Business-driven service placement for highly dynamic and distributed cloud systems

Abstract

The emergence of large-scale Cloud computing environments characterized by dynamic resource pricing schemes enables valuable cost saving opportunities for service providers that could dynamically decide to change the placement of their IT service components in order to reduce their bills. However, that requires new management solutions to dynamically reconfigure IT service components placement, in order to respond to pricing changes and to control and guarantee the high-level business objectives defined by service providers. This paper proposes a novel approach based on Genetic Algorithm (GA) optimization techniques for adaptive business-driven IT service component reconfiguration. Our proposal allows to evaluate the performance of complex IT services deployments over large-scale Cloud systems in a wide range of alternative configurations, by granting prompt transitions to more convenient placements as business values and costs change dynamically. We deeply assessed our framework in a realistic scenario that consists of 2-tier service architectures with real-world pricing schemes. Collected results show the effectiveness and quantify the overhead of our solution. The results also demonstrate the suitability of business-driven IT management techniques for service components placement and reconfiguration in highly dynamic and distributed Cloud systems

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“…This manuscript is an extension of previous works [9], [8] and [10] published, respectively, at the TCC journal, and at the CNSM 2018 and the IM 2019 conferences. Those papers illustrated the models adopted by BDMaaS+ for businesslevel performance assessment of IT services in hybrid Cloud environments and showcased the effectiveness of our solution for what-if scenario analysis purposes.…”

Energy aware cloud‐edge service placement approaches in the Internet of Things communications

“…Tortonesi et al 42 provided an approach to manage IT service components placement in a business driven management as a service (BMDMaas) model. As the cloud computing deploys so the cloud providers need reconfiguration in their systems.…”

Auto-Scaling Web Applications in Clouds

“…Regarding the second problem type, [Tortonesi and Foschini 2016] proposed a genetic-based algorithm to search the deployment of a two-tier application across multiple clouds with minimum resource and SLA violation cost. [Rochman et al 2014] modeled the problem as a min-cost flow problem and solved it with Bipartite Graph Algorithm.…”