Online FPS games: effect of router buffer and multiplexing techniques on subjective quality estimators

First Person Shooters are a genre of online games in which users demand a high interactivity, because the actions and the movements are very fast. They usually generate high rates of small packets which have to be delivered to the server within a deadline. When the traffic of a number of players shares the same link, these flows can be aggregated in order to save bandwidth. Certain multiplexing techniques are able to merge a number of packets, in a similar way to voice trunking, creating a bundle which is transmitted using a tunnel. In addition, the headers of the original packets can be compressed by means of standard algorithms. The characteristics of the buffers of the routers which deliver these bundled packets may have a strong influence on the network impairments (mainly delay, jitter and packet loss) which determine the quality of the game. A subjective quality estimator has been used in order to study the mutual influence of the buffer and multiplexing techniques. Taking into account that there exist buffers which size is measured in terms of bytes, and others measured in packets, both kinds of buffers have been tested, using different sizes. Traces from real game parties have been merged in order to obtain the traffic of 20 simultaneous players sharing the same Internet access. The delay and jitter produced by the buffer of the access router have been obtained using simulations. In general, the quality is expected to be reduced as the background traffic grows, but the results show an anomalous region in which the quality rises with the background traffic amount. Small buffers present better subjective quality results than bigger ones. When the total traffic amount gets above the available bandwidth, the buffers measured in bytes add to the packets a fixed delay, which grows with buffer size. They present a jitter peak when the offered traffic is roughly the link capacity. On the other hand, buffers which size is measured in packets add a smaller delay, but they increase packet loss for gaming traffic. The obtained results illustrate the need of knowing the characteristics of the buffer in order to make the correct decision about traffic multiplexing. As a conclusion, it would be interesting for game developers to identify the behaviour of the router buffer so as to adapt the traffic to it.     

Gewinnung von Brennstoff für Kraftwerke aus Klärschlamm

Production of a combustible for coal-fired power stations based on sewage sludge . This article describes various means of achieving more extensive mechanical dewatering of sewage sludges. The heat content of the sludge solids can become available for combustion processes. The sludge filter cakes obtained by the “CarboSed” process can be disposed of with minimum energy consumption by fluidized bed combustion or be used after appropriate work-up as a combustible in coal-fired power stations with full exploitation of their heat content for generation of steam and electric current. Partial substitution of primary energy sources such as coal and fuel oil by sludge filter cake is feasible in all cases. The processes thus make a contribution to the saving of energy resources and the disposal of sludge.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.     

Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

A class of high performance Maekawa-type algorithms for distributed systems under heavy demand

Summary Distributed Mutual Exclusion algorithms have been mainly compared using the number of messages exchanged per critical section execution. In such algorithms, no attention has been paid to the serialization order of the requests. Indeed, they adopt FCFS discipline. Conversely, the insertion of priority serialization disciplines, such as Short-Job-First, Head-Of-Line, Shortest-Remaining-Job-First etc., can be useful in many applications to optimize some performance indices. However, such priority disciplines are prone to starvation. The goal of this paper is to investigate and evaluate the impact of the insertion of a priority discipline in Maekawa-type algorithms. Priority serialization disciplines will be inserted by means of agated batch mechanism which avoids starvation. In a distributed algorithm, such a mechanism needs synchronizations among the processes. In order to highlight the usefulness of the priority based serialization discipline, we show how it can be used to improve theaverage response time compared to the FCFS discipline. The gated batch approach exhibits other advantages: algorithms are inherently deadlock-free and messages do not need to piggyback timestamps. We also show that, under heavy demand, algorithms using gated batch exchange less messages than Maekawa-type algorithms per critical section excution. Roberto Baldoni was born in Rome on February 1, 1965. He received the Laurea degree in electronic engineering in 1990 from the University of Rome La Sapienza and the Ph.D. degree in Computer Science from the University of Rome La Sapienza in 1994. Currently, he is a researcher in computer science at IRISA, Rennes (France). His research interests include operating systems, distributed algorithms, network protocols and real-time multimedia applications. Bruno Ciciani received the Laurea degree in electronic engineering in 1980 from the University of Rome La Sapienza. From 1983 to 1991 he has been a researcher at the University of Rome Tor Vergata. He is currently full professor in Computer Science at the University of Rome La Sapienza. His research activities include distributed computer systems, fault-tolerant computing, languages for parallel processing, and computer system performance and reliability evaluation. He has published in IEEE Trans. on Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Software Engineering and IEEE Trans. on Reliability. He is the author of a book titled Manufactoring Yield Evaluation of VLSI/WSI Systems to be published by IEEE Computer Society Press.This research was supported in part by the Consiglio Nazionale delle Ricerche under grant 93.02294.CT12This author is also supported by a grant of the Human Capital and Mobility project of the European Community under contract No. 3702 CABERNET     

Level Lines of a Polynomial on a Plane

Programming and Computer Software - A method for calculating the location of all types of level lines of a real polynomial on the real plane is proposed. To this end, the critical points and...     

A Novel Particle/Photon Detector Based on a Superconducting Proximity Array of Nanodots

The current frontiers in the investigation of high-energy particles demand for new detection methods. Higher sensitivity to low-energy deposition, high-energy resolution to identify events and improve the background rejection, and large detector masses have to be developed to detect even an individual particle that weakly interacts with ordinary matter. Here, we will describe the concept and the layout of a novel superconducting proximity array which show dynamic vortex Mott insulator to metal transitions, as an ultra-sensitive compact radiation-particle detector.