Consolidation of Performance and Workload Models in Evolving Cloud

Application Topologies

Santiago G

omez S

aez, Vasilios Andrikopoulos and Frank Leymann

Institute of Architecture of Application Systems, University of Stuttgart, Universit

atsstr. 38, Stuttgart, Germany

Keywords:

Cloud Application Topology, Cloud Application Distribution, Cloud Application Performance Engineering,

Cloud Application Workload.

Abstract:

The increase of available Cloud services and providers has contributed to accelerate the development and has

broaden the possibilities for building and provisioning Cloud applications in heterogeneous Cloud environments.

The necessity for satisfying business and operational requirements in an agile and rapid manner has created the

need for adapting traditional methods and tooling support for building and provisioning Cloud applications.

Focusing on the application’s performance and its evolution, we observe a lack of support for specifying,

capturing, analyzing, and reasoning on the impact of using different Cloud services and conﬁgurations. This

paper bridges such a gap by proposing the conceptual and tooling support to enhance Cloud application topology

models to capture and analyze the evolution of the application’s performance. The tooling support is built

upon an existing modeling environment, which is subsequently evaluated using the MediaWiki (Wikipedia)

application and its realistic workload.

1 INTRODUCTION

The existence of a wide technological landscape of-

fered in the Everything-as-a-Service (*aaS) model has

contributed to an increase of applications partially or

completely running or built in the Cloud, potentially as

a composition of Cloud services (Andrikopoulos et al.,

2013). The adoption of continuous software delivery

models, such as DevOps, aim at offering ﬂexibility

and agility for a quick response to market changes.

The DevOps emergence boosted efforts in research

and industry towards developing concepts and tools

to assist application developers to develop, provision,

and (re)deploy cloud applications in a simpliﬁed, in-

teroperable, and agile manner.

Standards like TOSCA

allow the automated and

interoperable provisioning and conﬁguration of Cloud

services to host the different application components.

However, there exists a lack of native support for as-

sisting application developers in the selection and con-

ﬁguration of appropriate Cloud services based on a set

of business and operational objectives. When focusing

on the performance of the application, such an analysis

becomes even more complex and wider, as (i) the ﬂuc-

Topology and Orchestration Speciﬁcation for Cloud

Applications (TOSCA) Version 1.0: http://docs.oasis-

open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.html

tuation of the application workload has an impact on

the resources demand and QoS, and (ii) the existence

of multiple applications running on the same physical

environment of a provider has an unpredictable im-

pact on the offered performance (G

omez S

aez et al.,

2015). Towards narrowing such a gap, in (G

omez S

aez

et al., 2014) we proposed a process-based approach

aimed at assisting application developers to efﬁciently

(re)distribute

their application components spanning

multiple Cloud offerings while focusing on ﬂuctuating

and evolving workloads and performance demands.

This work materializes the vision described in (G

omez

aez et al., 2014) by developing the concepts for con-

solidating performance aspects in Cloud application

topologies. The main contributions of this work are:

the derivation of a life cycle targeting the speciﬁca-

tion, analysis, and adaptation of Cloud applications

for evolving workloads and performance require-

ments,

the development of the foundations to enrich Cloud

application topology models with evolving busi-

ness and operational requirements, and workload

behavioral models,

its corresponding tooling support built atop the

160

Sáez, S., Andrikopoulos, V. and Leymann, F.

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies.

In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1, pages 160-169

ISBN: 978-989-758-182-3

TOSCA

and OpenTOSCA

speciﬁcation and

ecosystem, respectively, and

the evaluation of the proposed approach using the

well known MediaWiki

application and its realis-

tic workload.

The remainder of the paper is structured as fol-

lows. Section 2 presents the background this work

builds upon. The proposed life cycle for application

(re)distribution, and the meta-models for enriching

Cloud application topologies with evolving perfor-

mance and workload models are presented in Section 3.

The tooling support is presented in Section 4, which is

then evaluated in Section 5. Section 6 presents related

works, and Section 7 conclusions and future work.

2 MOTIVATION &

BACKGROUND

MWiki_App:

PHP_App

PHP_Module:

PHP_Container

Ubuntu_14.04LTS:

Virt_Linux

MWiki App:

Web_App

IBM_Server:

Physical_Server

wikiDB: SQL_DB

consists_of

Ubuntu_14.04LTS:

Virt_Linux

MySQL:

SQL_DBaaS

AWS_EC2_m3.

xlarge: AWS_EC2

AWS_RDS_m3.

large: AWS_RDS

Physical Server IaaS Solution DBaaS Solution

AWS_EBS_gp2:

Distr_Storage

uses

interacts_with

AWS_EC2_t2.

medium:AWS_EC2

AWS_EBS_gp2:

Distr_Storage

uses

KPI

alt_hosted_on

hosted_on

Alternative Topology Node

Topology Node

Application-specific

Topology Node

KPI

Performance Indicator

Workload Specification

Apache_Server:

Web_Server

MySQL: SQL_

RDB_Server

Figure 1: MediaWiki Application Topology Model.

Figure 1 depicts multiple viable distributions of the

two-tiered PHP-based MediaWiki

application. The

MediaWiki topology breaks the application stack in

two main component groups: (i) application speciﬁc

components, i.e. each application tier description, and

(ii) the application independent sub-topology/ies (i.e.

the

- and

-topologies in the terminology of (An-

drikopoulos et al., 2014a), respectively). The avail-

able number and nature of Cloud offerings allows for

a partial or complete distribution of the application

components, therefore building a wide spectrum of

alternative

-topologies (Andrikopoulos et al., 2014a).

TOSCA: http://docs.oasis-open.org/tosca/TOSCA/v1.0/

os/TOSCA-v1.0-os.html

OpenTOSCA: http://www.iaas.uni-stuttgart.de/Open

TOSCA/

MediaWiki Application: https://www.mediawiki.org/

wiki/MediaWiki

WikiMedia Foundation: https://wikimediafounda

tion.org/wiki/Home

For instance, it is possible to outsource the MediaWiki

front-end tier to a VM-based offering, such as Ama-

zon EC2

, and migrate its backend database tier to an

off-premise EC2 VM or in an Amazon RDS

database

instance.

Collaborative Loop

Figure 2: Performance Aware Cloud Application

(Re)Distribution Process (G

omez S

aez, 2014).

The existence of such a spectrum of alternative

-topologies together with the necessity to rapidly sat-

isfy changing business and operational requirements

introduces a multi-dimensional problem involving the

analysis and revision of requirements and objectives.

More speciﬁcally, it involves the evaluation of the

trade-off between two or more dimensions, e.g. cost

vs. performance, etc., during the modeling and pro-

duction phases of the application. Towards bridging

such a gap, in (G

omez S

aez et al., 2014) and (An-

drikopoulos et al., 2014a) we proposed a methodology

and formal framework, respectively. Such approaches

aim at allowing developers and operation engineers

to distribute and redistribute their application compo-

nents spanned among multiple Clouds to cope with

changing business and operational requirements, and

ﬂuctuating workloads.

The Performance Aware Cloud (Re)Distribution

Process depicted in Figure 2 consists of several tasks:

(i) modeling the application topology, (ii) enriching

such topology with business and operational require-

ments, and the workload characteristics, which are

then (iii) processed and analyzed to subsequently

(iv) discover, construct, and evaluate alternative

topologies. The (v) deployment and production phase

of the application assists in building the necessary

knowledge to (vi) analyze the evolution of the appli-

AWS EC2: http://aws.amazon.com/ec2/

AWS RDS: http://aws.amazon.com/rds/

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

161

cation performance demands and workload behavior

through monitoring techniques. The Collaborative

Loop gears towards supporting the

(re-)distribution

the application over time to rapidly react to changing

requirements and ﬂuctuating workloads. This research

paper provides the conceptual and tooling support to-

wards supporting the Modeling and Enrichment tasks

in Figure 2.

3 PERFORMANCE-AWARE

MODELING & ENRICHMENT

OF CLOUD APPLICATIONS

This section presents two fundamental aspects that

must be taken into consideration for achieving an agile

(re)distribution of Cloud applications spanning mul-

tiple Clouds: (i) the development of a life cycle for

selecting and conﬁguring Cloud resources to satisfy

application business and operational requirements, and

(ii) the derivation of the necessary foundations in the

Modeling and Enrichment tasks depicted of the Per-

formance Aware Cloud Application (Re)Distribution

Process to support the different phases of the life cycle.

3.1 Life Cycle

Santiago Gómez Sáez

KPI Specification

Workload Model

Derivation

Workload Evolution &

Characterization

Top-down

Bottom-up

Resources Selection &

Configuration

Figure 3: Application (Re)Distribution Life Cycle.

The speciﬁcation and analysis of the application busi-

ness and operational performance require to consider

two aspects: (i) the difference between the required

and offered performance, and (ii) the evolution of the

application workload behavior. According to several

investigations (Bahga and Madisetti, 2011; Gmach

et al., 2007; John et al., 1998; Mian et al., 2013;

Watson et al., 2010), two approaches can be derived

for designing and provisioning adaptable Cloud ap-

plications w.r.t. changing business and operational

requirements, and ﬂuctuating workloads: top-down

and bottom-up. The life cycle depicted in Figure 3

supports the (re)distribution of applications w.r.t. their

business and operational requirements. A ﬁrst phase

consists of the KPI Speciﬁcation, by means of deﬁning

and specifying the business and operational require-

ments. Together with the speciﬁcation and analysis

of the application workload in the Workload Model

Derivation phase, the resources can be selected and

conﬁgured in the Resources Selection & Conﬁguration

phase. However, the previous phases do not directly en-

able the analysis of the workload ﬂuctuation. Towards

such a goal, the Workload Evolution & Characteriza-

tion phase allows to analyze the workload evolution

during the application’s production phase, using, e.g.

monitoring techniques.

The execution of the previous phases in the top-

down approach empowers the allocation of resources

to satisfy business and operational requirements. The

bottom-up approach builds on the adaptation of re-

sources and in the reﬁnement of business and opera-

tional requirements. The bottom-up approach mainly

builds towards deriving the optimal resource alloca-

tion and conﬁguration w.r.t. the application perfor-

mance (Mian et al., 2013). In this work we build

towards the consolidation of the top-down and bottom-

up application workload analysis approaches over time

in order to proactively satisfy application demands by

dynamically (re-)adapting its topology.

3.2 KPI Requirements Speciﬁcation

The speciﬁcation of business and operational require-

ments typically relate to the application’s business

KPIs, e.g. expected proﬁt, maximum accepted latency

to ensure a user’s satisfaction level, etc. The meta-

model depicted in Figure 4 establishes the placeholders

for the speciﬁcation and the analysis of adaptive per-

formance requirements in the KPI Speciﬁcation phase

of the Application (Re)Distribution Life Cycle. Perfor-

mance Requirements can be partitioned in two main

correlated groups (see Figure 4): Operational Require-

ments and Business Requirements. Operational Re-

quirements relate to the provisioning and conﬁguration

of resources, etc., while the Business Requirements re-

late to the business objectives, e.g. expected revenue

per user, maximum expenditure, etc. Both Business

and Operational Requirements have one overlapping

characteristic: Metrics can be used to quantitatively an-

alyze and evaluate the fulﬁllment of such requirements

(see Figure 4).

In order to simplify the selection of performance

metrics, these can be classiﬁed and organized in the

proposed meta-model within different Metric Cate-

CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science

162

Performance Demand

Evolution

Application Workload

Profile

Workload

- timeInterval

Metric

Category

Metric

- name

- description

Measure Sample

- size

- time interval

Application Workload

Performance Requirement &

Observation

Workload Mix

1..*

Behavior

Model

1..*

Application

Model

1..*

Application State

1..*

influences

1..*

impact_on

Defined as a Probabilistic

Distribution Model, e.g.

normal, poisson, etc.

Workload Operations

Ocurrences Probabilities

Figure 5: Workload Behavior Speciﬁcation.

the application are distributed within a Workload Mix,

based on, e.g. popularity, probability of occurrence,

etc., each having an impact on every Application State

of the Application Model. For example, transitions

between Application States originate in the execution

of the user’s requests, e.g. log-in operations, Wiki

page search, etc. The distribution of requests within

the workload mix and the distribution of users over

time deﬁne a Behavioral Model, i.e. a statistical model

representing the behavioral characteristics of users and

requests, which can be leveraged in order to drive sta-

tistical analyses, categorizations, or estimations.

3.4 Cloud Resources Selection &

Reconﬁguration

In this section we investigate how Cloud application

topology models can be enhanced with the previously

depicted knowledge. Since Cloud application topolo-

gies describe the set of components, services, and

the relationships among them, the enrichment support

must take into consideration the different levels where

Cloud application topology models can be enriched.

The remaining of this section outlines in a ﬁned granu-

lar manner such possibilities.

Due to the generic nature of GENTL among dif-

ferent Cloud application topology languages, we use

it as the basis for analyzing the modeling and enrich-

ment points of Cloud application topology models (An-

drikopoulos et al., 2014b) (see Figure 6). KPI require-

ments and workload behavioral characteristics can be

fundamentally speciﬁed in two granular ways. A ﬁned

granular description consits of decomposing and de-

scribing the performance requirements and workload

characteristics, respectively, e.g. on the topology Com-

ponent or Relationship levels. For instance, the usage

of different storage services to store application data,

such as AWS S3

or AWS RDS, may require ﬁned

granular description of the workload operations for

each topology Component independently, due to the

different nature of such data services. Relations among

the topology components depicted as Connectors have

a signiﬁcant impact in the overall performance of ap-

AWS S3: http://aws.amazon.com/s3

CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science

164

Performance Demand

Evolution

Cloud Application

Component

Group

Connector

Topology

1..1

target

source

1..1

0..1 0..1

0..1

Application Workload Profile

Metric Category

Requirement

- threshold

1..*

Figure 6: Application Topology Enrichment with Performance and Workload Models.

plications, as they are usually impacted by the network

conﬁguration and characteristics. The speciﬁcation of

performance information in sub-topologies enables to

group reusable Cloud components and services with

common KPI requirements and workload behavioral

characteristics.

The enhancement of Cloud application topologies

with KPI requirements and workload information can

be leveraged during the design, provisioning, and pro-

duction phases of the application for predicting and

analyzing the performance of Cloud offerings. Such in-

formation can be leveraged in future decision making

tasks towards assisting application developers to select,

conﬁgure, and dynamically adapt Cloud resources.

4 ARCHITECTURE &

IMPLEMENTATION

In the following we discuss the tooling support to

support Cloud application developers in the Modeling

and Enrichment tasks of performance-aware Cloud

application topologies. Due to the high adoption of

the TOSCA standard in both the industry and research

domain, we build the technological support atop of the

TOSCA speciﬁcation and its corresponding modeling

environment OpenTOSCA Winery (Kopp et al., 2013).

PERFinery - an OpenTOSCA Winery Extension

PERFinery

is a TOSCA-based modeling environ-

ment geared towards the enrichment of TOSCA-based

Cloud application topologies with evolutionary perfor-

mance requirements and workload models.

The speciﬁcation of non-functional requirements

of Cloud applications is supported in TOSCA by at-

taching custom policies to the application topology,

typically conforming to the Policy4TOSCA deﬁni-

tion (Waizenegger et al., 2013). Policy4TOSCA en-

ables the deﬁnition of Policy Types and Policy Tem-

plates comprising actions which must be performed

at concrete phases of the application life cycle and

on speciﬁc layers of the application. However, Pol-

icy4TOSCA lacks of tooling support and does not

capture evolving performance information, such as

the inﬂuence of workloads on the fulﬁllment of KPI

requirements. The topology enrichment support in

this work empowers the current TOSCA policy def-

inition support by enabling the graphical creation of

custom Policies comprising the different measurable

and analyzable application requirements.

Figure 7 depicts the architecture of PERFinery, and

highlights the components that have been extended in

Winery. The Topology Elements Manager, Topology

and Plan Modeler, and the Repositories are the major

components (see Figure 7). The Topology Elements

PERFinery Modeling Environment: http://www.iaas.

uni-stuttgart.de/PERFinery/

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

165

Topology

Modeler

Services

Manager

GUI

Workload

Manager

GUI

Policy

Manager

GUI

Repository Manager

CSAR ImporterCSAR Exporter

Workload

Repository

Topology

Repository

Policy

Repository

Figure 7: PERFinery Modeling Environment Architecture

(extended from (Kopp et al., 2013)).

Manager enables the administration and management

of reusable artifacts among multiple topology tem-

plates, e.g. Node Types, Relationship Types, or Deploy-

ment Artifacts. The Topology Modeler provides the

modeling artifacts towards designing and visualizing

Cloud application topologies. PERFinery comprises

repositories for storing Cloud application topology

models and reusable artifacts. The modeling elements

are provided through the Topology Modeler and Ele-

ment Manager client-side GUIs developed using Java

Web technologies and HTML5 (see Figure 7). More-

over, a REST interface is also offered atop of the Topol-

ogy Repository for persisting and retrieving TOSCA

artifacts, e.g. CSAR packages. The generated TOSCA

and Policy4TOSCA templates, and workload speciﬁ-

cations, as well as the corresponding CSAR packages,

are persisted in separate repositories. The persistence

of workload models is driven and persisted in an inde-

pendent Workload Repository. Generated workloads

can be used towards enriching modeled application

topologies with their behavioral characteristics, e.g.

user arrival rate, transactions’ mix and distribution of

requests, etc.

Application topology and workload models can be

created, viewed, and modiﬁed by navigating through

the repository and the different sections that PERFin-

ery offers. For instance, Figure 8 depicts the topology

model created for the MediaWiki application used for

the motivation of this work, which is subsequently

enhanced with performance information.

5 EVALUATION

We evaluate our approach using the MediaWiki

(Wikipedia) application and its realistic workload pro-

vided in (Urdaneta et al., 2009). The MediaWiki front-

end tier encapsulates the presentation and business

Table 1: Workload Characteristics.

Operation Type #Requests Ratio

99.96%

Write 75

0.04%

Image Retrieval 8971

4.49%

Skins Retrieval 66926

33.46%

Wiki Pages Retrieval 106347

53.17%

Others 7634

3.81%

logic layers, while its back-end provides its persis-

tency mechanisms. We used the workload provided

in Wikibench

for describing the workload. Figure 8

shows the view for building custom performance Pol-

icy Proﬁles.

W.r.t. the workload speciﬁcation, a ﬁrst step con-

sisted of sampling 200K HTTP requests of the original

WikiBench workload describing the characteristics de-

picted in Table 1. However, such a workload sample is

not distributed among the different users. For this pur-

pose, the workload sample is referenced in PERFinery

together with the speciﬁcation of its behavioral char-

acteristics, as depicted in Figure 9, respectively. More

speciﬁcally, the workload model is speciﬁed in CSV

format and comprises multiple users arriving at differ-

ent time intervals and executing a set of requests mixes

deﬁned in the workload sample.

The extensions realized in Winery serve as the ba-

sis for enriching TOSCA topologies with performance

information. More speciﬁcally, it provides Cloud ap-

plication developers with the means to graphically in-

clude QoS information as TOSCA policies and work-

load descriptors.

6 RELATED WORK

Most analytical approaches and frameworks in the

literature focus on combining operational expenses

analysis with one or more dimensions pertaining to

performance as part of their mission to support Cloud

application developers.

DADL is presented in (Mirkovic et al., 2010) as

a language to describe the architecture, behavior and

needs of a distributed application to be deployed in

the Cloud, as well as describing available Cloud offer-

ings for matching purposes. Similarly, in (Antonescu

et al., 2012), the authors propose a policy and action-

based approach that matches and dynamically adapts

the allocation of infrastructure resources to an appli-

cation topology in order to ensure SLAs. The Cloud-

Mig (Frey and Hasselbring, 2011) approach builds

Wikibench Project: http://www.wikibench.eu/

CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science

166

Figure 8: PERFinery Modeler — Speciﬁcation of Performance Requirements.

Figure 9: PERFinery Modeler — Workload Behavioral

Model Speciﬁcation.

on an initial topology and utilization model of the ap-

plication that is mapped or adapted through model

transformation in order to optimize the distribution of

the application across Cloud offerings. The MODA-

Cloud (di Nitto et al., 2013) and CloudML (Brandtzæg

et al., 2012) projects focus on providing a multi-

dimensional early design support of applications by

applying model transformation techniques and code

generation for multi-cloud applications. Further multi-

cloud application distribution approaches are targeted

by the SeaClouds EU Project

, by means of providing

SeaClouds EU Project: http://www.seaclouds-

project.eu/project.html

a Cloud Service Orchestrator capable of provision-

ing and managing application components spanned

among multiple Cloud environments (Brogi et al.,

2014). The CACTOS EU Project

is possibly the

closest approach to the fundamentals developed as

part of this work. The CACTOS environment aims at

ﬁtting resources within a provider for diverse appli-

cation workloads. However, all previous approaches

introduce complex tasks, e.g. creation of simulation

models, which often require the intervention of domain

experts, causing an overhead in the development and

(re)deployment tasks of applications. Moreover, the re-

lationship of topology models with varying application

workloads is not yet fully covered.

Further optimization of distribution of applications

like the Palladio-based approach discussed in (Miglie-

rina et al., 2013) aims at optimizing for availability

and operational expenses. Moreover, optimization

mechanisms are based on simulation techniques requir-

ing the deﬁnition of their corresponding models. The

MOCCA framework (Leymann et al., 2011) deals with

the same problem by introducing variability points in

the application topology in order to cope with possi-

ble alternative deployment topologies. CMotion (Binz

et al., 2011) uses an approach based on topology mod-

eling, generation of alternative topologies, and conse-

quent evaluation and selection of one of those alter-

Cactos EU Project: http://www.cactosfp7.eu/

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

167

natives based on multiple criteria. The work in (An-

drikopoulos et al., 2014a) uses the notion of typed

graphs for similar purposes and proposes a formal

framework to support this effort. In a similar approach,

MADCAT (Inzinger et al., 2014) incorporates to the

topology model scalability elements, and reﬁnes the

topology model from a high-level application topology

to a ready for deployment one.

The vision this paper pursues aims at leveraging

existing non-functional requirement speciﬁcation ap-

proaches, such as the ones previously discussed, to-

wards providing the conceptual foundations to specify

the performance aspects and analyze the impact of

ﬂuctuating workloads for various Cloud application

distribution and conﬁguration alternatives in a simpli-

ﬁed manner by enriching Cloud application topology

models.

7 CONCLUSIONS AND FUTURE

WORK

The heterogeneity of available Cloud services has be-

come a challenge for application developers when con-

sidering a rapid and efﬁcient selection and conﬁgura-

tion of Cloud offerings. Application components can

be distributed or replaced by different Cloud services,

potentially spanned among multiple Clouds. Focus-

ing on the business and operational performance of

Cloud applications, there currently exists a lack of

modeling and decision making support for capturing,

analyzing, and assessing when migrating, conﬁguring,

and utilizing different Cloud services under ﬂuctuating

workloads and intermittent QoS levels.

The assessment of, and guidance in the distribu-

tion of multi-Cloud applications is the core motivation

behind this work. We build towards enabling the efﬁ-

cient (re-)distribution of Cloud applications by means

of selecting and conﬁguring Cloud offerings to cope

with ﬂuctuating workloads and evolving performance

demands. The ﬁrst step towards such a goal is covered

in this work by providing the means for the enhance-

ment of application deployment models with perfor-

mance information and workload behavioral charac-

teristics. This work tackles the various phases of our

proposed application performance-aware application

(re)distribution life cycle by establishing the founda-

tions and tooling support for enhancing Cloud appli-

cation topology models with evolving performance

requirements and workload models. For this purpose,

we propose a conceptual model aimed at enriching

Cloud application topologies with evolving perfor-

mance requirements and workload behavioral char-

acteristics, which can used as the basis for capturing

and analyzing the performance and workload evolution

when distributing the application components among

different Cloud services. The technological support

developed in this work builds upon the TOSCA and

Policy4TOSCA speciﬁcations, and its corresponding

tooling support is built atop the Winery modeling envi-

ronment of the OpenTOSCA ecosystem, which is then

evaluated using the well known MediaWiki application

and its realistic workload.

Future investigations are aligned to the develop-

ment process of the tool chain depicted in (G

omez

aez et al., 2014), and to reuse or realize, when deemed

necessary, the concepts and instrumentation support

to gather, aggregate, and automate the analysis and

application (re-)distribution assessment tasks.

ACKNOWLEDGEMENTS

This work is partially funded by the FP7 EU-FET

project 600792 ALLOW Ensembles and the German

610872 DFG Project SitOPT.

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