Service Composition
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ojrlopez27/copernic
공식 코드Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing
https://github.com/ojrlopez27/nl-service-composition
공식 코드NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
https://gitlab.com/xrl2/chat4xai
공식 코드An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.