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Scalable and Accurate Prediction of Availability
of Atomic Web Services
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.

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#1, Ist
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EXISTING SYSTEM:
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. LUCS addresses the
scalability issues of the existing availability prediction models by
partitioning user and service parameter values into discrete sets based on
their similarity
PROPOSED SYSTEM:
We proposed a novel approach for predicting the availability atomic web
services based on the collected past invocation data. Our model, LUCS,
builds on the prior work on availability prediction for atomic services
based on collaborative filtering algorithms. The LUCS model predicts
service availability based on the user’s geographic location, the geographic

3. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101
#1, Ist
Cross, Ist
Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com
www.ecruitments.com
location of the service, the current load of the service provider, and the
computational requirements of the service.
CONCLUSION:
In this paper we proposed a novel approach for predicting the availability
atomic web services based on the collected past invocation data. Our
model, LUCS, builds on the prior work on availability prediction for
atomic services based on collaborative filtering algorithms. The LUCS
model predicts service availability based on the user’s geographic location,
the geographic location of the service, the current load of the service
provider, and the computational requirements of the service. LUCS
addresses the scalability issues of the existing availability prediction models
by partitioning user and service parameter values into discrete sets based
on their similarity.