Scalable and Accurate Prediction of Availability of Atomic Web Services. Silic, M., Delac, G., Krka, I., & Srbljic, S. IEEE TRANSACTIONS ON SERVICES COMPUTING, 7(2):252–264, IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA, 2014. doi abstract bibtex The modern information systems on the Internet are often implemented as composite services built from multiple atomic services. These atomic services have their interfaces publicly available while their inner structure is unknown. The quality of the composite service is dependent on both the availability of each atomic service and their appropriate orchestration. In this paper, we present LUCS, a formal model for predicting the availability of atomic web services that enhances the current state-of-the-art models used in service recommendation systems. LUCS estimates the service availability for an ongoing request by considering its similarity to prior requests according to the following dimensions: the user's and service's geographic location, the service load, and the service's computational requirements. In order to evaluate our model, we conducted experiments on services deployed in different regions of the Amazon cloud. For each service, we varied the geographic origin of its incoming requests as well as the request frequency. The evaluation results suggest that our model significantly improves availability prediction when all of the LUCS input parameters are available, reducing the prediction error by 71 percent compared to the current state-of-the-art.
@article{WOS:000337901500010,
abstract = {The modern information systems on the Internet are often implemented as
composite services built from multiple atomic services. These atomic
services have their interfaces publicly available while their inner
structure is unknown. The quality of the composite service is dependent
on both the availability of each atomic service and their appropriate
orchestration. In this paper, we present LUCS, a formal model for
predicting the availability of atomic web services that enhances the
current state-of-the-art models used in service recommendation systems.
LUCS estimates the service availability for an ongoing request by
considering its similarity to prior requests according to the following
dimensions: the user's and service's geographic location, the service
load, and the service's computational requirements. In order to evaluate
our model, we conducted experiments on services deployed in different
regions of the Amazon cloud. For each service, we varied the geographic
origin of its incoming requests as well as the request frequency. The
evaluation results suggest that our model significantly improves
availability prediction when all of the LUCS input parameters are
available, reducing the prediction error by 71 percent compared to the
current state-of-the-art.},
address = {10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA},
author = {Silic, Marin and Delac, Goran and Krka, Ivo and Srbljic, Sinisa},
doi = {10.1109/TSC.2013.3},
issn = {1939-1374},
journal = {IEEE TRANSACTIONS ON SERVICES COMPUTING},
keywords = {Prediction; QoS; availability; web services},
number = {2},
pages = {252--264},
publisher = {IEEE COMPUTER SOC},
title = {{Scalable and Accurate Prediction of Availability of Atomic Web Services}},
type = {Article},
volume = {7},
year = {2014}
}
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