Name
Title | ||
|---|---|---|
Data-Guided Approach for Learning and Improving User Experience in Computer Networks. |
Abstract | ||
|---|---|---|
Machine learning algorithms have been traditionally used to understand user behavior or system performance. In computer networks, with a subset of input features as controllable network parameters, we envision developing a data-driven network resource allocation framework that can optimize user experience. In particular, we explore how to leverage a classier learned from training instances to optimally guide network resource allocation to improve the overall performance on test instances. Based on logistic regression, we propose an optimal resource allocation algorithm, as well as heuristics with low-complexity. We evaluate the performance of the proposed algorithms using a synthetic Gaussian dataset, a real world dataset on video streaming over throttled networks, and a tier-one cellular operator’s customer complaint traces. The evaluation demonstrates the eectiveness of the |
Year | Venue | Field |
|---|---|---|
2015 | ACML | Data mining,User experience design,Resource allocation algorithm,Computer science,Video streaming,Computer network,Gaussian,Resource allocation,Heuristics,Operator (computer programming),Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 4 | 0.39 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanan Bao | 1 | 53 | 4.40 |
| Xin Liu | 2 | 3919 | 320.56 |
| Amit Pande | 3 | 269 | 24.58 |