Zulfa, Mulki Indana and Hartanto, Rudy and Permanasari, Adhistya Erna and Ali, Waleed (2021) Web Caching Strategy Optimization Based on Ant Colony Optimization and Genetic Algorithm. In: 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), 21-22 July 2021, Surabaya.
Full text not available from this repository. (Request a copy)Abstract
Web caching is a strategy that can be used to speed up website access on the client-side. This strategy is implemented by storing as many popular web objects as possible on the cache server. All web objects stored on a cache server are called cached data. Requests for cached web data on the cache server are much faster than requests directly to the origin server. Not all web objects can fit on the cache server due to their limited capacity. Therefore, optimizing cached data in a web caching strategy will determine which web objects can enter the cache server to have maximum profit. This paper simulates a web caching strategy optimization with a knapsack problem approach using the Ant Colony optimization (ACO), Genetic Algorithm (GA), and a combination of the two. Knapsack profit is seen from the number of web objects that can be entered into the cache server but with the minimum objective function value. The simulation results show that the combination of ACO and GA is faster to produce an optimal solution and is not easily trapped by the local optimum. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Additional Information: | Cited by: 1 |
Uncontrolled Keywords: | Combinatorial optimization; Genetic algorithms; Intelligent systems; Profitability; Ant Colony Optimization (ACO); Cache servers; Client sides; Knapsack problems; Limited capacity; Maximum profits; Objective function values; Optimal solutions; Ant colony optimization |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 29 Oct 2024 07:11 |
Last Modified: | 29 Oct 2024 07:11 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8482 |