Service selection on BigData-space based on heterogeneous QoS preferences

Service Composition is playing a pivotal role in diverse industries to approach more cost effective solutions in an efficient manner. Web service selection is playing a key role in the domain of the composition and it considers as the most important last informant of successful Automatic service composition. Since it is growing the web services in tremendous speed due to the innovation of cloud computing. It results to be available similar functionality over different providers. Then service selection based on Non-Functional Properties preferences becomes a significant factor in the service domain. Therefore, it arises severe concern in selection due to NP-hard problem and increasing the number of web services available. However, it has a considerable limitation in the conventional standalone computational platform of service selection. Therefore, we proposed service selection in distributed environment, which is BigData-space. Since Linear Programming (LP) plays major programming technique in service selection domain. Therefore, we select LP model to escalate it functionalities on BigData-space to improve the performance of the web service selection. We have identified, end-users selection is highly depend on custom QoS preferences on their selection. Therefore, we proposed LP based 0-1 Multi Choice Knapsack Problem to be satisfied the service selection in BigData-space.