez J..

A UML KPI Proﬁle for Energy Aware Design and Monitoring of Cloud Services.

DOI: 10.5220/0005564004320437

In Proceedings of the 10th International Conference on Software Engineering and Applications (ICSOFT-EA-2015), pages 432-437

ISBN: 978-989-758-114-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

curity characteristics already understood by users and

developers, energy consumption behavior of server-

side components is often completely unknown. Rare

are those who could state quantiﬁable requirements

on the energy consumption behavior on the server-

side of particular features of their application. The

aim of this paper is to provide the developer with tools

that will ease the following key steps to move to an

energy-aware cloud application development:

At Requirements Level - To structure the ap-

proach, the Goal-Question-Metric (GQM) paradigm

is used (Basili et al., 1994). In particular, it can be

used to propose generic goals and questions that de-

velopers will often want answers to in order to gain a

more precise knowledge of the energy consumed by

various features or components of their application. It

also makes the link with a number of already identi-

ﬁed energy-related metrics (Bozzelli et al., 2013).

At Design Level - To capture the information on

how to measure energy consumption of a feature or

component in a way that follows the traditional mod-

elling approach used by analysts and eases further

processing, the design model language must be aug-

mented with annotations to connect design element

to energy requirements (goals and questions). This

will enable the automated deployment of measure-

ment probes to monitor the speciﬁed KPI and report

them in terms of the questions and goals of the GQM

identiﬁed at requirements level. This short paper tar-

gets more speciﬁcally this step.

At Runtime Level - Probes collect the speciﬁed

data and report them to a monitoring infrastructure

part of the energy-aware Cloud stack. This monitor-

ing itself is efﬁcient in terms of data collection strat-

egy (frequency of sampling, data transmission, data

aggregation). Application monitoring occurs at the

SaaS level but relies on data from the lower PaaS and

IaaS layers, for example, for collecting Watt-hour of

a blade or CPU percentage time of a process running

in a virtual machine (VM).

For easing adoption by developers, it is also very

important to propose a practical tool that will seam-

lessly integrate with current development habits and

mainstream development environments. In this re-

spect the Uniﬁed Modelling Language (UML) is now

universally known by developers and supported by

development environments (OMG, 1997). Standard

extensions mechanisms, based on stereotypes, are

available to enrich the existing diagramming nota-

tions, in our case with energy-related information.

Such approaches have been quite successful in the

past, e.g. the MARTE proﬁle for embedded systems

(OMG, 2009). The proﬁle can be ”plugged” into an

existing model of a target application. If no model ex-

ists, a simpliﬁed energy-oriented model can be built

only for the concerned part of the application. It

will naturally help to capture all the relevant energy-

related elements and in a possible later step be used

as a basis to drive the application refactoring. In this

short article, we present a UML-based approach simi-

lar to the one followed by MARTE, but applied to en-

ergy related requirements and design decisions. Our

work is structured as follows. Section 2 details the

design of the proﬁle by explaining its meta-model. It

shows how energy goals and questions are captured

and how to add design annotations to specify energy

consumption measurements able to answer the given

questions. It also presents a reference implementa-

tion. Section 3 illustrates the proﬁle applied to a Photo

Album web-application. Section 4 discusses some re-

lated work. Finally, section 5 draws some conclu-

sions about our experience so far and identiﬁes further

work.

2 PROFILE DESIGN

To gather energy requirements, the design annotation

and mapping with deployment time probes, we aug-

mented UML with two stereotypes at different level of

granularity. A ﬁrst stereotype, preparedForMeasure-

ment, provides information to prepare a UML model

for a measurement session while a second stereotype,

forMeasurement, provides information on each appli-

cation elements relevant to measure (e.g. a method,

a class, a deployment element such as a service, a

VM,...). The deﬁnition of those two stereotypes also

relies on a number of auxiliary DataTypes and Enu-

merations. In the rest of this section, we will use italic

font to refer to concepts in the metamodel diagrams.

2.1 Stereotype for Measurement Session

The preparedForMeasurement stereotype is used to

specify global information related to monitoring

goals. It is depicted in Figure 1 Users can provide

general information on a model for specifying mon-

itoring needs. Notably, Information provided at the

model level relate to

1. the explicit speciﬁcation of MonitoringGoal and

associated QualityQuestions to answer. Stereo-

typed KPI modeled by user will then have to ex-

plicitly identify and deﬁne the questions (Ques-

tionID and QuestionText) they help to answer.

2. a set of information (GlobalNPIDefInput) to de-

ﬁne globally:

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433

Figure 1: Meta-model - Goal-Questions-Metrics.

• measures: global repository where probing in-

formation are found,

• workload: global repository where invocation

commands to exercise workloads on the appli-

cation are found,

• visualisation: global repository where visuali-

sation information are found.

The information location is implementation inde-

pendent but a URL to some conﬁguration reposi-

tory is expected, e.g. (Chef, 2009).

2.2 Stereotype for Measured Element

The forMeasurement stereotype can be attached to an

UML element on which measurement can be con-

ducted (statically or dynamically). The top of ﬁg-

ure 2 shows the standard UML element to which it

can be attached (operation, class, component, etc). To

address the need of dynamic measurements for those

Figure 2: Meta-model - KPI and Visualisation.

type of elements, it is necessary to deﬁne the forMea-

surement facet which is mainly composed of a list

of KPI deﬁnition and some technology speciﬁc infor-

mation that will be useful at deployment time. Each

KPI associated to a UML element is captured by the

KPIDefInput. In addition to a identifying KPIName

and KPIRepositoryURL, it contains a link to relevant

questions it addresses (at least one) and the following

deﬁning ﬁelds:

• MeasurementDef (mandatory): deﬁnes on what to

perform the measurement as KPIMeasureDefIn-

put. This scope can be ﬁnely speciﬁed using

strategies related to the container level (package,

class, method, process,...) or inheritance/call level

(self, self+children, self+children+called)

• WorkloadDef information (mandatory): needed

to conduct dynamic test sessions on an execution

environment to capture the desired measurement

for relevant workload categories, deﬁned using

the KPIWorkloadDefInput.

• VisualizationDef data (optional): to further pre-

cise dashboards and other visualization widgets

useful to present and to interpret measurement re-

sults. Note it is optional because all required in-

formation may already be present in the global

GlobalVisualizationDefInput.

2.3 Reference Implementation

Our reference implementation was developed on Pa-

pyrus, an Open Source Eclipse-based UML tool

(Eclipse Foundation, 2007). Papyrus supports the def-

inition of proﬁles through . pro f ile projects that can

be speciﬁed with the tool itself. They can then be

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434

applied to normal UML projects which will then ben-

eﬁt from the speciﬁed extension. Papyrus automat-

ically generates all the input forms required to cap-

tures the structured and typed information speciﬁed

in the proﬁle. Subsequently, different query technolo-

gies can be used to retrieve the energy-related infor-

mation encoded in an instantiated model. In our ref-

erence implementation, we used (Eclipse Foundation,

2006) which provides a nice declarative language to

transform the source UML model into some target

such as the monitoring deployment descriptor in an

OVF format for instance. Finally, the visualisation

and reporting currently rely on (BIRT, 2005).

3 PHOTO ALBUM CASE STUDY

3.1 Case Study Description

Photo Album is a 3-tier web application that is de-

signed to be desktop-like on-line photo manager (Tse-

bro et al., 2009). It provides social services for up-

loading photos, storing and previewing them, creating

albums and sharing them with other users. The visu-

alisation layer is implemented in JavaScript while the

business logic in Java runs on the server-side and a

database for storing issue data can run on the same

server or on a different machine. It is very representa-

tive of applications that can be deployed in SaaS mode

on a Cloud and that can beneﬁt of the PaaS and IaaS

layers elasticity/reconﬁgurability features.

3.2 GQM Energy Analysis

We restrict ourselves to a simple goal together with

related questions as described in Table 1. Those el-

ements are encoded into a preparedForMeasurement

entry which is directly attached the to the Photo Al-

bum UML design project.

3.3 Use Case Annotation

The UML Use Case diagram is the simplest to use

because it easily relates to business level services typ-

ically in the application server. Figure 3 shows the

two identiﬁed features of our GQM analysis.

Figure 3: Annotated Use Case for the Photo Album.

Table 1: Goal and question deﬁnition for the Photo Album.

Type ID Description

Goal EE Study the impact of executing the following features

and components of Photo Album on the energy con-

sumption to determine if a refactoring effort is worth

undertaking:

• Upload a multimedia item in an album

• Compile an album

Questions EE TQ1 What is the overall energy consumed when exercis-

ing the given feature or component for each workload

category?

EE SQ1 How does the energy consumed every second varies

when exercising the given feature or component for

each workload category?

... ...

The ﬁrst use case Upload a multimedia item is an-

notated with a forMeasurement stereotype. This was

done using the KPIDefInput partly shown in Figure 4.

It is worth noting that such appropriate dialogue win-

dows are automatically generated by Papyrus from in-

formation described in the meta-model.

Figure 4: KPI Deﬁnition for photo upload.

For the PhotoAlbum Use Case, the recommended

measurement strategy is coarse grained, i.e. measur-

ing all the contained elements and following both the

inheritance and call graphs. Note that the fullName

ﬁeld can accept a speciﬁc language for specifying the

deployment target to monitor with some facilities like

regular expression. It is used to specify a upload

entry-point method and a samba process performing

the ﬁle upload.

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3.4 Deployment Annotations and

Monitoring Process

Figure 5 shows a deployment view. It should nor-

mally only show the photo album related VM, i.e. the

application VM PA-APP-VM and the database VM)

PA-BD-VM along side the Test VM is used to inject

speciﬁc workloads in a controlled way on the applica-

tion under energy monitoring. However, to give some

insight on the monitoring process, Figure 5 also rep-

resents the infrastructure VMs managing the energy

monitoring: the SaaS Modelling VM offers developer

front-end tools such as Papyrus, aggregation, report-

ing and visualisation tools and the PaaS Infrastructure

VM runs a global efﬁcient monitoring service.

Regarding the deployment process, information

for generating the probe descriptor and the test load

speciﬁcation is extracted from the UML model using

Acceleo. Those elements are then passed respectively

to the probe deployment and load generator services.

Beside application features of components, as il-

lustrated earlier, a UML model can also use annotated

application VMs with forMeasurement information,

for example to capture VM level monitoring and mea-

sure the impact of speciﬁc Cloud architectural com-

ponents, in our case, it could be used to determine

the energy consumption of a load balancer which dis-

tributes the load to keep good response times and

therefore identify explicitly a time-energy trade-off

that could take place.

3.5 Reporting the Results

Figure 6 shows a typical report generated from BIRT.

It shows historical data gathered from the monitor-

Figure 5: Deployment View.

Figure 6: Reporting at KPI level (partial).

ing service on virtualised components such as CPU,

IO, memory access. Those are transparently trans-

lated into Watt history and total Joule consumption

based on an energy model. The link with the re-

lated question and goal is also reported. The data can

then be analysed, for example to correlate the Watt

consumption with some element (CPU, network ac-

cess...) and/or some speciﬁc load event. Based on

this measurement information, bottlenecks could be

identiﬁed and improvements to a speciﬁc service or

more globally to an architecture refactoring can be

triggered.

4 RELATED WORK

As already mentioned, much work has been devoted

to energy efﬁciency in the embedded domain given

the limited resource available. The MARTE proﬁle is

capturing a lot of resource categories, including en-

ergy (OMG, 2009). In a SysML context, they can

be related to requirements however the proﬁle does

not support our richer traceability to KPI and based

on a systematic GQM approach. Further extensions

dealing more speciﬁcally with energy have been de-

veloped (Shorin and Zimmermann, 2013). Their aim

is more directed towards design time energy estima-

tion rather than runtime monitoring and evolution as

ours.

Concerning energy requirements and goals, there

is little room for them in the current analysis frame-

work: standards structuring non-functional require-

ments like the ISO2910 and even the more recent

SQuaRE essentially captures them from a perfor-

ICSOFT-EA2015-10thInternationalConferenceonSoftwareEngineeringandApplications

436

mance efﬁciency point of view and do not enable any

reasoning on them. As pointed by a Microsoft report,

most of the time energy consideration are simply not

present in software speciﬁcations except for speciﬁc

systems like embedded sytems and fail to address fun-

damental issues such as the available power budget,

the impact of energy in the selection of a speciﬁc de-

sign, or the resolution of conﬂicts with other require-

ments. At organisation level, the situation is different

with a current trends to help organisation in their en-

ergy management strategies, for example using goal-

oriented techniques (Stefan et al., 2011). Although

this work seems farther away from the context pre-

sented in this article, the framework presented could

well be used to collect measurement data needed as

evidence for compliance to (ISO 50001, 2011) on En-

ergy Management.

5 CONCLUSIONS AND FUTURE

WORK

In this short paper, we presented a UML proﬁle for

the energy-aware design of Cloud application both in

relation with a well deﬁned energy management strat-

egy deﬁned in terms of goals and questions but also

in relation with well-deﬁned and monitorable KPI.

We illustrated our work on a typical 3-tier applica-

tion. So far, we were able to perform some partial

deployment and data collection experiments on a re-

alistic case study. Although those are not yet fully

automated, we could perform an aggregation of the

collected data as measure of KPI satisfaction and link

it to the question and goal levels.

We are currently validating our KPI proﬁle on

a large real world application: a end-to end mul-

timedia cross-channel solution for sharing informa-

tion across news agencies, broadcasters and publish-

ers (ATC, 2014). This case study will allow us to

better evaluate the ability of our KPI proﬁle to cap-

ture all the energy-relevant aspects, especially going

beyond CPU consumption, e.g. communication and

storage. From there, we will also be able to reason

on some possible design trade-offs. We also plan to

improve the automation of probe deployment and for-

malise some part of the proﬁle in order to provide easy

mapping on popular automation frameworks such as

Chef. Concerning the reporting, we will replace our

current static BIRT-based prototype by a more inter-

active web-based framework that will ease our anal-

ysis. A later step will be to further enrich our proﬁle

with self-adaptation strategies than can preserve en-

ergy goals even when the execution context is evolv-

ing due to evolution in load, network throughput, in-

frastructure costs, etc.

ACKNOWLEDGEMENTS

This work was partly funded by the European Com-

mission under the FP7 ASCETiC project (nr 610874).

Many thanks to the ASCETiC partners for their feed-

back and support.

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