A Conceptual Framework Supporting Pattern Design Selection for

Scientiﬁc Workﬂow Applications in Cloud Computing

Ehab Nabiel Alkhanak

1 a

, Saif Ur Rehman Khan

2 b

, Alexander Verbraeck

3 c

and Hans van Lint

1 d

Transport and Planning Department, Faculty of Civil Engineering and Geosciences (CiTG),

Delft University of Technology (TU Delft), Delft, The Netherlands

Department of Computer Science, COMSATS University Islmabad (CUI), Islamabad, Pakistan

Department Multi-actor Systems, Faculty of Technology, Policy and Management (TPM),

Delft University of Technology (TU Delft), Delft, The Netherlands

Keywords:

Scientiﬁc Workﬂow Application, Workﬂow Management System, Design Patterns, Cloud Computing

Environment.

Abstract:

Scientiﬁc Workﬂow Applications (SWFA) play a vital role for both service consumers and service providers

in designing and implementing large and complex scientiﬁc processes. Previously, researchers used parallel

and distributed computing technologies, such as utility and grid computing to execute the SWFAs, these

technologies provide limited utilization for the shared resources. In contrast, the scalability and ﬂexibility

challenges are better handled by using cloud-computing technologies for SWFA. Since cloud computing offers

a technology that can signiﬁcantly utilize the amounts of storage space and computing resources necessary for

processing large-size and complex SWFAs. The workﬂow pattern design has provided the facility of re-using

previously developed workﬂow solutions that enable the developers to adopt them for the considered SWFA.

Inspired by this, the researchers have adopted several patterns of design to better design the SWFA. Effective

pattern design that can consider challenges that may not become visible only in the implementation stage

of a SWFA. However, the selection of the most effective pattern design in accordance with an execution

method, data size, and problem complexity of a SWFA remains a challenging task. Motivated by this, we

have proposed a conceptual framework that facilitates in recommending a suitable pattern design based on the

quality requirements and capabilities are given and advertised by cloud consumers and providers, respectively.

Finally, guidelines to assist in a smooth migrating of SWFA from other computation paradigms to cloud

computing.

1 INTRODUCTION

The workﬂow has been deﬁned by Workﬂow Man-

agement Coalition (WfMC) as “The automation of a

business process, in whole or part, during which doc-

uments, information or tasks are passed from one par-

ticipant to another for action, according to a set of pro-

cedural rules” (Hollingsworth and Hampshire, 1993).

In this research context, a workﬂow can be deﬁned

as a series of processes and these processes represent

one or several computational tasks based on their de-

pendencies. These computational tasks can be any

executable instances (e.g., load sets, report sets, pro-

https://orcid.org/0000-0002-9880-6365

https://orcid.org/0000-0002-9643-6858

https://orcid.org/0000-0002-1572-0997

https://orcid.org/0000-0003-1493-6750

grams, and data) with different structures (e.g., jobs,

pipeline, data distribution, data aggregation, and data

redistribution). Generally speaking, a Workﬂow Ap-

plication (WFA) acts like software that automatically

handles scientiﬁc or business-related jobs by creat-

ing and initiating these instances while the Workﬂow

Management Service offers a certain API to facilitate

the management of these processes. Historically, re-

searchers have emphasized workﬂow applications for

a business domain (e.g., banking systems, transaction

systems). Developers need to consider large-scale,

fault-tolerant, complex, and maintainable scientiﬁc

processes when they need to deal with scientiﬁc work-

ﬂows. Some of the main application areas of Sci-

entiﬁc Workﬂow Applications (SWFA) are Bioinfor-

matics, Geoinformatics, Cheminformatics, Biomedi-

cal Informatics, and Astrophysics (Pan et al., 2019).

Alkhanak, E., Khan, S., Verbraeck, A. and van Lint, H.

A Conceptual Framework Supporting Pattern Design Selection for Scientiﬁc Workﬂow Applications in Cloud Computing.

DOI: 10.5220/0008916102290236

In Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020), pages 229-236

ISBN: 978-989-758-424-4

229

Scientiﬁc Workﬂow Management Systems (SWFMS)

like Pegasus SWFMS (Deelman et al., 2015) com-

prise a set of components (including Mapper, Local

execution engine, Job scheduler, Monitoring compo-

nent) that focus on automating the processes of large-

size data. SWFMS provides a graphical environment

that supports the reuse and integration of domain-

related workﬂows. Ultimately, SWFMS facilitate sci-

entists in performing their domain-speciﬁc workﬂows

by analysing given dataset(s) and visualizing the re-

sult of the processing of the dataset(s).

Previously, researchers have used parallel and grid

computing resources to execute SWFAs (Dong, 2009;

Juve et al., 2013). Today’s SWFAs generate a large

amount of data and due to this huge increase in size

and complexity, parallel and grid computing tech-

nologies are unable to fulﬁl the associated computa-

tional demands (i.e. vast amount of storage space,

strict completion deadlines, etc) because these tech-

nologies are not scalable and unable to fully utilize

the available recourse.

Consequently, researchers aim on providing meth-

ods for the design and realisation of scientiﬁc work-

ﬂows and also setting up computational environments

that provide signiﬁcant storage space and powerful

computational resources and most importantly the re-

searchers aim to map the designed workﬂow compo-

nents to existing computation environments. Cloud

computing resources provide optimal resource utiliza-

tion that best matches the demands of scientists by

providing on-demand, scalable, and ﬂexible solutions

for considered SWFA (Juve and Deelman, 2011).

The workﬂow pattern design in cloud computing

environments provides the facility of re-using previ-

ously developed workﬂow templates, which conse-

quently enables the developer to use them to ensure

better design of SWFA. In this manuscript, the au-

thors focus on type level of workﬂow patterns and not

on complete workﬂows which include the mapping of

their task/components to actual scripts and services.

Fig. 1 depicts three main stages of the SWFA pat-

tern design lifecycle: (i) workﬂow design, (ii) de-

signer interpretation, and (iii) cloud execution envi-

ronment. In the workﬂow design stage, the scientists

use previous workﬂows patters and thus effectively

systematically reuse knowledge for controlled exper-

imentation (Pan et al., 2019). After that, in the de-

sign interpretation stage, the parameters and data re-

sources are determined based on the experimentation

requirements. As tasks could be realised by different

services/scripts which could operate on equivalent-

typed but different in format and structure data sets.

Thus, the interpretation stage simpliﬁed these com-

plexities between the tasks by selecting the appropri-

ate services/scripts which are depend on the selection

of actual data resources. Notice that the determined

selections will subsequently be used for the execu-

tion stage in the pattern design lifecycle. Finally, the

main purpose of cloud execution environment stage is

to ﬁrst deploy the cloud computing environment that

is able to execute the workﬂow. And based on the

complete speciﬁcation of the workﬂow that has been

collected from previous two stages (workﬂow pattern

selection, workﬂow authoring, development/selection

of workﬂow components, etc.) the cloud resources

consume and create the data of SWFA for the desired

execution. Due to parameters dependences (that have

been determined in designer interpretation stage) ef-

fect the task executions, thus all these tasks cannot

be executed concurrently in the cloud execution envi-

ronment. The runtime executions required a runtime

monitoring component that could continuously moni-

tors and informs the scientists about the current status

of the SWFA execution. This monitoring functional-

ity plays a crucial role about successful execution of

SWFA. As overall, the pattern design stages are con-

sisting of design, deployment and provisioning where

the latter includes execution, monitoring and adapta-

tion.

In the literature, a number of workﬂow pattern de-

signs have been proposed to better design the SWFA

(Ramakrishnan et al., 2011; Genez et al., 2012; Wu

et al., 2013b). However, the selection of most efﬁ-

cient workﬂow pattern design remains a challenging

task as it required to consider several aspects. The

user requirements are the main aspects which include

the make-span, reliability, deadline, and budget, while

the other aspects are related to the particular execu-

tion method, data size, and problem complexity of a

SWFA. In this paper, we propose a conceptual frame-

work that suggest suitable workﬂow pattern designs

based on user functional and non-functional require-

ments service consumers and quality constraints of

service providers. The migration of SWFA from other

computation paradigms to cloud computing requires

considering several aspects and constraints. Thus,

based on the literature we provide a set of guide-

lines to assist this transformation to fully utilize the

strengths of cloud computing. Furthermore, a classi-

ﬁcation of potential workﬂow pattern design is pro-

posed to guide the framework in selecting a most ap-

propriate workﬂow pattern design for the considered

SWFA.

The remainder of this paper is organized as fol-

lows: Section 2 presents our proposed conceptual

framework. Section 3 provides a classiﬁcation of re-

ported pattern design for SWFAs. A set of guide-

lines to shift SWFA from different computing envi-

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230

Figure 1: The Pattern Design Stages of Scientiﬁc Workﬂow

Applications Lifecycle.

ronments to the cloud computing is provided in Sec-

tion 4. Section 5 discusses previously conducted rel-

evant works. Finally, Section VI concludes this paper

and outlines a certain number of future research di-

rections.

2 THE PROPOSED

CONCEPTUAL FRAMEWORK

The utilization of cloud computing resources is de-

pending on the respective application requirements

and also relates to the resources being consumed by

the WFM. Thus, there is a need for a lightweight

WFMS that still offers the right functionality to its

users and this can be achieved by selecting appropri-

ate workﬂow pattern design. Motivated by this, we

present our proposed conceptual framework for se-

lecting workﬂow pattern design for SFWA in cloud

computing. Figure 2 depicts the high-level layered

architecture of our proposed conceptual framework.

The proposed framework consists of four main lay-

ers, including cloud actor, cloud scientiﬁc workﬂow

application, cloud use cases, and pattern design. The

ﬁrst layer represents the various cloud actors, which

can be categorized based on the speciﬁed rules of

the SWFA users, tasks and access rights. The main

actors are: (i) Scientist: SWFA provides interac-

tive tools to help scientists in better executing their

own workﬂows and visualise the results in a real-time

manner. (ii) System developer: or system admin-

istrator responsible for designing the WfMS based

on the requirements of service consumers. (iii) Ser-

vice vendor, and (iv) Resource owner: they are ser-

vice providers (i.e. cloud service providers) that offer

the SWFS system with virtualised computational re-

sources (i.e. private, public or hybrid).

There are two types of interactions handled in the

cloud environment using Application Programming

Interfaces (APIs): (i) inter-interaction (i.e. the inter-

action between cloud actor and cloud SWFA), and (ii)

intra-interaction (i.e. interaction between one cloud

actor with another cloud actor). An example of inter-

action is inter-interaction between scientists and de-

veloper. The developers design a SWFA that could

simplify the process for scientists to reuse the same

workﬂows and provides an easy-to-use environment

to track and share output results virtually.

The second layer manages the cloud scientiﬁc

workﬂow application, and we distinguish two main

types: (i) scientiﬁc applications, and (ii) manage-

ment applications. The scientiﬁc application pro-

vides a user-friendly interface that enables cloud ac-

tors to interact with a scientiﬁc workﬂow. The man-

agement application handles all management related

issues that may occur while executing the SWFAs.

E.g., one application for the design of an SWFA and

another for the execution of an SWFA. Note that de-

velopers continuously monitor the performance of the

management application and perform maintenance re-

lated tasks to avoid any service degradation as well as

the system could also perform some adaptations to the

SWFAs. The third layer represents cloud use-cases.

The use-cases depict different scenarios that may oc-

cur while the developer interacts with the cloud re-

sources. Therefore, in the context of cloud use-cases,

cloud computing offers different types of application

paradigms. In the literature, cloud use cases are cat-

egorized into four classes, including application use

cases, delivery of services use cases, deployment of

application use cases, and interaction between the ap-

plication and cloud services use cases based on user

proﬁles (Petcu, 2010).

The fourth layer then encompasses pattern design.

A Conceptual Framework Supporting Pattern Design Selection for Scientiﬁc Workﬂow Applications in Cloud Computing

231

The circles denote the cloud use-cases (with differ-

ent colours and each colour maps to a different kind

of cloud use case) and the arrows represent their de-

pendencies. Thus, we could deﬁne workﬂow pattern

design as a selection of the right use cases in order to

instantiate a speciﬁc workﬂow management scenario.

In other words, workﬂow pattern design results in a

cluster of use cases on which speciﬁc dependencies

are introduced. We consider that different use case

kinds are covered and we try to select them in order

to support the whole management of an SWFA.

The following are the constraints that need to be

considered in order to determine suitable workﬂow

pattern design for SWFA in a cloud computing en-

vironment:

• Identify the interaction between cloud actors

and SWFA. This mapping is based on the require-

ments and preferences such an actor would have.

These requirements and preferences are bound to

the proﬁle of that actor that is maintained by the

proposed framework.

• Identify the real mapping between cloud actors

and cloud use-cases.

• Identify the required computational require-

ment based on the nature and type of SWFA.

• Identify the values for the parameters and con-

trolling mechanism based on the type of schedul-

ing or optimization methods.

• Select the most suitable workﬂow pattern de-

sign from the classiﬁcation list, which has been

presented in the next section.

3 CLASSIFICATION OF

SCIENTIFIC WORKFLOW

APPLICATIONS (SWFA)

PATTERN DESIGN

The current state-of-the-art lacks in using different

types of pattern designs for SWFA. The majority of

the work focuses on control ﬂow and workﬂow data

pattern design only. However, (Kiepuszewski et al.,

2003) reports that both control ﬂow and workﬂow

data pattern design is difﬁcult to adopt and follow

for SWFA due to implicitly deﬁned control ﬂow re-

lations. In contrast, there are several useful but ig-

nored pattern designs, which are effective to model

and implement existing SWFAs. We have analysed

and stated possible classiﬁcation SWFA pattern de-

signs based on the reviewed papers. Fig. 3 presents

this categorization by presenting two levels of cate-

gories of SWFA design patterns. Our classiﬁcation

Figure 2: High-Level Layered Architecture of Proposed

Framework.

identiﬁes four main categories. These main categories

are: (i) control ﬂow patterns, (ii) workﬂow data pat-

terns, (iii) workﬂow resource patterns, and (iv) other

scientiﬁc patterns. Each aspect is further categorized

into several classes based on computational nature

and use cases. It can be clearly seen that control ﬂow

pattern design has a greater number of sub-categories

than any other aspect.

4 GUIDELINES TO ASSIST THE

MIGRATION OF SWFA FROM

OTHER COMPUTATION

PARADIGMS TO CLOUD

COMPUTING

Migration from one computational paradigm to an-

other is never an easy task. In order to fully utilize

the strengths of cloud computing, developers need to

consider the following quality constraints (Figure 4)

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232

Figure 3: Classiﬁcation of SWFA Pattern Design.

for successful transformation from other computing

paradigms to cloud computing:

4.1 Demand Cloud Resource

Cloud actors can request and acquire cloud resources

at any time according to their requirements to execute

SWFA. This quality constraint provides ﬂexibility in

terms of problem size, budget, and Make-span. Con-

sequently, it improves resource utilization by provid-

ing instant responsiveness to cloud user requests.

4.2 Elasticity

This refers to the on-demand response from cloud to

actors. This quality constraint facilitates WFMS to

manage the available resources according to SWFA

demands. The use of popular workﬂow patterns (e.g.,

data distribution, data aggregation) enables it to fully

utilize the available resources.

4.3 Legacy Applications

It contains a variety of software components for dif-

ferent types of cloud actors. The WFMS integrates

these heterogeneous types of components into a single

application. The legacy code can easily be executed

using virtualization technology together with clouds.

4.4 Provenance and Reproducibility

This constraint refers to the metadata storage for the

computation by keeping all traceability information

about the origin and type of data using a particular

computation.

4.5 Application Deployment

This quality constraint focuses on the usage of vir-

tualization technology in Cloud computing environ-

ment dynamically pre-loaded and/or deployed onto a

virtual machine (VM) based on the computational re-

quirements.

4.6 Scalability

This is one of the most important quality constraints,

especially when dealing with large-size data and re-

sources. To efﬁciently deal with data-intensive and

computer-intensive workﬂows, a necessary graphical

environment is required to fully utilize the capabilities

of cloud resources.

4.7 Reliability and Fault-tolerance

The cloud actors (mainly developer) have to explicitly

specify the contingency actions (e.g., the strategies

to deal with particular failure) to make the workﬂow

more reliable against the unreliable environments.

Figure 4: Quality Constraints for SWFA Migration to Cloud

Computing.

5 RELATED WORK

In the literature, several review studies relevant to this

area of research have been performed. A decade ago,

(Yu and Buyya, 2005) propose taxonomy by consid-

ering four aspects: (i) workﬂow design, (ii) workﬂow

A Conceptual Framework Supporting Pattern Design Selection for Scientiﬁc Workﬂow Applications in Cloud Computing

233

scheduling, (iii) fault tolerance, and (iv) data move-

ment to evaluate scientiﬁc WFMSs in terms of Grid

computing, however, this classiﬁcation is not suitable

for selecting a most suitable workﬂow pattern design.

In contrast, (Petcu, 2010) focus on providing gen-

eral pattern features, and re-using patterns from re-

lated architectures. In terms of application deploy-

ment reasoning, the authors provide a mechanism via

which the most appropriate patterns are selected by

considering a combination of virtualization technol-

ogy of cloud and self-service facilitated application

deployment. The deﬁnitions of patterns on different

security levels are provided to handle the security of

data services in Clouds. On the other hand, a gen-

eral interaction pattern for service providers is rec-

ommended by (Deelman et al., 2015). Other previous

work (Alkhanak et al., 2015) focused on cost-aware

workﬂow scheduling challenges in cloud computing.

In the previously conducted work, we have reported

on various cost optimization aspects and parameters

covered by workﬂow scheduling approaches neces-

sary to understand the body of knowledge in this area

of research.

However, to the best of our knowledge, no work

has been done related to classiﬁcations or guidelines

useful in selecting the most suitable workﬂow pattern

design for SWFA in cloud computing. Table 1 illus-

trates state-of-the art scientiﬁc workﬂow application

approaches.

From Table 1, all pattern design is control-ﬂow

based, and this shows that there is a need to utilize

other types of workﬂow patterns design such as work-

ﬂow data patterns, workﬂow resource patterns, and

other scientiﬁc patterns. Also, it can be clearly ob-

served from Table 1 that most of the proposed ap-

proaches have considered the Cloud/Grid provider

as the cloud actor. This could be due to the proﬁt

that Cloud/Grid provider could gain from utilizing

the pattern designs. Also, this shows there is a need

to give more attention to the functional and non-

functional requirements of the service consumer (i.e.

researcher). In summary, our proposed conceptual

framework and guidelines to assist the migration of

SWFA from other computation paradigms to cloud

computing could help both cloud actors.

6 CONCLUSION AND FUTURE

WORK

Scientiﬁc Workﬂow Applications (SWFA) have sig-

niﬁcantly attained the researcher’s attention since the

advent of cloud computing. Cloud computing offers a

technology that can signiﬁcantly utilize the amounts

Table 1: The State-of-the Art Scientiﬁc Workﬂow Applica-

tion Approaches.

Reference Pattern

Design

Cloud Actor

(Wu et al.,

2013b)

Control ﬂow Service con-

sumer

(Abrishami

and

Naghibzadeh,

2012)

Control ﬂow Service con-

sumer

(Ramakrishnan

et al., 2011)

Control ﬂow Cloud/Grid

provider

(Nargunam

and Shajin,

2012)

Control ﬂow Cloud/Grid

provider

(Wu et al.,

2013a)

Control ﬂow Cloud/Grid

provider

(Ostermann

et al., 2010)

Control ﬂow Cloud/Grid

provider

(Liu et al.,

2011)

Control ﬂow Cloud/Grid

provider

(Yang et al.,

2008)

Control ﬂow Service con-

sumer

(Genez et al.,

2012)

Control ﬂow Service con-

sumer

(Xu et al.,

2009)

Control ﬂow Cloud/Grid

provider

(Saeid Abr-

ishami, 2013)

Control ﬂow Cloud/Grid

provider

(Lin and Lu,

2011)

Control ﬂow Cloud/Grid

provider

(Pandey et al.,

2010)

Control ﬂow Cloud/Grid

provider

(Tanaka and

Tatebe, 2012)

Control ﬂow Cloud/Grid

provider

(Bittencourt

and Madeira,

2011)

Control ﬂow Cloud/Grid

provider

of storage space and computing resources necessary

for processing large-size and complex SWFAs. Cur-

rently, developers are facing numerous challenges

while transforming SWFAs from other computational

paradigms to cloud computing.

This paper presented a conceptual framework that

can identify the interaction between cloud actors

and SWFA. And also identify the real mapping be-

tween cloud actors and cloud use-cases which ulti-

mately identify the required computational require-

ment based on the nature and type of SWFA. Fur-

thermore, we devised a taxonomy for SWFA pat-

tern design, which helps developers in selecting the

CLOSER 2020 - 10th International Conference on Cloud Computing and Services Science

234

most suitable pattern design based on their require-

ments. Moreover, a set of guidelines is provided

to effectively migrate SWFA from other computa-

tion paradigms to cloud computing. The pattern de-

sign for SWFA in cloud computing can certainly de-

crease development and maintenance cost and time.

In the future, other types of workﬂow applications

(e.g., business workﬂows) would be considered in a

cloud computing environment. An extensive evalu-

ation about the performance of proposed framework

using a real-world scientiﬁc workﬂow application in

a cloud platform would be conducted. Furthermore,

we plan to make a connection between our concep-

tual framework and a WFMS by identifying the tradi-

tional architecture of a WFMS and which conceptual

elements are exploited by which WFMS components

such showcase can be exploited across the whole sci-

entiﬁc workﬂow (management) lifecycle.

ACKNOWLEDGEMENTS

This work has been sponsored partially by the

NWO/TTW project Multi-scale integrated Trafﬁc Ob-

servatory for Large Road Networks (MiRRORS) un-

der grant number 16270. This work is related to the

PhD research by Dr. Ehab Al-Khannaq, sponsored by

RG114-12ICT, supervised by Prof. Dr. Sai Peck Lee,

and supported by Ministry of Education, Malaysia.

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