Profiling SLAs for cloud system infrastructures and user interactions

Abstract

Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this paper, we propose the CloudCost UML profile, which allows the modeling of cloud architectures and the users’ behavior when they interact with the cloud to request resources. We then investigate how to increase the profits of cloud infrastructures by using price schemes. For this purpose, we distinguish between two types of users in the SLAs: regular and high-priority users. Regular users do not require a continuous service, so they can wait to be attended to. In contrast, high-priority users require a constant and immediate service, so they pay a greater price for their services. In addition, a computer-aided design tool, called MSCC (Modeling SLAs Cost Cloud), has been implemented to support the CloudCost profile, which enables the creation of specific cloud scenarios, as well as their edition and validation. Finally, we present a complete case study to illustrate the applicability of the CloudCost profile, thus making it possible to draw conclusions about how to increase the profits of the cloud infrastructures studied by adjusting the different cloud parameters and the resource configuration.

Towards a GDPR-compliant cloud architecture with data privacy controlled through sticky policies

“…The complete detailed description is available in Appendix A . In previous works ( Bernal et al, 2019 ; Cambronero et al, 2021 ), we have presented some aspects of the cloud infrastructure, but without considering data storage and management. In contrast, in this work, we focus on this aspect of the cloud, defining a GDPR-compliant architecture to manage the data of users who access the cloud.…”

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

“…Dynamic container orchestration according to microservices interactivity patterns and concerning entities is an active research topic that has room for additional investigation. Notably, scientific research highlights two sorts of rating strategies, namely, profiling and ranking, for effective content distribution of interconnected services using the repairing genetic algorithm (RGA) [43], bubble-up and bubble-flux [44], the powerconscious model [45], service request routing optimization [46], resource balance ranking schema [47], ge-kube using Kubernetes for geographical distributions [48], energy usage in relation to processing intensive communication workloads [49], StressCloud to monitor cloud energy consumption [50], the positioning of microservice instances and resource distribution optimization [46], and CloudCost which used a UML profile to allow the modeling of user behavior and cloud architecture [51].…”

“…Figure 5. Key findings from the literature review of concepts, theories, and existing frameworks[6][7][8][9][10][11][12][13][14][15][18][19][20]23,29,33,[43][44][45][46][47][48][49][50][51].…”