Journal
IEEE TRANSACTIONS ON CLOUD COMPUTING
Volume 6, Issue 4, Pages 977-990Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2016.2541141
Keywords
Optimization of services systems; services management; bridging business and IT architecture; installation management; simulation; modeling; visualization
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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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