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PROXY CACHE CLEANUP IMPROVEMENT BY USING AN AGENT-BASED MODEL

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dc.contributor.author Sirour, Hiba Ali Nasir
dc.contributor.author Supervisor, - Yahia Abdalla Mohammed
dc.contributor.author Co Supervisor:, - Amir Abdelfattah Ahmed Eisa
dc.contributor.author Supervisor - Yahia Abdalla Mohammed CO-Supervisor - Amir Abdelfattah Ahmed Eisa
dc.date.accessioned 2016-01-25T10:27:57Z
dc.date.available 2016-01-25T10:27:57Z
dc.date.issued 2015-10-14
dc.identifier.citation Sirour , Hiba Ali Nasir . PROXY CACHE CLEANUP IMPROVEMENT BY USING AN AGENT-BASED MODEL \ Hiba Ali Nasir Sirour ; Yahia Abdalla Mohammed .- Khartoum:Sudan University of Science and Technology,Faculty of Computer Science and Information Technology,2015.-185 p:ill;28cm.-phD en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/12575
dc.description Thesis en_US
dc.description.abstract Web proxy caching is one of the effective solutions to avoid web service bottleneck, reduce traffic over the Internet and improve scalability of the web service. The core of a caching system is the caching replacement policies. This study describes the use of intelligent agent model to improve the performance of the proxy cache. A multi-agent system has been developed to control the cache cleanup task on the hierarchy caches. Fuzzy logic is used to combine LFU, LRU and Size caching replacement policies on the parent cache side. LFU and LRU policies are used on the child caches side. Cache cleaner Agents use fuzzy logic to make an intelligent decision and remove the web object proactively when it has high clean up priority. Reactive Coordination has been applied between the parent and child cleaner agents to achieve the cleanup task in efficient way, they have a common goal to increase hit ratio and byte hit ratio. Coordination agent applied the coordination rules when the web object with medium priority is found in parent and children caches. Q- learning algorithm has been implemented by the cleaner agent to avoid difficult calculation when it reached a similar state and take a suitable action. A reward value has been associated to each action, when Cleaner agent takes its optimal action that leads to the goal, it has an instant high reward. Other actions have low reward. States and actions had been represented on a graph each node represented a "state", agent's movement from one node to another represented the "action". The model has been tested using five samples of workload generated using Webtraff simulator, these samples represented the users requests and used cache sizes. The standard performance metrics Hit Ratio and byte hit ratio are used to evaluate the cache performance. Simulation results show that when the cache size increase the new approach PCCIA performs better than LRU,LFU and Size replacement polices in terms of hit rate and byte hit rate. en_US
dc.description.sponsorship Sudan University of Science & Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science & Technology en_US
dc.subject information technology en_US
dc.subject Software Engineering en_US
dc.subject PROXY CACHE CLEANUP en_US
dc.subject IMPROVEMENT BY USING en_US
dc.title PROXY CACHE CLEANUP IMPROVEMENT BY USING AN AGENT-BASED MODEL en_US
dc.title.alternative استخدام انموذج العميل لتحسين تفريع ذاكرة الوكيل المحبأه en_US
dc.type Thesis en_US


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