D. Marinca

In Content Delivery Networks (CDNs), knowing the popularity of video content helps the manager to take efficient decisions about which video content should be cached near the end users and also about the duplication degree of each video. Expand

The deadline assignment problem arises in distributed systems where each subtask composing a distributed task must receive a local deadline such that the task end-to-end deadline is met. Expand

We present the ECOFRAME metro ring network and how we can provide virtual circuit emulation with QoS guarantee at a subwavelegth level. Expand

We introduce a decision maker, called a forecaster, that predicts the popularity based on the predictions of several experts. Expand

We use machine learning methods to predict the short term evolution of link quality, in order to switch the data transmission on a better quality link. Expand

In the industrial environments of the future, robots, sensors, and other industrial devices will have to communicate autonomously and in a robust and efficient manner with each other, relying on a large extent on wireless communication links, which will expand and supplement the existing wired/Ethernet connections. Expand

We present a self-learning and prediction method able to predict the number of solicitation individually for each video content in a CDN. Expand

We use real traces extracted from YouTube to compare different prediction methods and determine the best parameter tuning for experts and forecasters to predict the daily number of requests. Expand

We propose a novel content caching scheme referred as “Cache Management using Temporal Pattern based Solicitation” (CMTPS), to further minimize both service delays and load in the network for Video on Demand (VoD) applications. Expand