@inproceedings { ISCC2023,
	title = {Proactive Resource Orchestration Framework for Cloud/Fog Platform},
	journal = {Proceedings of the 28th IEEE Symposium on Computers and Communications (ISCC)},
	year = {2023},
	publisher = { IEEE},
	address = {Tunis/Tunisia},
	abstract = {Cloud computing makes complex computing an off-premise activity by offering software- and hardware-based services using standard security protocols over the Internet. It has been seen that the cloud is not ideal for latency-sensitive applications. Thanks to the current growth of network communication and infrastructure, fog adds a computing resource delegation layer between the user and the cloud. Fog aims to improve latency-sensitive applications support. Here, we propose one unified, proactive resource orchestration framework from a cloud/fog service provider perspective. The framework consists of a predictor and a resource allocator module. Users subscribe to these resources to execute their applications. The framework does not require application-specific information and is modular, meaning a service provider can customise each module. We have presented the framework prototype by showing each module{\textquoteright}s simulated performance results using the parameters of our cloud/fog research test bed.},
	keywords = {Cloud, Fog, Orchestration, Prediction, Resource Allocation},
	author = {Mazumdar, Somnath and Dreibholz, Thomas}
}
@article {26067,
	title = {The boundary effect: Perceived post hoc accuracy of prediction intervals},
	journal = {Journal of Judgment and Decision Making},
	volume = {13},
	number = {4},
	year = {2018},
	month = {07/2018},
	pages = {309{\textendash}321},
	publisher = {Society for Judgment and Decision Making},
	abstract = {Predictions of magnitudes (costs, durations, environmental events) are often given as uncertainty intervals (ranges). When are such forecasts judged to be correct? We report results of four experiments showing that forecasted ranges of expected natural events (floods and volcanic eruptions) are perceived as accurate when an observed magnitude falls inside or at the boundary of the range, with little regard to its position relative to the {\textquotedblleft}most likely{\textquotedblright} (central) estimate. All outcomes that fell inside a wide interval were perceived as equally well captured by the forecast, whereas identical outcomes falling outside a narrow range were deemed to be incorrectly predicted, in proportion to the magnitude of deviation. In these studies, ranges function as categories, with boundaries distinguishing between right or wrong predictions, even for outcome distributions that are acknowledged as continuous, and for boundaries that are arbitrarily defined (for instance, when the narrow prediction interval is defined as capturing 50 percent and the wide 90 percent of all potential outcomes). However, the boundary effect is affected by\&nbsp;label. When the upper limit of a range is described as a value that {\textquotedblleft}can{\textquotedblright} occur (Experiment 5), outcomes both below and beyond this value\&nbsp;were\&nbsp;regarded as consistent with the forecast.},
	keywords = {forecasting, Prediction, prediction intervals},
	author = {Teigen, Karl-Halvor and L{\o}hre, Erik and Sigrid M{\o}yner {Hohle}}
}
