Name
Abstract | ||
|---|---|---|
We propose a simple scheme for selecting objects (either for caching or picking objects that are peers in a peer-to-peer system) to maximize the long-run reward obtained by a system. Our approach does not require a detailed record of the value specific objects add to the system. Therefore, it can be implemented using less computing and memory resources as compared to machine learning and artificial intelligent algorithms. One of the main contributions we make is to show that one can still derive optimal policies. The model that we use and the policy that we present are both applicable in a variety of contexts. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11277-012-0951-5 | Wireless Personal Communications |
Keywords | Field | DocType |
Optimization,Object selection,Caching policy | Computer science,Real-time computing,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
71 | 4 | 0929-6212 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amr Alasaad | 1 | 48 | 7.85 |
| Sathish Gopalakrishnan | 2 | 426 | 33.10 |
| Victor C. M. Leung | 3 | 9717 | 759.02 |