A Vendor Neutral Quality of Service Monitoring Model for Software as a Service Cloud Computing Solutions

Abstract

The large number of providers of cloud services, offering comparable solutions marketed at different prices and at distinctive Quality of Service (QoS) levels, portends a decision challenge to users. The users have to make a selection or a comparison between the available providers of cloud services in so far as performance of their cloud solutions is concerned. Even though there exists computational models for developing QoS measuring tools, they are not vendor agnostic therefore hampering cross vendor performance comparison. To abate the decision challenge and enable cross cloud performance comparison, various research have been done culminating in probable solutions, like the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Heterogeneous Similarity Metrics (HSM), Event Based Multi Cloud Service Applications Framework, Multiple-Cloud Monitoring platform, Multicloud Security Applications (MUSA) framework, the PeRformance Evaluation of SErvices on the Cloud (PRECENSE) framework and Cross-Layer MultiCloud Application Monitoring with Benchmarking as a Service (CLAMBS). Whereas there is existence of research meant to address the cross cloud performance comparison, the shortcoming is that they rely on the use of existing vendor specific tools, customized for the specific cloud providers’ infrastructure which are then spread across different cloud providers, while in some instances the use of customized software agents installed in various cloud providers’ platform, and use of synthetically generated data.

This research addressed the existing gap by developing a cloud QoS monitoring framework from which a vendor agnostic cloud QoS monitoring model was designed. The focus was on Software as a Service (SaaS) cloud computing solutions. In designing of the model, the research focused on the location of the QoS monitoring tool, the intention of monitoring, and the mode of access to the cloud services. The QoS parameters monitored by the vendor neutral tool were service stability, service response time and service availability, which are the main quantitative parameters for cloud QoS as far as performance is concerned. The tool was subjected to Google docs and Microsoft 365 cloud services for comparison performance, under the same computing platform and Internet conditions. From the comparison, the average service response time for Google was 4.47 seconds while for Microsoft was 6.04 seconds. Both platforms had an availability of 100% since at no time during the testing period did any of the platform report a platform failure that would have led to outage of services. Whereas the availability is 100%, the fluctuations in the service response time were higher for Microsoft at 5.966 seconds than for Google at 2.003 seconds, meaning the Google platform was more stable than the Microsoft platform. From the trust evaluation, it was noted that the two compared cloud providers, Google and Microsoft, were both trustable since the results they reported were within the confidence interval of those reported by the vendor neutral model. Further research could be extended to monitor Infrastructure as a Service and Platform as a Service solutions. Advanced studies could also focus on other common aspects used by all cloud providers at the client side, for example the operating system, where the monitoring capability could be installed as a utility on the operating system.