1402660
On Unstructured File Sharing Networks
We study the interaction among users of unstructured file sharing applications, who compete for available network resources (link bandwidth or capacity) by opening multiple connections on multiple paths so as to accelerate data transfer. We model this interaction with an unstructured file sharing game. Users are players and their strategies are the numbers of sessions on available paths. We consider a general bandwidth sharing framework proposed by Kelly [1] and Mo and Walrand [2], with TCP as a special case. Furthermore, we incorporate the Tit-for-Tat strategy (adopted by BitTorrent [3] networks) into the unstructured file sharing game to model the competition in which a connection can be set up only when both users find this connection beneficial. We refer to this as an overlay formation game. We prove the existence of Nash equilibrium in several variants of both games, and quantify the losses of efficiency of Nash equilibria. We find that the loss of efficiency due to selfish behavior is still unbounded even when the Tit-for-Tat strategy is believed to prevent selfish behavior.
11427190
Exploiting the IPID Field to Infer Network Path and End-System Characteristics
In both active and passive network Internet measurements, the IP packet has a number of important header fields that have played key roles in past measurement efforts, e.g., IP source/destination address, protocol, TTL, port, and sequence number/acknowledgment. The 16-bit identification field (IPID) has only recently been studied to determine what information it might yield for network measurement and performance characterization purposes. We explore several new uses of the IPID field, including how it can be used to infer: (a) the amount of internal (local) traffic generated by a server; (b) the number of servers in a large-scale, load-balanced server complex and; (c) the difference between one-way delays of two machines to a target computer. We illustrate and validate the use of these techniques through empirical measurement studies.
5847118
A self-tuning structure for adaptation in TCP/AQM networks
Congestion control in TCP/AQM networks is expected to perform well for a wide-range of conditions, but recent advances in modeling and analysis indicate that present AQM schemes need an extra dose of adaptability to cope. This paper answers the call and proposes a self-tuning structure wherein AQM parameters are automatically tuned in response to on-line estimation of link capacity and traffic load. This approach is applicable to any AQM scheme that is parameterizable in terms of link capacity and TCP load. In this paper, we will describe this self-tuning structure, illustrate its application to PI and RED AQMs, provide stability analysis, and conduct <tt>ns</tt> simulations to compare with both fixed AQM schemes and the recently proposed adaptive RED.
1774011
TCP connection game: a study on the selfish behavior of TCP users
We present a game-theoretic study of the selfish behavior of TCP users when they are allowed to use multiple concurrent TCP connections so as to maximize their goodputs or other utility functions. We refer to this as the TCP connection game. A central question we ask is whether there is a Nash equilibrium in such a game, and if it exists, whether the network operates efficiently at such a Nash equilibrium. Combined with the well known PFTK TCP model (1998), we study this question for three utility functions that differ in how they capture user behavior. The bad news is that the loss of efficiency or price of anarchy can be arbitrarily large if users have no resource limitations and are not socially responsible. The good news is that, if either of these two factors is considered, efficiency loss is bounded. This may partly explain why there will be no congestion collapse if many users use multiple connections.
7846317
Throughput differentiation using coloring at the network edge and preferential marking at the core
In this paper we introduce an innovation in differentiated services architecture consisting of adaptive two-level coloring at the edge and preferential marking at the core. We identify general properties of these two processes which, when met, guarantee a desirable fixed point for the network; i.e., one where aggregated flow rates meet or exceed given targets in an over-provisioned network. Specific mechanisms realizing the aforementioned properties lead to so-called active rate management controllers for edge coloring, and a preferentially-marking, active queue management controller at the core. We discuss stability of the fixed point for this network, and validate results using ns simulations.
19398479
An application of the AHP in 3PL vendor selection of a 4PL system
As a fourth party logistics provider integrates the capacities of third party logistics providers, 3PL vendor selection is an important problem. Furthermore, 3PL vendor selection is a complex multi-criteria decision problem. The decision made by the decision maker or an evaluation team can be improved by a systematic and logistical approach to access priorities based on the inputs of the decision maker or the team made by several people. The analytic hierarchy process (AHP) can be very useful in reaching a likely result which can satisfy the decision maker or the team. In this paper, an AHP-based model is formulated and applied to a case study to examining the function in 3PL vendor selection. The use of the proposed model indicates that the computed quantitative evaluations can be applied to improve the precision of the selection.
24698432
An AHP/DEA Methodology for 3PL Vendor Selection in 4PL
Vendor selection is an important and complex problem, which contains many criteria. The analytic hierarchy process (AHP) can be very useful in reaching a likely result which can satisfy the subjective opinion of the decision maker or the evaluation team. On the other hand, the Data Envelopment Analysis (DEA) can select vendors objectively with the quantitative data. In this paper, a four-step model based on both AHP and DEA is formulated and applied to a case study. The use of the proposed model can give precise evaluation combining the subjective opinion from the decision makers with the objective data of the relevant factors. In other words, this approach can add subjective factors to the evaluation without losing the objective precision of the selection.
760636
Accelerated occlusion culling using shadow frusta
Many applications in computer graphics and virtual environments need to render datasets with large numbers of primitives and high depth complexity at interactive rates. However, standard techniques like view frustum culling and a hardware z-bu er are unable to display datasets composed of hundred of thousands of polygons at interactive frame rates on current high-end graphics systems. We add a \conservative"visibility culling stage to the rendering pipeline, attempting to identify and avoid processing of occluded polygons. Given a moving viewpoint, the algorithm dynamically chooses a set of occluders. Each occluder is used to compute a shadow frustum, and all primitives contained within this frustumare culled. The algorithmhierarchicallytraverses the model, culling out parts not visible from the current viewpoint using e cient, robust, and in some cases specialized interference detection algorithms. The algorithm's performance varies with the location of the viewpoint and the depth complexity of the model. In the worst case it is linear in the input size with a small constant. In this paper, we demonstrate its performance on a city model composed of 500;000 polygons and possessing varying depth complexity. We are able to cull an average of 55% of the polygons that would not be culled by view-frustum culling and obtain a commensurate improvement in frame rate. The overall approach is e ective and scalable, is applicable to all polygonal models, and can be easily implemented on top of view-frustum culling.
16669804
An effective region-based image retrieval framework
We present a region-based image retrieval framework that integrates efficient region-based representation in terms of storage and retrieval and effective on-line learning capability. The framework consists of methods for image segmentation and grouping, indexing using modified inverted file, relevance feedback, and continuous learning. By exploiting a vector quantization method, a compact region-based image representation is achieved. Based on this representation, an indexing scheme similar to the inverted file technology is proposed. In addition, it supports relevance feedback based on the vector model with a weighting scheme. A continuous learning strategy is also proposed to enable the system to self improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.
13998980
Image annotation refinement using random walk with restarts
Image annotation plays an important role in image retrieval and management. However, the results of the state-of-the-art image annotation methods are often unsatisfactory. Therefore, it is necessary to refine the imprecise annotations obtained by existing annotation methods. In this paper, a novel approach to automatically refine the original annotations of images is proposed. On the one hand, for Web images, textual information, e.g. file name and surrounding text, is used to retrieve a set of candidate annotations. On the other hand, for non-Web images that are lack of textual information, a relevance model-based algorithm using visual information is used to decide the candidate annotations. Then, candidate annotations are re-ranked and only the top ones are reserved as the final annotations. To re-rank the annotations, an algorithm using Random Walk with Restarts (RWR) is proposed to leverage both the corpus information and the original confidence information of the annotations. Experimental results on both non-Web images of Corel dataset and Web images of photo forum sites demonstrate the effectiveness of the proposed method.