Name
Abstract | ||
|---|---|---|
Context-aware adaptation service is rapidly becoming an important issue. This service overcomes the limitations of wireless devices and maintains adequate service levels in changing environments. The majority of existing studies concentrate on adaptation modules on the client, proxy, or server. These existing studies thus suffer from the problem of having the workload concentrated on a single system. This means that increasing the number of users increases the response time of a user’s request. In this paper, the adaptation module is dispersed and arranged over the client, proxy, and server. The module monitors the context of each system and creates a dispersed adaptation system, optimized for efficient operation. Through this method, faster adaptation work is made possible, even when the number of users increases, and the dividing workload makes more stable system operation possible. In order to evaluate the proposed system, a prototype is constructed and dispersed operations are tested using multimedia content, simulating server overload and comparing the response times and system stability with existing server based adaptation systems. The effectiveness of the system is confirmed through the results. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11875581_144 | IDEAL |
Keywords | Field | DocType |
context-aware adaptation service,response time,hybrid system,simulating server overload,faster adaptation work,adaptation module,proposed system,adaptation system,dynamic web-content adaptation,system stability,single system,stable system operation,service level,adaptive modulation,adaptive system | Computer science,Server,Response time,Artificial intelligence,Content management,Dynamic web page,Content adaptation,Distributed computing,Service level,Workload,Hybrid system,Machine learning,Embedded system | Conference |
Volume | ISSN | ISBN |
4224 | 0302-9743 | 3-540-45485-3 |
Citations | PageRank | References |
1 | 0.36 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaewoo Cho | 1 | 11 | 1.78 |
| Seunghwa Lee | 2 | 20 | 4.68 |
| Eunseok Lee | 3 | 227 | 47.07 |