Cloud computing is in current demand and widely used for businesses because of its scalability and flexibility. An agreement is signed between cloud providers and customers to guarantee the level of services are available as promised, it is called Service Level Agreement (SLA). To assess if the services are compliant with SLA, availability of the cloud services is important to be monitored. There are some existing monitoring tools provided by the cloud service providers. However, those can be often biased that leads to calls for independent SLA evaluation. This study aims to solve the problem of verifying service availability parameters in terms of uptime/downtime against SLA by proposing a monitoring tool. This paper focuses on reliable assessment of SLA for both customers and service providers. Furthermore, this research discusses the challenges of SLA tracking and consequences of not assessing it. This study also explores that automated monitoring systems minimizes human-effort, time and the potential risk of errors. This approach is even more essential for organizations that use multiple cloud services from different vendors. The proposed monitoring tool can automatically verify the SLA by monitoring the availability of the cloud services. To avoid business losses and maintain reputation for customer trust, it is crucial to validate the SLA. The research problem related to SLA monitoring is challenging in terms of relevant previous studies of this area. Using experiment-based research strategy and quantitative data analysis method, the results have shown that the proposed solution is able to monitor the service availability and validate with SLA. The recommended solution is of significant interest to organizations as they are migrating and using single or multiple cloud services by different vendors as per the business need. The monitoring system serves as a valuable tool for both customers and service providers to solve issues related to availability monitoring of the cloud services and ensure the SLA commitment is met.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-220368 |
| Date | January 2023 |
| Creators | Gupta, Sneha, Anika, Mosammath Nazifa Anjum Islam |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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