Latency Efficient Cache Placement using Learning Techniques

Abstract:

This thesis addresses several challenges that are faced in developing a solution for cache placement at the edge of the network due to continuous changes in content popularity, user mobility, and number of users within each network. It also considers the challenges related to high computation requirements of future applications that need to satisfy power and delivery time constraints. This dissertation aims to overcome those challenges in developing new solutions by employing intelligence and machine learning techniques (ILT) for mobile edge networks. We formulate the cache placement problem as a latency efficient cache placement optimization problem that considers four objectives to place contents in SBSs and UT caches. The multi-objective function takes the advantages of user mobility pattern to decide on each SBS and UT cache content. The function is resolved into a weighted fusion decision with four objectives with four objectives, three of them are related to user mobility computed from previous data sets and one objective is related to content popularity. We study the impact of user mobility on increasing the cache hit rate, which decreases the latency of downloading the requested data content. The results show the impact of user mobility on reducing the total energy that is consumed for transmitting the contents to the UTs. We propose a new cache placement algorithm by formulating cache placement decision as a binary classification problem (to cache and not to cache) based on user locations, contact probability, communication range, contact duration, and content popularity. Artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR) are used to model cache placement decisions. We investigate the characteristics of the input features (attributes) and the properties of these characteristics that affect supervised machine learning approaches. The performance of new cache placement models using supervised learning techniques are evaluated to study the sensitivity of classification decisions with the change of system parameters. Finally, we develop a semi-supervised self-training (SSST) classification model for cache placement problem. We assess the proposed SSST algorithm through experiments with datasets on different learning techniques. The performance comparison of different machine learning models was carried out with the same datasets. For the hit rate we investigated the sensitivity of the classification by the changes in the environment parameters to show the effectiveness of the proposed theme.

(Anyone can attend this zoom session.  Please send an email to  Dr. A. Anpalagan or Dr. M. Jaseemuddin requesting the zoom URL and passcode)