Discussions

To conclude, the presented experiments showed that MonSLAR indicates a clear improvement in terms of the message size overhead, where it introduces much better performance than PM because of the use of REST technology in comparison to the SOAP protocol. The study revealed that the message size overhead of MonSLAR is approximately five times less than the message size overhead caused by using SOAP in previous research (PM); this is due to the use of XML format in SOAP protocol. The results produced in this study corroborate the findings of a great deal of the previous research in this field (section 2.4.2.3). These results are consistent with those described by Mumbaikar and Padiya (2013) that confirmed the increase in the message size to be five times less in REST technology in comparison to SOAP (Mumbaikar & Padiya, 2013).

Moreover, there are similarities between the attitudes expressed by MonSLAR and those presented by Markey and Clynch (2013). Their study showed the decrease in the message size due to the use of REST technology in comparison with SOAP technology especially in the case of using JSON files, this reduction in message size in REST was two times less from that in SOAP (Markey & Clynch, 2013).

Furthermore, the study presented by Mulligan and Gra (2009) revealed that there was a reduction in the packet size for the case of using REST in comparison to SOAP technology, the results of their study showed that the packet size in REST is approximately two times less than packet size in SOAP protocol (Mulligan & Gra, 2009). It is important to be mentioned that their study considered the CRUD methods using GET, PUT, POST, and DELETE methods; while in MonSLAR, embedding the data in the header of OPTIONS method was investigated.

These results also accord with the study presented by Mohamed and Wijesekera (2012), who discussed the difference between the message size of SOAP and REST. They introduced a sample for each case showed that the payload’s message size overhead in REST is twenty-five times less than in SOAP protocol (Mohamed & Wijesekera, 2012). This is due to sending the data in the message payload. Again, the data in MonSLAR was sent within the header of the message in REST OPTIONS method.

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At the same time, MonSLAR makes an enhancement to the monitoring process by delivering the monitored data to the client side in addition to the SaaS service using REST architecture, by embedding the monitored data in the response of the SaaS REST service. MonSLAR also provides information about overall user satisfaction using a decision making tool. The experiments also explored the behaviour of the monitoring system, which revealed that the response time overhead with and without using MonSLAR is comparable.

The qualitative evaluation revealed that MonSLAR outperforms the other monitoring frameworks in the research field. This was achieved by comparing the features of MonSLAR with the available monitoring frameworks. These features involved the following: the ability of MonSLAR to detect any SLA violations automatically; in addition to real-time records of the monitored data. Furthermore, an automatic notify- ability feature that helps the client of the SaaS services in controlling the negotiated services. These features save the need for a third party service to achieve the monitoring process. Not to forget the communication architecture feature, which considers the use of a middleware as a tool to manage the monitoring process. MonSLAR uses SOM that provides loose coupling and reuse capabilities to the provided services; besides the interaction type used for managing the web services, MonSLAR uses REST technology, which adds lightweight characteristics and reduces the need for the use of technologies like SOAP to transmit the monitored data.

On the other hand, the user study for validating the SaaS-Qual metric showed that the QoE value depends on the combination of the model’s parameters. However, the study tells that some of the parameters have higher effects on user satisfaction than others. In the current study, comparing the results obtained from both the fuzzy engine test with the study survey results indicate that Security has the highest priority for the SaaS users. Another finding was that Reliability has the second highest effect on user satisfaction. Whereas Features parameter was found to have the third highest priority, followed by Responsiveness and Flexibility having the fourth highest priority, while Rapport has the lowest priority among the parameters.

The findings of the user study have implications for adjusting the rules of the proposed fuzzy engine. It is interesting to note that the results obtained from the adjusted system revealed an overall improvement in the QoE level.

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One of the issues revealed from these findings was that the SaaS-Qual model parameters can be used to estimate the QoE value, but an adaption is required to adjust the parameters weights according to the users’ satisfaction and requirements.

7.7

Chapter Summary

This chapter presented a test for the main functionality of MonSLAR to present user- centric monitoring, in addition to an evaluation of the performance of the proposed middleware. The evaluation methods showed the ability of the middleware to achieve the aim of the research of monitoring the QoE value, and provided user centric monitoring using the REST architecture methods with an acceptable performance in comparison with the monitoring frameworks in the literature review.

Three different approaches have been used to evaluate MonSLAR. Firstly, a quantitative evaluation is used to investigate the overhead caused by MonSLAR in terms of the message size. Secondly, the proposed middleware is evaluated using qualitative study by comparing the main characteristics of MonSLAR with the available monitoring frameworks presented in the literature review. Finally, another evaluation has been introduced to evaluate estimating the QoE value using fuzzy logic; this was achieved by conducting a questionnaire survey.

The next chapter concludes the thesis, discussing the main achievements of the research and proposing a set of recommendations for improvements in future research.

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