Adaptive Caching Networks With Optimality Guarantees - 2018


We have a tendency to study the optimal placement of content over a network of caches, a problem naturally arising in several networking applications. Given a demand of content request rates and ways followed, we tend to would like to see the content placement that maximizes the expected caching gain, i.e., the reduction of routing prices because of intermediate caching. The offline version of this drawback is NP-arduous and, normally, the demand and topology might be a priori unknown. Hence, a distributed, adaptive approximation algorithm for inserting contents into caches is desired. We have a tendency to show that path replication, a straightforward algorithm frequently encountered in literature, will be arbitrarily suboptimal when combined with traditional eviction policies. We propose a distributed, adaptive algorithm that performs stochastic gradient ascent on a concave relaxation of the expected caching gain, and constructs a probabilistic content placement among a one-one/e factor from the optimal, in expectation. Motivated by our analysis, we have a tendency to also propose a novel greedy eviction policy to be used with path replication, and show through numerical evaluations that both algorithms considerably outperform path replication with traditional eviction policies over a broad array of network topologies.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Adaptive Pulse Wave Imaging Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo ABSTRACT: Imaging the mechanical characteristics of the artery wall may aid in the diagnosis of vascular
PROJECT TITLE : An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics ABSTRACT: One of the most important preprocessing steps for high-level tasks in the field of image analysis and computer vision is
PROJECT TITLE : Learned Image Downscaling for Upscaling Using Content Adaptive Resampler ABSTRACT: SR models based on deep convolutional neural networks have shown greater performance in recovering the underlying high-resolution
PROJECT TITLE : Multipatch Unbiased Distance Non-Local Adaptive Means With Wavelet Shrinkage ABSTRACT: Many existing non-local means (NLM) approaches either utilise Euclidean distance to quantify the similarity between patches,
PROJECT TITLE : Depth Restoration From RGB-D Data via Joint Adaptive Regularization and Thresholding on Manifolds ABSTRACT: By integrating the properties of local and non-local manifolds that offer low-dimensional parameterizations

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry