A Distributed Routing Method for Dynamic Multicasting

Single point, sender based control does not scale well for multicast delivery. For applications, such as group video or teleconferencing a low total cost multicast tree is required. In this article we present a destination driven algorithm to minimize the total tree cost of multicast tree in a dynamic situation for the whole session duration. In this heuristic approach we considered the staying duration of participants are available at the time of joining. The performance of our algorithm is analyzed through extensive simulation and evaluated against several other existing dynamic multicast routing and also against one well known near optimum heuristic algorithm used for solving Steiner tree problem. We have further tested our algorithm using erroneous information given by the joining participants. Simulation results show that its performance does not degrade that much even when the range of error is considerably high, which proves the robustness of our algorithm.     

为．NET应用程序实现一个类似Word的对象模型

所有的Microsoft Office应用程序都构建在支持自动化的对象模型之上。任何开发人员都能够使用对象模型来驱动应用程序UI以及添加、编辑和删除内容，就像一个真正的用户在与应用程序交互。丰富对象模型结合自动化支持使Office应用程序真正成为可扩展和可插接的。为了扩展Microsoft Word的行为而满足每个人自身的需要，任何人都能够在很短的时间里编写出一个强大的外接程序。作为优秀的面向对象开发人员，我们使用丰富的结构和优秀合理的对象模型（遵循模型一视图一控制器设计模式（MVC）来开发自己的应用程序。     

An engineering approach to dynamic prediction of network performance from application logs

Network measurement traces contain information regarding network behavior over the period of observation. Research carried out from different contexts shows predictions of network behavior can be made depending on network past history. Existing works on network performance prediction use a complicated stochastic modeling approach that extrapolates past data to yield a rough estimate of long‐term future network performance. However, prediction of network performance in the immediate future is still an unresolved problem. In this paper, we address network performance prediction as an engineering problem. The main contribution of this paper is to predict network performance dynamically for the immediate future. Our proposal also considers the practical implication of prediction. Therefore, instead of following the conventional approach to predict one single value, we predict a range within which network performance may lie. This range is bounded by our two newly proposed indices, namely, Optimistic Network Performance Index (ONPI) and Robust Network Performance Index (RNPI). Experiments carried out using one‐year‐long traffic traces between several pairs of real‐life networks validate the usefulness of our model. Copyright © 2005 John Wiley & Sons, Ltd.     

TCP-Cherry for satellite IP networks: Analytical model and performance evaluation

TCP-Cherry is a novel TCP congestion control scheme that we devised for ensuring high performance over satellite IP networks and the alikes which are characterized by long propagation delays and high link errors. In TCP-Cherry, two new algorithms, Fast-Forward Start and First-Aid Recovery, have been proposed for congestion control. Our algorithms use supplement segments, i.e., low-priority segments to probe the available bandwidth in the network for the TCP connections along with carrying new data blocks. In this paper, we present our new congestion control scheme, TCP-Cherry and devise an analytical model for it. Our major contributions in this paper include the analytical model and equations for performance evaluation, validation of the analytical model through comparison between analytical and simulation results and devising a guideline to tune the buffer related parameters both at the sender as well as the receiver ends for optimum throughput performance. Experiments show that simulation results and the calculated throughput from our analytical model match quite closely, thereby verifying the appropriateness of the model. In addition, from analysis of simulation results, we discover that a buffer size at the receiver, rwnd, that is around four times maxcwnd, or the maximum congestion window at the sender side, is likely to maintain high throughput over a wide range of operating conditions.     

A new approach to ensure fairness over the Internet

Congestion signaling is an essential issue for ensuring a high network performance. Explicit congestion notification (ECN) has been proved to provide a faster indication of congestion and thus improve the QoS of a TCP/IP network. In this paper, we present a new scheme for ECN named fair in‐time marking (FIM), which employs a mechanism that as well as providing an indication of incipient congestion, assures a fair service to all the connections. To facilitate such services using FIM, we propose inclusion of some simple yet effective functionalities in the routers. This is important as the client base of the Internet is increasing rapidly and the demand for a reasonably fair service is getting stronger. Experiments show that FIM offers the best fairness, sometimes better by around 20% or more without affecting efficiency, compared with other proposed schemes for ECN. Copyright © 2004 John Wiley & Sons, Ltd.     

Ensuring fairness among ECN and non‐ECN TCP over the Internet

Explicit Congestion Notification (ECN) has been proved to provide a fast indication of incipient congestion and thus better the performance of a TCP/IP network. In this work, we carry out investigations on gateway or router performance in providing fairnesss when both FIM ECN‐capable and non‐ECN‐capable connections are employed. We propose a new packet‐dropping scheme called Fair In‐time Dropping (FID) which drops packets from a connection upon detecting an incipient indication of congestion depending on its share of gateway or router buffer occupancy. We also show that a combination of FIM and FID offers the best fairness compared with a combination of FIM along with other dropping schemes. Copyright © 2003 John Wiley & Sons, Ltd.     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.