A Task Orientated Requirements Ontology for Cloud Computing
Richard Greenwell
, Xiaodong Liu
, Kevin Chalmers
and Claus Pahl
Institute for Informatics and Digital Innovation, Edinburgh Napier University, Colinton Road, Edinburgh, U.K.
School of Computing, Dublin City University, Glasnevin, Dublin, Republic of Ireland
Keywords: Cloud, Service, Description, Semantic, Requirements, Engineering, Ontology.
Abstract: Requirements ontology offers a mechanism to map requirements for cloud computing services to cloud
computing resources. Multiple stakeholders can capture and map knowledge in a flexible and efficient
manner. The major contribution of the paper is the definition and development of an ontology for cloud
computing requirements. The approach views each user requirement as a semantic intelligence task that
maps and delivers it as cloud services. Requirements are modelled as tasks designed to meet specific
requirements, problem domains that the requirements exist in, and problem-solving methods which are
generic mechanisms to solve problems. A meta-ontology for cloud computing is developed and populated
with ontology fragments on to which cloud computing requirements can be mapped. A critical analysis of
the usage of ontologies in the requirements process is made and a case study is described that demonstrates
the approach in a real-world application. The conclusion is that problem-solving ontologies provide a useful
mechanism for the specification and reuse of requirements in the cloud computing environment.
Wind and Schrödl (Wind and Schrödl, 2011)
describe a number of approaches to Requirements
Engineering (RE) in cloud computing, which were
found to be unsuitable in a number of key areas,
such as architecture selection, legal issues and
pricing. Cloud services require semantics to express
functionality derived from many service providers.
Semantic web-services have successfully used
ontologies (Fensel et al., 2003), as have a number of
RE approaches (Happel and Seedorf, 2006). An
ontological approach can address some of the
shortcomings seen in the current cloud computing
RE process, such as lack of completeness,
consistency and conflicts between requirements.
Ontologies have been used for modelling
requirements for various aspects of information
systems. Farfeleder et al (Farfeleder et al., 2011)
describe ontologies using natural language for
formalising and verifying requirements in embedded
systems. Jureta et al.(Jureta et al., 2008) discuss the
use of ontologies in stakeholder communication.
A particularly useful ontological RE approach is
described by Bogg et al (Bogg et al., 2011). Bogg
explores the use of Problem-Solving Methods
(PSMs) expressed as an ontology in RE. PSM are
reusable methods or approaches to problems that can
be used across a number of knowledge domains. The
approach is seen as cogent for cloud computing, as
large compute clouds can be seen in a service
brokerage process, which could provide access to a
large number of PSMs instances to solve problems
across a number of knowledge domains.
In this paper, we advocate the view that the cloud
computing environment can be seen as a problem-
solving environment. Users have problems which
can be tackled using a cloud computing, at a given
quality of service and cost. Requirements ontology
is used to support this problem-solving approach.
The requirements are modelled as tasks designed to
meet specific requirements, problem domains that
requirements exist in, and as problem-solving
methods which are generic mechanisms to solve
problems and bridges between the three elements.
The approach enables each user requirement to be
considered as a semantic task, which can be
implemented as a cloud service.
The remaining parts of the paper are organised as
follows: section two presents an overview of the
important work and aspects of ontology usage in RE.
The design of the ontology is defined in section
three. Section four provides a specification of the
Greenwell, R., Liu, X., Chalmers, K. and Pahl, C.
A Task Orientated Requirements Ontology for Cloud Computing Services.
In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1, pages 121-128
ISBN: 978-989-758-182-3
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
requirements ontology. A case study is presented in
section five which demonstrates and verifies the
proposed RE approach. Section six provides
discussion of the approach. Conclusions are drawn
in section seven, with future work identified.
Ontologies provide a structured framework for
modelling the concepts and relationships of a
domain of expertise. Ontologies support the creation
of repositories of domain-specific reference
knowledge (Crubézy and Musen, 2003). Ontologies
have been used for requirements engineering for a
number of years. Zave and Jackson (Zave and
Jackson, 1997) described “core” ontology as solving
the “requirements problem”. The core ontology
established the minimum set of information required
for engineering requirements as:
S, W R
R are given requirements
S is a complete specification
W are domain assumptions
Proof of Obligation requires that the
specification and domain assumptions to be satisfied
by the requirements (Classen, 2007). This points to a
“pure” but simplistic approach to RE that only
specification and domain assumptions are required
in the RE process. The approach is criticised, by
Jureta et al (Jureta et al., 2008), who state that partial
requirements cannot be described in Zave and
Jackson’s model, and only a complete specifications
can be created. The requirements specifications
cannot be ranked in terms of better or worse
requirements for a given specification. Non-core
requirements cannot be defined and, nice to have
requirements may be lost.
Castanada et al (Castaneda et al., 2010) identify
a number of benefits in using ontology in the RE
process. A requirements model is imposed enabling
the structuring of requirements and the knowledge
domain in question. The Interrelationships between
requirements can be defined.
A number of attempts have been made to specify
an ontology to describe the components of cloud
computing, a typical example being Youseff et al
(Youseff et al., 2008). These ontological approaches
suffer from viewing cloud computing as a
continuation of Software as a Service and
concentrate on low level virtualisation.
A cloud computing ontology has been developed
and enhanced for cloud computing using the work of
Norton et al (Norton et al., 2008). The ontology
development is the major contribution of this paper,
along with ontology ‘Fragments’. The ontology can
be seen as a meta-ontology for RE in cloud
computing environments, building on more generic
ontologies for problem-solving.
Each user requirement can be defined as a
semantic task, this facilitates enhanced capability in
the validation of specification, the discovery of
services and composition of cloud services. Cloud
computing can be seen as more complex than
traditional Information (IT) environments. User
requirements are expressed at a high level, a
brokerage layer or service will find and price these
requirements from a number of cloud computing
resources. Cloud computing resources will then
execute tasks for these brokered requirements.
Expressing requirements using a problem-
solving ontology allows the requirements engineer
to utilise an approach that is well suited to the cloud
computing environment. Tasks can be seen as a unit
of work that is well understood by users. Problem
Solving Methods (PSMs) can be seen as reusable
specifications for solving the problems posed by
tasks. Domain models can be built as an ontology so
it can be understood by users and verified using
ontological reasoning tools. The requirements
ontology can be seen as a specialisation of more
generalised problem-solving ontology such as the
Unified Problem-solving Method Development
Language (UPML) which will be described.
Fensel et al (Fensel et al., 2003) describe
(UPML) which is a framework for developing
knowledge-intensive reasoning systems based on
libraries of generic problem-solving components.
They go onto describe the UPML architecture as
tasks that defines the requirements for the problem
that is to be solved. Problem-solving methods (PSM)
define the reasoning process and, domain models
that describe domain knowledge. Bridges are used to
map and define the relationship and transformation
between the task and PSM.
Crubézy and Musen (Crubézy and Musen, 2003)
describe how Problems Solving Methods (PSMs)
and domain ontologies are combined to produce
knowledge systems. Musen (Musen, 2001) describes
domain ontologies as a “Characterisation of
concepts and relationships in an application area,
providing a domain of discourse”. Domain
ontologies define problem specific knowledge.
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
A detailed requirement ontology can be mapped to a
number of ‘knowledge components’ for
implementation within ontology modelling tools.
The knowledge component provides a base selection
of properties such as description and requirement
pragmatics. Elements of the ontology then inherit
properties from the knowledge component.
Specialist PSMs such as problem decomposers
(PSMs that can split a task into subtasks) can be
developed for specific purposes. Requirements
engineers can develop their own specialist tasks,
PSMs and domain models for a specific
requirements problem using powerful mechanisms
such as inheritance and set operations. The usage of
tasks, PSMs and domain models will lead to greater
reuse as a generic method PSM.
Figure 1 (below) describes a model into which
requirements can be tailored. This machine readable
model is used directly in cloud computing
The ontology provides a checklist of ‘what’
requirements are needed and is specified in terms of
tasks, PSMs and Domain Models. The model
provides representation for elements of
requirements. Requirements are expressed in terms
of semantics and, concepts such as tasks can be
expressed in terms of rich semantics, as can
relationships between tasks, PSMs and Domain
Models. This allows the requirements engineers’
greater expressive power, and the ability to carry out
fuzzy searches and to map new knowledge and
requirements via the reasoning tools seen in
ontology management tools.
is_a is_a
Figure 1: Requirements Ontology Implementation.
The architecture of the ontology is described in
Figure 2 (below). The highest layer deals with
problem-solving for cloud computing. Users will
have tasks which use the PSMs and domain
ontology. The brokerage layer defines elements in
terms of ontology, tasks will be executed at a
strategic level across the cloud environment dealing
with issues such as cost and quality of service. The
low level layer deals with operational requirements.
UPML Brokerage
Discovery Adaptation Comparison
Mediation Grounding Fault Handling
Choreography Monitoring Pricing
UPML Problem
Solving Ontology
User Interface for
Problem Solving
Ontology Manipulation
Problem Solving
UPML Ontology for
Low Level Resources
Low Level
Cloud Interface
Adapters and Bridges
Distributed Cloud
Figure 2: The hierarchical Stricture of Ontology.
The details of each element of the requirements
ontology from figure 2 (above) will now be
3.1 Problem Solving
The problem-solving layer relates to the high level
requirements of users expressed as ontology and,
they describe ‘what’ is required; which may be full
requirements or partial requirements expressed as
ontology fragments. The requirements must be
matched to low level requirements through the
brokerage process via semantic or fuzzy matching.
The separation of requirements into tasks, PSMs
and domain knowledge and representation as UPML
provides ease of mapping to low level requirements
through the brokerage process.
3.2 Brokerage
The brokerage of requirements map high level
A Task Orientated Requirements Ontology for Cloud Computing Services
requirements to lower level requirements. This is
carried out by semantic searching, fuzzy matching
and negotiated processes. Discovery can be driven
by the Quality of Service (QOS) requirements.
These requirements are carried forward from user
requirements expressed in the high level layers of
the requirements ontology. Monitoring can use the
QOS requirements to define the requirements for
service failure. Pricing requirements can also be
related to QOS.
Discovery requirements describe what and how
cloud services are found across a set of cloud
resources. Adaptation requirements relate to how
defined requirements can be adapted to meet new
user requirements. The adaptation process is made
easier by the use of ontology as sets of requirements
that that can be adapted by recombination through
semantic relationships provided in the UPML
ontology. Closely related to adaptation, composition
requirements describe how sets of cloud resources
are combined to meet high level user requirements.
Mediation requirements define how high level
problem-solving requirements will be translated into
low level requirements by a process of iteration.
Many of the mediation requirements are concerned
with QOS, Rimal et al (Rimal et al., 2011) describe
the need for quality of service requirements in cloud
computing. Quality of service provides a guarantee
of the availability and performance of tasks inside
the cloud. Requirements are supported by service
elements such as security, reliability and
dependability. Stakeholder groups will place value
on service elements, for example low latency short
burst resources will be required by some users,
whereas other users will require long running
resource pools. Grounding requirements link the
execution of the requirements with how the
requirement is to be executed at a low level. Fault
handling requirements provide actions that are
necessary when errors occur at low levels in cloud
Choreography requirements provide the
approach required for coordinating higher level
requirements so they are performed correctly at a
low level. Monitoring requirements specify the
information required as tasks are executed and
choreographed. Pricing requirements at the
brokerage level deal with pricing estimation for a
given high level tasks and aggregate pricing for
packages of low level tasks.
The requirements ontology can draw upon many
leading research concepts seen in the literature to
represent concepts in the requirements ontology as a
complete ontology or ontology fragments. Robinson
(Robinson, 2003) describes service monitoring,
which is a brokerage component within the
requirements ontology. The high level requirements
for monitoring can be represented as a PSM:
1. Define the design-time model
a. Define goals and requirements
b. Define obstacles and monitors
2. Define the run-time model
3. Monitor the running program
It should be noted that this PSM can be used by a
number of tasks as the PSM can act on a number of
problem domains. The requirements are represented
in the brokerage layer. A primary goal can be
decomposed by a specialist PSM called a problem
decomposer. Tasks such as monitor will have inputs,
outputs, competencies and formal definitions seen in
Figure 2 (above). Lower level representation can
also be represented as ontology.
The discovery and monitoring processes can use
a similar service discovery and monitoring approach
in cloud computing. High level requirements are
used to drive the service discovery of web-services.
Users can then select cloud services that match their
QOS requirements.
Service adaptation can be seen in the
requirements ontology. Higher level requirements
goals and services categories can be represented as
domain models; these are measured using a
‘measure’ PSM. In the brokerage layer service
definitions are domain models used by a monitor
definition PSM. The lower service domain models
are monitored by PSM.
Ontological representation could provide many
of the features required by researchers, such as fuzzy
searching and the matching and representation of
partial requirements using ontological fragments.
3.3 Low Level Requirements
Resource description requirements of each low level
cloud resource are required so that they can be
brokered. Examples of a resource description could
be maximum CPU capacity, storage capacity,
response time and spare CPU capacity.
Sun et al (Sun et al., 2012) point out that cloud
computing has seen vendors offering a number of
cloud computing platforms. Ontology can be used to
describe vendors’ offerings and can be used to
abstract models from the integration of disparate
offerings. Pricing requirements at a low level deal
with areas such as the cost of CPU capacity and
storage capacity. The requirements of cloud adaptors
and bridges provide information for brokerage
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
The three levels of the requirements ontology
(problem-solving, brokerage and low level) are all
described in terms of UPML. This allows mapping,
stepwise refinement, interaction and reasoning to be
carried out between the layers. The usage and
processing of ontology fragments has been described
by a number of researchers (Nebot and Berlanga,
2009), (Kalfoglou et al., 2008) and (Packer et al.,
The high level problem-solving requirements are
specific to each individual requirements domain or
process. They are still defined and structured in a
UPML and the example of high level problem-
solving is shown in the case study (below). The
requirements ontology concentrates on the brokerage
and low level aspects of RE. Brokerage fragments
will take the problem-solving requirements and
consider the requirements for their fulfilment. An
example of a brokerage fragment will be given for
discovery. Discovery is the process of finding
resources for the fulfilment of a high level
requirement. In Figure 3 (below), the RE ontology
fragment for discovery is shown. The UPML
ontology provides the framework for ontology
fragments, which in turn guide the subsequent RE
process. The discovery process is driven by two
tasks, the discover resources search the cloud
resource model and the cloud technology to build a
catalog of resources and will search the catalog with
a query string to allow the resources to be
Table 1 (below) shows how properties can be
defined for the “Discover Resources” task.
Table 1: Discover Resources Task.
Cardinality Type
Input Exactly 1 Cloud Resource
Input Exactly 1 Cloud
Output Exactly 1 Catalog
Now the requirements ontology has been
described in detail a case study will demonstrate
how the requirements can be defined for each
requirements ontology element.
Available Resources
Similarity Algorithm
Knowledge Component
Domain Model
Cloud Resource Model
Cloud Tolopology
Problem Solving Method
Reasoning Resource
Build Catalog of low
Level Resources
Parse Query String
Search Catalog
Discover Resources
Figure 3: Discovery Element Ontology Fragment.
The case study shows how RE can utilise a UPML
based ontology using the concepts described in the
requirements ontology. The case study describes the
requirements for a document similarity framework,
which allows documents such as academic texts to
be compared to the papers they reference. Manning
et al (Manning et al., 2008) outline an approach for
document similarity.
Collect the documents to be indexed
Tokenise the text
Carry out linguistic pre-processing of tokens
Index the documents that each term occurs in
Use similarity measures based on mathematical
measures, such as Cosine Similarity
A Task Orientated Requirements Ontology for Cloud Computing Services
Report or carry out further processing such as
The case study is particularly suited to cloud
computing as large amounts of parallel processing
are required to process documents. In single
processor machines finding and comparing
thousands of documents can take several hours.
There is also scope to expand the application to
recursively find referenced documents from the
documents referenced from a study text.
5.1 High Level Problem-solving
Requirements Ontology.
The high level tasks required for the case study are
described Table 2 (below). The requirements
describe a workflow of tasks which need to be
executed to carry out document similarity for a
document from a student course.
Table 2: Case Study: High Level Requirements.
Task Requirements Description
Find academic references
in course Documents
Parsing to find
document references.
Create structured
references and import
into reference
management system
Format references so
they are machine
Find academic papers for
Find references
automatically using
Extract plain text from
the PDF files, break into
pages and tokenise text
Use off-the-shelf
cloud software
Pre-process tasks
and indexation
Use off-the-shelf
software libraries
Create similarity
measures and match
Suited to Cloud
computing Burst of
processor bound tasks
Reporting Report document
These task requirements are then converted into
UPML ontology. The requirements are split into
tasks, PSM and Domain Models.
5.2 Brokerage
The brokerage ontology matches the high level
requirements to the low level requirements ontology.
Each aspect of the ontology will now be discussed.
Discovery can be seen as requirements which use
a ‘find low level requirement for a high level
requirement’ PSM. The high level requirement task
‘evaluate_corpus’ requires the low level formulas
such as ‘Ratio Distance’ and will discover
Adaptation is the process of adapting low level
requirements to meet a new or existing high level
requirement. Composition defines the ordering of
requirements tasks to complete the goal of producing
document similarity for a corpus of documents. The
tasks in the case study are self-organising as output
from one low level resource feeds the input of
another low level resource.
Mediation is driven by the high level
requirements specification to find the most
appropriate low level resource by stepwise
Grounding is a simple mapping of high level
tasks requirements to individual software modules.
Fault handling requirements deal with actions
that occur in low level programming language
modules, virtual machines and physical machines.
An off the shelf chorography model was used.
Yazir et al (Yazir et al., 2010) describe the
PROMETHEE methodology for chorography across
multiple cloud resources where a number of physical
machines (PM) are allocated including Virtual
Machines (VM) across a set of cloud resources.
Monitoring requirements concern the information
required being used to review the progress of the
low level execution of tasks.
An existing pricing model was used in this case
study. Henzinger et al. (Henzinger et al., 2010)
discuss the Flexprice model for pricing across
multiple cloud resources. In a commercial
implementation of a document similarity framework
high level task requirements will be priced across a
number of cloud providers and the most cost
effective solution will be selected
5.3 Low Level Requirements Ontology
A number of formulas are required to calculate
document similarity. UPML allows individual
software components to be described. Tasks describe
the operations required to meet requirements high
level requirements. UPML can describe both high
and low level requirements in a structured way.
Requirements engineering ontology provides a three
layer framework for RE in the cloud computing
environment built on UPML.
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
The ontology can be checked for correctness and
reasoning and can map new knowledge from the
ontology that can be relayed to users. Requirements
can be inserted into the ontology and used at a later
date. Requirements can be found using semantic or
fuzzy searching as well as syntactical searching.
The requirements ontology environment can be
used to develop meta-services. These meta-services
support two key features that are new to cloud
computing self-service and on-demand provision.
The high level and brokerage requirements seen in
the requirements ontology allow customers to access
on-demand self-service via meta-services.
The case study has demonstrated the
requirements ontology built on UPML. The three
layers of the requirements ontology provide
guidance for the definition of a document similarity
framework for study texts and the papers referenced
from the study text. High level requirements,
brokerage and low level requirements are expressed
as textual requirements and, then as a UPML
ontology. Ontology mapping and reasoning tools can
be used to match each layer of the model, so that
high level requirements can be executed by
appropriate resources in the cloud. The use of
ontology leads to a greater reuse of requirements and
the generation of new requirements by reasoning.
The reuse of requirements is a key advantage of
using a UPML based ontology. A PSM can be used
in many knowledge domains and knowledge
domains can be re-used for new requirements.
Problem-solving ontologies are seen as useful for
cloud computing as it can be seen as a problem-
solving paradigm, as opposed to an extension of
SaaS or virtualisation of existing applications.
This paper has described an ontology driven
approach to requirements engineering for cloud
computing. This is embodied in the requirements
ontology which was built on a specialised form of
ontology based on a UPML, which is well suited to
service specification. A key aspect of the approach is
the examination of the brokerage requirements,
which bridge high level and low level requirements
The requirements engineering problem is broken
down into three sets of concepts: tasks which
describe the work that is to be done, problem-
solving methods which describe the solutions to
problems, and a problem domain which describes
concepts for a given requirements scenario. The
requirements ontology builds on a UPML structured
ontology approach across the three distinct levels in
cloud computing RE. Ontology mapping is seen as a
key tool for linking requirements at different levels
in the requirements ontology.
Future work will see the implementation being
expanded to allow for a simpler specification of
knowledge components such as tasks, domain
knowledge, problem-solving methods and bridges.
In future case studies, more complex brokerage will
be used. Security will be included in the future
version of the requirements ontology as it is a major
emerging area in cloud computing.
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