Download Advances in Information Technology: 6th International by Komate Amphawan, Philippe Lenca (auth.), Borworn PDF

By Komate Amphawan, Philippe Lenca (auth.), Borworn Papasratorn, Nipon Charoenkitkarn, Vajirasak Vanijja, Vithida Chongsuphajaisiddhi (eds.)

This ebook constitutes the lawsuits of the sixth foreign convention on Advances in info know-how, IAIT 2013, held in Bangkok, Thailand, in December 2013. The 23 revised papers provided during this quantity have been conscientiously reviewed and chosen from a number of submissions. They take care of all components relating to utilized info technology.

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Additional info for Advances in Information Technology: 6th International Conference, IAIT 2013, Bangkok, Thailand, December 12-13, 2013. Proceedings

Example text

It consists of two main processing modules, forecaster and cloud cache replacement. They operate as follows. , a request for an object that was in a cache but has been since purged, thus cache eviction is required to serve the request [3]) takes place, the vector of k lru cached objects’ profits together with a required cache space are fed into the forecaster to forcast a near-optimal window size. During this stage, each input vector is passed internally into the input vector normalization process then the normalized vector is presented to the MLP component to forecast a low-level window size.

As an early attempt in the new field of client-side shared cloud caching, this paper presents a novel intelligent cloud cache replacement policy, i-Cloud (named so for its intended application domain), along with technical and economical performance results and significant findings based on real HTTP traces. 2 Related Works We have investigated an extensive number of nonintelligent web cache replacement policies [12] as summarized in our previous work [6]. NNPCR [9] and its extension, NNPCR-2 [14], apply artificial neural networks to rate object cacheability.

The perceptrons’ inputs are frequency, recency and size. Intelligent long-term cache removal algorithm based on adaptive neuro-fuzzy inference system (ANFIS) [1] takes access frequency, recency, object size and downloading latency as the inputs of a trained ANFIS to dictate noncacheable objects. In training ANFIS, objects requested again at later point in specific time are considered cacheable. The oldest noncacheable object is removed first. Intelligent web caching using artificial neural network and particle swarm optimization algorithm [15] trained the network to keep slow downloading, big and frequently accessed objects in cache.

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