ice providers as shown in our case study, Our DSS recommends Service C, Service B and Service A are the SaaS services that are android compatible and recommended to the user by our system.
・ Data Property Similarity Reasoning: This is a type of reasoning that occurs within the ontology when a user queries the DSS Cloudysme for two or more cloud services with same datatype properties for a range of data. To demonstrate that our Decision System which includes a semantically designed ontology of cloud services can perform datatype similarity reasoning. In a scenario whereby the same SME owner in the two scenarios above also considers a SaaS cloud service with a file size restriction of data between 2 and 14 Gigabytes (GB). Before we show our DSS recommendation we will first show the RDF format of the above requirement within the ontology (Subject: (Cloud Service) Predicate: (has Filesize Restriction GB) Object: Int [> X <]). We query our ontology to meet user requirement by using machine readable language as The follows query is sent (SaaS and has File Size Restriction GB some int [>1, <15]) We translate the query in lay terms as follows: (Software as a service with a file size restriction between 1 and 14 gigabytes).
From the above datatype property reasoning, our system recommends Service C and Service A as the cloud services that meet the user requirement in Figure 6 above.
At the stage we have proven that our system can undergo conceptual similarity reasoning, Object property similarity reasoning and Data property similarity reasoning in answering user requirements in view of aiding SME owners in cloud service adoption process. The next section will be demonstrating the use individual matching by our system to meet user requirements.
Figure 6. Data property similarity reasoning.
5.2.3. Individual Similarity Matching
This is a condition whereby cloud services are recommended based on the ability of each of their attributes to meet certain acceptable standards as shown in section 4 and this is achieved with the use of semantic rules as shown in Figure 7. From the above case scenarios and recommendations, our system has proven that it has enough knowledge to aid SME owners towards adopting cloud services for their businesses. In addition, our system has also shown that different cloud services meet different user requirements, furthermore, we have adopted the minimum acceptable standard for each criteria as represented above within our DSS. To prove that our system can undergo individual similarity matching, we demonstrate this by showing SaaS cloud services that meet the acceptable standard for File Size Restriction with an acceptable standard of (0.0721 ≥ 721) as seen in above. To achieve this requirement, Semantic rules have been established within our ontology below. The following rule is being queried:
(SaaS and (hasFSRPriority some int[≥721]))(?x)) - > FSRacceptible (?x)
From the above Individual similarity matching our system recommends, Service D, Service C and Service B as the cloud services that meet the acceptable standard for File Size Restriction. Our above system recommendation will enable the user narrow his choice to either of the recommended cloud services in his adoption decision making .The next section will be on cloud service ranking.
6. Cloud Service Ranking
Cloudysme service ranking is done using the 5 Stars, 4 Stars, 3 Stars, 2 Stars, 1 as discussed in Section 3.5. In this section we show the use of semantic rules (Figure 8) within the system in machine readable form. While Figure 9 shows a
Figure 7. Individual similarity matching.
Figure 8. Showing rules within the system.
query execution listing Service B as the only SaaS storage service to meet the 5 star service ranking.
Figure 9. Showing Service B as the only SaaS to meets the 5 star ranking.
Below is a system query executed to show the service that meets the 5 star SaaS service ranking.
In order to determine if our approach can aid SMEs in decision making towards adoption of SaaS cloud service for their business, we use 30 SMEs from various business sectors as pivotal test and we try to observe how many times our system recommended a particular cloud service for adoption based on the SME owner requirement. Figure 10 shows a graphical representation of the number of times a particular cloud service was recommended.
Likewise we try to identify which of the attributes was mostly identified by the SME owner as a priority when adopting SaaS cloud service for their business Figure 11 shows a graphical representation of our findings.
8. Related Works
In recent times, many researchers have proposed, designed, developed and implemented cloud frameworks and systems that allow users find suitable services that meet their requirement. While some have developed algorithms for resource management, others use ontology models to represent cloud services and to perform process matching between object and data properties of cloud service attributes in a bid to meet users’ requirements   . Ontologies can be defined as a “formal explicit specification of a shared conceptualization”    . They are useful for information retrieval to deal with user queries as they
Figure 10. Showing number of times a service was recommended.
Figure 11. Showing SME owner priority towards cloud service adoption.
contain a set of concepts on the domain and the relationships between these concepts  . In addition, ontologies are known to have three important applications as follows: To enable the communication between software systems, to facilitate interoperability and to aid the communication among humans   . Furthermore, the challenges associated with traditional search tools as well as matching between user requirement and advertised services by providers can be eliminated through ontologies and semantic technologies  . It is important in the area of information integration as seen in the work of  , Knowledge management  , Information retrieval and question answering  and Recommendation  .
Presently, most cloud service ontologies are general with little or no detailed work on each cloud services  . Although the work of  presents a unified view of cloud computing representing its components and their relationships. However, this cloud service ontology has a service orientation showing a clear distinction between different layers such as cloud application layer (SaaS), software environment (PaaS) and cloud Infrastructure (IaaS). Due to the rise in number of service providers rendering similar services, there has been a continuous research towards distinctively understanding the different layers of these services, their attributes, relationships, functional and non-functional properties. The work of  presents a tabular representation of survey findings of cloud services, thereby comparing the offerings of services provided by major service providers in each service layer IaaS (Amazon web service, GoGrid, Flexiscale, Mosso) PaaS and SaaS (GoogleApp Engine, Azure, force.com, GigaSpaces). Also  propose a taxonomy of comparison of cloud services providing detailed characteristics in a hierarchical form using common terminologies associated with each layer as a baseline for information and communication. The authors  classified cloud services based on pricing of complex services as well as security and reliability. While a framework for ranking cloud services by evaluating cloud offerings and ranking them based on their ability to meet users quality of service requirements is proposed by  . Our work compliments these previous works in the area of cloud service information gathering, classification and utilization of service ranking tools toward meeting user requirements.
An ontology enhanced cloud service discovery system is proposed by  the system enables users select cloud services providers based on the provided ontology. The work of  proposed an ontology based discovery of cloud providers with a range of querying possibilities on different cloud service layers. Cloud service provider resource management ontology is proposed by  while the work of  proposed an ontology that relates to service lifecycle and cloud governance. The work of Han et al. (2009) focuses on the ranking of available service providers using a statistical approach. The Authors,  claimed that their ontology is much better in the aspect of querying possibilities and more comprehensive in respect to other works where the three cloud service layers SaaS, PaaS, IaaS have been considered as it can be used in discovery of cloud services as well as resource management in more complex and comprehensive manner.
This study is complementary for existing cloud computing works as it proposes an ontology that can be used for cloud service discovery, knowledge management and service ranking. Our work goes a step further by comparing different advertised cloud services and their attributes using a multiple criteria decision making method by proposing an extended version of AHP towards cloud service adoption decision and the use of semantic rules within the ontology to aid cloud service information retrieval and decision making process to meet user requirement.
One important decision in our approach is to equip our decision support system which is included in our framework with an ontology. This is because semantic web supports descriptive logical reasoning using software’s like pellet. In addition, it allows the use of semantic rules to give consistent and accurate feedback in a view to recommend solutions that meets user requirements, it allows the use of ontology language to translate human language to machine readable language. This makes it easy for SME owners to get real time solution and knowledge of cloud services rather than surfing the net for information or overlooking cloud services for their business due to lack of knowledge. The slow adoption rate associated with cloud service adoption by SMEs can be attributed to the number of service providers offering similar cloud service packages at different prices, lack of knowledge, Security issues, trust and compatibility as discussed in the above section.
Our proposed framework which tends to address the issue of slow adoption rate is divided into four major phases. The first phase comprises of advertised cloud services by services providers and this is represented in our case study. The attribute for each cloud service represented in our framework was obtained from the cloud service provider website. The second phase is the Filtering phase which an extended version of Analytical Hierarchical Process (AHP) which is multi criteria decision making method to determine the weight of each cloud service attribute, assign acceptable standard for each attribute as well as propose a protocol for cloud service ranking. To achieve this, we compare two similar cloud services attribute from different service providers head to head using pairwise comparison. This is followed by prioritizing the attributes based on superiority of one over another. The degree of consistency of the pairwise comparison is being measured by determining the consistency ratio (CR) of each comparison. In addition, we determine the acceptable benchmark for each cloud service criteria by performing a head to head comparison of all the criterion based on their level of importance to meet user requirement and we depict visual representation of our findings using a Kaviat graph. The third phase of our framework is composed of a Decision support system which holds a knowledge management of cloud services. The information obtained in the first and second phase of our framework are represented in this phase. Furthermore, in this phase, intelligent system reasoning takes place in a bid to meet user requirements based a set of semantic rules. The cloud service knowledge management is presented in our ontology using protégé and this gives the DSS the ability to fetch accurate and timely information by matching user requirement with the required information, to make accurate and timely recommendation towards cloud service adoption. The fourth phase of our framework is the cloud service ranking stage. This is achieved based on a set of rules within the system ontology and these rules are set according to how the cloud services meet the acceptable bench mark assigned to each judgement criteria. We adopt a set of protocols which must be met for each cloud service to be ranked either 5 Star, 4 Star, 3 Star, 2 Star or 1 Star. Finally, we use a case study scenarios to prove that the use of a semantic web ontology can aid in the decision making process of cloud service adoption by SMEs when equipped with a knowledge of cloud services. Also, from our cloud service ranking, we conclude that Service B (not the real name) is the only cloud service to attain the 5 star ranking. The four major phases discussed above make up our framework in our bid to contribute to solving the slow adoption rate of cloud services by SMEs. Finally, from our implementation findings using 30 SMEs from various sectors, Service B was the most recommended service by our system based on user requirement while Price was the attribute the highest attribute considered by participating SMEs towards SaaS storage adoption.
10. Conclusions and Future Work
Cloud computing is seen as a technology that provides services over the internet just like public utilities. Many cloud service providers present cloud services in their own format as their no standardization for representing cloud services. Similarly, the presence of dominant players within the IT sector providing this technology shows that cloud computing is important in our world today. This has made it challenging for possible adopters especially in the SME sector to make a decision on which cloud service will best suit their company business process. Therefore, the need for a framework that will aid in cloud service adoption decision in terms of how service providers meet user requirement is vital.
The benefits of the proposed cloud service adoption framework are: The multi-tasking ability of our middleware tackles the issue of specific tasking associated with previous proposed systems, Our proposed ranking method can be adopted by other researchers in other fields, The proposed framework can also be used to compare other cloud service layers, it also has the ability to aid SME owners in cloud service adoption decision thereby tackling the issue of adoption complexity faced by prospective adopter. Furthermore, from our service ranking Service B (not real name) is the only cloud service to attain the 5 Star ranking which is the highest ranking within our framework. In addition, from our pivot studies, service was the most recommended service based on user requirement while price was the highest attribute required by users.
In future work, we intend to extend our work to the quantifiable quality of cloud services (IaaS & PaaS) as well as develop a system that can be used to aggregate the QOS configuration between cloud service layers in different applications.
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