Earliest-deadline-first service in heavy-traffic acyclic networks

This paper presents a heavy traffic analysis of the behavior of multi-class acyclic queueing networks in which the customers have deadlines. We assume the queueing system consists of J stations, and there are K different customer classes. Customers from each class arrive to the network according to independent renewal processes. The customers from each class are assigned a random deadline drawn from a deadline distribution associated with that class and they move from station to station according to a fixed acyclic route. The customers at a given node are processed according to the earliest-deadline-first (EDF) queue discipline. At any time, the customers of each type at each node have a lead time, the time until their deadline lapses. We model these lead times as a random counting measure on the real line. Under heavy traffic conditions and suitable scaling, it is proved that the measure-valued lead-time process converges to a deterministic function of the workload process

The Unintended Consequences of ICT in Sub-Saharan Africa

Akin is, like many things in cyberspace, an alias. In real life he\u27s 14. He wears Adidas sneakers, a Rolex Submariner watch, and a kilo of gold around his neck. Akin, who lives in Lagos, is one of a new generation of entrepreneurs that has emerged in this city of 15 million, Nigeria\u27s largest. His mother makes $30 a month as a cleaner, his father about the same hustling at bus stations. But Akin has made it big working long days at Internet cafes and is now the main provider for his family and legions of relatives. Call him a “Yahoo Yahoo Millionaire. Akin buys things online - laptops, BlackBerries, cameras, flat-screen TVs - using stolen credit cards and aliases. He has the loot shipped via FedEx or DHL to safe houses in Europe, where it is received by friends, then shipped on to Lagos to be sold on the black market. (He figures Americans are too smart to sell a camera on eBay to a buyer with an address in Nigeria.). Akin\u27s main office is an Internet cafe in the Ikeja section of Lagos. He spends up to ten hours a day there, seven days a week, huddled over one of 50 computers, working his scams. And he\u27s not alone: The cafe is crowded most of the time with other teenagers, like Akin, working for a chairman who buys the computer time and hires them to extract e-mail addresses and credit card information from the thin air of cyberspace. Akin\u27s chairman, who is computer illiterate, gets a 60 percent cut and reserves another 20 percent to pay off law enforcement officials who come around or teachers who complain when the boys cut school. That still puts plenty of cash in Akin\u27s pocket. (Quoted from http://www.nairaland.com/nigeria/topic- 37929.0.html)

Real-Time Monitoring of Video Quality in IP Networks

This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as the video codec and loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on many parameters that are difficult to estimate in real-time. As a result, we introduce a relative quality metric that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments