Name
Abstract | ||
|---|---|---|
For business processes based on micro service architecture in an enterprise, failures often occur because of modified business rules or goals, change of service availability, and dynamical running environment. Based on dynamical replanning technologies, these failures can be repaired at runtime. However, semantic conflicts among services from different providers can greatly decrease response efficiency and response success rate of a business goal. In this paper, we propose a novel service runtime self-adaptation framework to decrease response time and raise success rate. Distributed dynamic description logic is utilized to eliminate semantic conflicts among services and provide basic models for carrying out planning among services. Considering inputs, outputs, preconditions, and effects properties of services, local and global planning algorithms based on artificial intelligence graph planning are designed. Local planning can rapidly search a service-based path only including services from a provider, and global planning can try to explore a path including services from multiple providers. Based on these two algorithms, local and global replanning strategies are designed to handle runtime exceptions at service level and path level. We implement a prototype system by means of workflow engine Activiti and business process language BPMN2.0. Experiments show that compared with previous works, our framework can guarantee higher efficiency and success rate. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2810848 | IEEE ACCESS |
Keywords | Field | DocType |
Running self-adaptation,software service,graph planning,D3L,workflow | Ontology (information science),Service level,Business process,Computer science,Description logic,Workflow engine,Semantics,Business rule,Service-oriented architecture,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianghui Wang | 1 | 3 | 3.11 |
| Zhiyong Feng | 2 | 794 | 167.21 |
| Keman Huang | 3 | 242 | 22.29 |