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Service Composition

Let T be the task that the service composition needs to accomplish. The task T can be granulated to T 1 , T 2 , T 3 , T 4 , … , T n . i.e. T = {T 1 , T 2 , T 3 , T 4 , … , T n } . For each task T i , a set of service S i = S i 1 , S i 2 , S i 3 , … , S i m is discovered during the service discovery process such that all services in a set S i perform the same function and have the same input and output parameters (See Figure 2). S 1 = {S 11 , S 12 , S 13 , … , S 1m } , S 2 = {S 21 , S 22 , S 23 , … , S 2m } , S 3 = {S 31 , S 32 , S 33 , … , S 3m } , … , S n = {S n 1 , S n 2 , S n 3 , … , S n m } We need to select one service from each set S i in order to compose the big service such that the overall QoS attributes of the big service are optimal. The total number of the possible distinct service composition is n m . Let k be the the number of QoS attributes. Then the total num- ber of comparisons required are kn m . We need at least kn m comparisons to find whether the solution is optimal, thus making the problem as NP-Hard.

Papers

Showing 1120 of 45 papers

TitleStatusHype
Energy Loss Prediction in IoT Energy Services0
Optimization of Resource Service Composition in Cloud Manufacture Based on Improved Genetic and Ant Colony Algorithm0
HSC-Rocket: An interactive dialogue assistant to make agents composing service better through human feedback0
QoS-aware Big Service Composition using Distributed Co-Evolutionary AlgorithmCode0
FES: A Fast Efficient Scalable QoS Prediction Framework0
Drone-as-a-Service Composition Under Uncertainty0
Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction0
An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment0
Relational Model for Parameter Description in Automatic Semantic Web Service Composition0
Social-Sensor Composition for Tapestry Scenes0
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