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Long-Term IaaS Selection Using Performance Discovery
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DOI：10.1109/TSC.2020.3036677.png)
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摘要
En 中文
We propose a novel framework to select IaaS providers according to a consumer's long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.
Keyword:
Quality of service
Cloud computing
Throughput
Time factors
Benchmark testing
Australia
Service selection
long-term IaaS
temporal skyline
and cooperative performance prediction
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期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
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