The document describes LUCS, a model for predicting the availability of atomic web services. LUCS estimates availability based on prior requests' similarity according to location of the user and service, service load, and computational requirements. An evaluation on Amazon cloud services showed LUCS significantly improves predictions by reducing error by 71% compared to other models when all LUCS parameters are available. It addresses scalability issues by partitioning parameters into discrete sets based on similarity.

1. GLOBALSOFT TECHNOLOGIES
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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.

2. 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 availabilityfor 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 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 basedon 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.

3. SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.