An Overview of User-level Usage Monitoring in Cloud Environment

Cloud computing monitors applications, virtual and physical resources to ensure performance capacity, workload management, optimize future application updates and so on. Current state-of-the-art monitoring solutions in the cloud focus on monitoring in application/service level, virtual and physical (infrastructure) level. While some of the researchers have identified the importance of monitoring users, there is still need for developing solutions, implementation and evaluation in this domain. In this paper, we propose a novel approach to extract end-user usage of cloud services from their interactions with the interfaces provided to access the services called User-level Usage Monitoring. We provide the principles necessary for the usage data extraction process and analyse existing cloud monitoring techniques from the identified principles. Understanding end-user usage patterns and behaviour can help developers and architects to assess how applications work and which features of the application are critical for the users

Tram-tastic Cloud Computing

This master’s thesis evaluates the scalability and cost-effectiveness of the AWS cloud platform used to collect and utilize data generated by the 87 digitally equipped trams. The SL-18 Cloud Platform was developed before the trams arrived, and resource configuration estimates were made to handle the data generated by the trams. However, with a few trams currently operational, it is crucial to evaluate the allocation of resources to the services based on actual data. Thus, the thesis's objective is to estimate the data generated by all 87 trams and evaluate the current resource provisioning on the AWS Cloud Platform in terms of scalability and cost. By doing so, this study will provide insights into the optimal resource allocation required for the AWS Cloud Platform to accommodate the data generated by the trams. In this study, we use an existing Digital Twin tool for the trams to evaluate the scalability of the platform, ensuring that it can handle the load while keeping the cost low. To achieve this, the existing Digital Twin is modified to run 87 or more instances concurrently. Using this modified tool, the SL-18 IT platform, which processes real-time data from all 87 trams simultaneously, is evaluated. We monitored the metrics of AWS services to identify any issues. Then based on measurements, we make recommendations for each service's upgrading, downgrading, or keeping the current configuration. Most services are recommended to scale down to reduce costs, while three services require scaling up to be operational. Although our process is well-defined and could be replicated by other studies, it is crucial to have in-depth discussions with the relevant teams for each service and perform repeated validations and evaluations. This is also a necessary protocol in Sporveien to present the results to the various stakeholders and implement the recommended changes. With these changes, Sporveien can save costs and most importantly have a platform capable of handling the data load of 87 SL-18 trams

Right Scaling for Right Pricing: A Case Study on Total Cost of Ownership Measurement for Cloud Migration

Cloud computing promises traditional enterprises and independent software vendors a myriad of advantages over on-premise installations including cost, operational and organizational efficiencies. The decision to migrate software configured for on-premise delivery to the cloud requires careful technical consideration and planning. In this chapter, we discuss the impact of right-scaling on the cost modelling for migration decision making and price setting of software for commercial resale. An integrated process is presented for measuring total cost of ownership, taking in to account IaaS/PaaS resource consumption based on forecast SaaS usage levels. The process is illustrated with a real world case study

Making the Cloud Work for Software Producers: Linking Architecture, Operating Cost and Revenue

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