Visible to the public Service Oriented Resilience Strategy for Cloud Data Center

TitleService Oriented Resilience Strategy for Cloud Data Center
Publication TypeConference Paper
Year of Publication2018
AuthorsLiu, Y., Li, X., Xiao, L.
Conference Name2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)
KeywordsBig Data, big data security, cloud computing, cloud data center, computer centres, computer networks, data centers, Fault tolerance, hierarchical colored generalized stochastic petri net, IT architecture, maintenance engineering, Measurement, Metrics, Petri nets, pubcrawl, quality of service, resilience, resilience metric, Resilience strategy, Resiliency, Scalability, Servers, service oriented resilience, Stochastic processes, system resilience
AbstractAs an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.
DOI10.1109/QRS-C.2018.00056
Citation Keyliu_service_2018