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- W2947930713 abstract "The issue of less-than-100% reliability and trustworthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed ’gold-standard’. A cloud-based content distribution network (CDN) case study is described to illustrate our QoS assessment method." @default.
- W2947930713 created "2019-06-07" @default.
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- W2947930713 date "2019-04-08" @default.
- W2947930713 modified "2023-09-23" @default.
- W2947930713 title "Model-based System Identification for Cloud Services Analytics" @default.
- W2947930713 hasPublicationYear "2019" @default.
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