抄録
Information Centric Networks (ICNs) is a new architecture for the Future Internet to deliver content at large-scale. It relies on named data and caching features, which consists of storing content across the delivery path to serve forthcoming requests. In this paper, we study the problem of finding the optimal assignment of the objects in the available caches in ICN. The optimization problem is to cache objects in order to minimize overall network overhead. We formulate this problem as a combinatorial optimization problem. We show that this optimization problem is NP complete. Metaheuristic methods are considered as effective methods for solving this problem, Genetic Algorithm (GA) is one of those algorithms that can solve this problem efficiently. We will adapt cache management system based on GA for solving the considered problem. In contrast to traditional locally caching systems this algorithm consider both global and local search and make caching decisions about where and which item will be cached in order to minimize overall network overhead.
| 元の言語 | 英語 |
|---|---|
| DOI | |
| 出版物ステータス | 出版済み - 6 18 2014 |
| イベント | 9th International Conference on Future Internet Technologies, CFI 2014 - Tokyo, 日本 継続期間: 6 18 2014 → 6 20 2014 |
その他
| その他 | 9th International Conference on Future Internet Technologies, CFI 2014 |
|---|---|
| 国 | 日本 |
| 市 | Tokyo |
| 期間 | 6/18/14 → 6/20/14 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
これを引用
Distributed GA for popularity based partial cache management in ICN. / Mohammed A.a., Alaa; Okamura, Koji.
2014. 論文発表場所 9th International Conference on Future Internet Technologies, CFI 2014, Tokyo, 日本.研究成果: 会議への寄与タイプ › 論文
}
TY - CONF
T1 - Distributed GA for popularity based partial cache management in ICN
AU - Mohammed A.a., Alaa
AU - Okamura, Koji
PY - 2014/6/18
Y1 - 2014/6/18
N2 - Information Centric Networks (ICNs) is a new architecture for the Future Internet to deliver content at large-scale. It relies on named data and caching features, which consists of storing content across the delivery path to serve forthcoming requests. In this paper, we study the problem of finding the optimal assignment of the objects in the available caches in ICN. The optimization problem is to cache objects in order to minimize overall network overhead. We formulate this problem as a combinatorial optimization problem. We show that this optimization problem is NP complete. Metaheuristic methods are considered as effective methods for solving this problem, Genetic Algorithm (GA) is one of those algorithms that can solve this problem efficiently. We will adapt cache management system based on GA for solving the considered problem. In contrast to traditional locally caching systems this algorithm consider both global and local search and make caching decisions about where and which item will be cached in order to minimize overall network overhead.
AB - Information Centric Networks (ICNs) is a new architecture for the Future Internet to deliver content at large-scale. It relies on named data and caching features, which consists of storing content across the delivery path to serve forthcoming requests. In this paper, we study the problem of finding the optimal assignment of the objects in the available caches in ICN. The optimization problem is to cache objects in order to minimize overall network overhead. We formulate this problem as a combinatorial optimization problem. We show that this optimization problem is NP complete. Metaheuristic methods are considered as effective methods for solving this problem, Genetic Algorithm (GA) is one of those algorithms that can solve this problem efficiently. We will adapt cache management system based on GA for solving the considered problem. In contrast to traditional locally caching systems this algorithm consider both global and local search and make caching decisions about where and which item will be cached in order to minimize overall network overhead.
UR - http://www.scopus.com/inward/record.url?scp=84954489186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954489186&partnerID=8YFLogxK
U2 - 10.1145/2619287.2619305
DO - 10.1145/2619287.2619305
M3 - Paper
AN - SCOPUS:84954489186
ER -