Optimal Scheduling in a Queue with Differentiated Impatient Users

We consider a M/M/1 queue in which the average reward for servicing a job is an exponentially decaying function of the job’s sojourn time. The maximum reward and mean service times of a job are i.i.d. and chosen from arbitrary distributions. The scheduler is assumed to know the maximum reward, service rate, and age of each job. We prove that the scheduling policy that maximizes average reward serves the customer with the highest product of potential reward and service rate

Exploring the Baccalaureate Origin of Domestic Ph.D. Students in Computing Fields

Increasing the number of US students entering graduate school and receiving a Ph.D. in computer science is a goal as well as a challenge for many US Ph.D. granting institutions. Although the total computer science Ph.D. production in the U.S. has doubled between 2000 and 2010 (Figure 1), the fraction of domestic students receiving a Ph.D. from U.S. graduate programs has been below 50% since 2003 (Figure 2). The goal of the Pipeline Project of CRA-E (PiPE) is to better understand the pipeline of US citizens and Permanent Residents (henceforth termed domestic students ) who apply, matriculate, and graduate from doctoral programs in computer science. This article is the first of two articles from CRA-E examining this issue. This article provides an initial examination of the baccalaureate origins of domestic students who have matriculated to Ph.D. programs in computer science. We hope that trends and patterns in these data can be useful both in recruiting and, ultimately, in improving the quality and quantity of the domestic Ph.D. pipeline

Predicting User-Perceived Quality Ratings from Streaming Media Data

Abstract—Media stream quality is highly dependent on under-lying network conditions, but identifying scalable, unambiguous metrics to discern the user-perceived quality of a media stream in the face of network congestion is a challenging problem. User-perceived quality can be approximated through the use of carefully chosen application layer metrics, precluding the need to poll users directly. We discuss the use of data mining prediction techniques to analyze application layer metrics to determine user-perceived quality ratings on media streams. We show that several such prediction techniques are able to assign correct (within a small tolerance) quality ratings to streams with a high degree of accuracy. The time it takes to train and tune the predictors and perform the actual prediction are short enough to make such a strategy feasible to be executed in real time and on real computer networks. I

Revisiting a QoE Assessment Architecture Six Years Later: Lessons Learned and Remaining Challenges

Abstract. In 2003, we presented an architecture for a streaming video quality assessment syste

An optimal service ordering for a world wide web server

We consider alternative service policies in a web server with impatient users. User-perceived performance is modeled as an exponentially decaying function of the user's waiting time, reflecting the probability that the user aborts the download before the page is completely received. The web server is modeled as a single server queue, with Poisson arrivals and exponentially distributed file lengths. The server objective is to maximize average revenue per unit time, where each user is assumed to pay a reward proportional to the perceived performance. When file lengths are i.i.d., we prove that the optimal service policy is greedy, namely that the server should choose the job with the highest potential reward. However, when file lengths are independently drawn from a set of exponential distributions, we show the optimal policy need not be greedy; in fact, processor sharing policies sometimes outperform the best greedy policy in this case

Amy Csizmar Dalal, Ed Perry

Conducting quality assessment for streaming media services, particularly from the end user perspective, has not been widely addressed by the network research community and remains a hard problem. In this paper we discuss the general problem of assessing the quality of streaming media in a large-scale IP network. This work presents two main contributions. First, we specify a new measurement and assessment architecture that can flexibly support the needs of different classes of assessment consumers while supporting both new and existing measurements that can be correlated with user perceptions of media stream quality. Second, we demonstrate that a prototype implementation of this architecture can be used to assess a user's perceived quality of a media stream, by judicious choice and assessment of objective metrics. We conclude by discussing how this architecture can be used to predict future periods of stream quality degradation