SLA Violation Detection Model and SLA Assured Service Brokering (SLaB) in Multi-Cloud Architecture

Cloud brokering facilitates CSUs to find cloud services according to their requirements. In the current practice, CSUs or Cloud Service Brokers (CSBs) select cloud services according to SLA committed by CSPs in their website. In our observation, it is found that most of the CSPs do not fulfill the service commitment mentioned in the SLA agreement. Verified cloud service performances against their SLA commitment of CSPs provide an additional trust on CSBs to recommend services to the CSUs. In this thesis work, we propose a SLA assured service-brokering framework, which considers both committed and delivered SLA by CSPs in cloud service recommendation to the users. For the evaluation of the performance of CSPs, two evaluation techniques: Heat Map and IFL are proposed, which include both directly measurable and non-measurable parameters in the performance evaluation CSPs. These two techniques are implemented using real data measured from CSPs. The result shows that Heat Map technique is more transparent and consistent in CSP performance evaluation than IFL technique. In this work, regulatory compliance of the CSPs is also analyzed and visualized in performance heat map table to provide legal status of CSPs. Moreover, missing points in their terms of service and SLA document are analyzed and recommended to add in the contract document. In the revised European GPDR, DPIA is going to be mandatory for all organizations/tools. The decision recommendation tool developed using above mentioned evaluation techniques may cause potential harm to individuals in assessing data from multiple CSPs. So, DPIA is carried out to assess the potential harm/risks to individuals due to our tool and necessary precaution to be taken in the tool to minimize possible data privacy risks. It also analyzes the service pattern and future performance behavior of CSPs to help CSUs in decision making to select appropriate CSP

Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible, but prolonged observations provide more reliable information. However, due to technical complexities and costs of monitoring services, it may not be possible to access the service performance of cloud provider for longer time durations. The extended observation periods are also a necessity for prediction of future behavior of services. These predictions have very high value for decision making both for private and corporate cloud users, as the uncertainty about the future performance of purchased cloud services is an important risk factor. Predictions can be used by specialized entities, such as cloud service brokers (CSBs) to optimally recommend cloud services to the cloud users. In this paper, we address the challenge of prediction. To achieve this, the current service performance patterns of cloud providers are analyzed and future performance of cloud providers are predicted using to the observed service performance data. It is done using two automatic predicting approaches: ARIMA and ETS. Error measures of entire service performance prediction of cloud providers are evaluated against the actual performance of the cloud providers computed over a period of one month. Results obtained in the performance prediction show that the methodology is applicable for both short- term and long-term performance prediction

Comparisons of Heat Map and IFL Technique to Evaluate the Performance of Commercially Available Cloud Providers

Cloud service providers (CSPs) offer different Ser- vice Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to users requirements. Generally, a CSB considers the service commitments of CSPs rather than the actual quality of CSPs services. To overcome this issue, the broker should verify the service performances while recommending cloud services to the cloud users, using all available data. In this paper, we compare our two approaches to do so: a min-max-min decomposition based on Intuitionistic Fuzzy Logic (IFL) and a Performance Heat Map technique, to evaluate the performance of commercially available cloud providers. While the IFL technique provides simple, total order of the evaluated CSPs, Performance Heat Map provides transparent and explanatory, yet consistent evaluation of service performance of commercially available CSPs. The identified drawbacks of the IFL technique are: 1) It does not return the accurate performance evaluation over multiple decision alternatives due to highly influenced by critical feedback of the evaluators; 2) Overall ranking of the CSPs is not as expected according to the performance measurement. As a result, we recommend to use performance Heat Map for this problem

SLA Violation Detection Model and SLA Assured Service Brokering (SLaB) in Multi-Cloud Architecture

Cloud brokering facilitates Cloud Service Users (CSUs) to find cloud services according to their requirements. In the current practice, CSUs or Cloud Service Brokers (CSBs) select cloud services according to Service Level Agreement (SLA) committed by Cloud Service Providers (CSPs) in their website. In our observation, it is found that most of the CSPs do not fulfill the service commitment mentioned in the SLA agreement. Verified cloud service performances against their SLA commitment of CSPs provide an additional trust on CSBs to recommend services to the CSUs. In this thesis work, we propose a SLA assured service-brokering framework, which considers both committed and delivered SLA by CSPs in cloud service recommendation to the users. For the evaluation of the performance of CSPs, two evaluation techniques: Heat Map and Intuinistic Fuzzy Logic (IFL) are proposed, which include both directly measurable and non-measurable parameters in the performance evaluation CSPs. These two techniques are implemented using real data measured from CSPs. Both performance evaluation techniques rank/- sort CSPs according to their service performances. The result shows that Heat Map technique is more transparent and consistent in CSP performance evaluation than IFL technique. As cloud computing is location independent technology, CSPs should respect the current regulatory framework in delivering services to the users. In this work, regulatory compliance status of the CSPs is also analyzed and visualized in performance heat map table to provide legal status of CSPs. Moreover, missing points in their terms of service and SLA document are analyzed and recommended to add in the contract document. In the revised European data protection regulation (GPDR), data protection impact assessment (DPIA) is going to be mandatory for all organizations/tools. The decision recommendation tool developed using above mentioned evaluation techniques may cause potential harm to individuals in assessing data from multiple CSPs. So, DPIA is carried out to assess the potential harm/risks to individuals due to decision recommendation tool and necessary precaution to be taken in decision recommendation tool to minimize possible data privacy risks. To help CSUs in easy decision making to select cloud services from multi-cloud environment, service pattern analysis techniques and prediction of future performance behavior of CSPs are also proposed in the thesis work. Prediction patterns and error measurement shows that automatic prediction methods can be implemented for short time period as well as longer time period