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• W2290619914 abstract "The collocation of applications in the cloud each with unique traffic characteristics presents provisioning challenges to providers. Furthermore, the volatility present in traffic sent & received by these applications necessitates a predictive solution for effective QoS strategies. To comprehend the tasks faced by cloud providers in this regard, we undertook statistical analysis of traffic traces measured periodically over a week, taking into account its nonstationarity. Henceforth, we developed a model to predict bandwidth utilization applicable in maintaining SLAs for multiple traffic flows at the cloud network edge and core. The developed univariate forecast model employs the Auto-Regressive Integrated Moving Average (ARIMA) model augmented with a general class of Adaptive Conditional Score Models (ACS). Our motivation for employing the ACS stems from its robust adaptation to outliers & transients more efficiently with increased computational accuracy than current methods; one of such methods being the recently adopted Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) to model volatility. These are observable typically as flash-crowds, time-of-day load spikes and DDoS events. The model offers improvements in forecasts over ARIMA/GARCH models with a 10-15% increase in predictive accuracy. Based on this model, we present 2 novel bandwidth adaptive algorithms which find application in the cloud network. The first is resident in the application layer of the SDN stack as a controller integrated into the OpenStack cloud environment. It was tested for effectiveness in a small test-bed where it sustains SLAs at 95%. The second methodology has been adapted in ns-2 where we present it as R2CP: Robust Rate Control Protocol, an extension to the Rate Control Protocol (RCP). R2CP offers a 25% reduction in flow completion time over XCP/RCP & over 60% reduction compared to TCP." @default.
• W2290619914 created "2016-06-24" @default.
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• W2290619914 date "2015-12-01" @default.
• W2290619914 modified "2023-09-25" @default.
• W2290619914 title "An Adaptive Score Model for Effective Bandwidth Prediction and Provisioning in the Cloud Network" @default.
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• W2290619914 doi "https://doi.org/10.1109/glocomw.2015.7414021" @default.
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