Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models

Simulating a large-scale network like the Internet is a challenging undertaking because of the sheer volume of its traffic. Packet-oriented representation provides high-fidelity details but is computationally expensive; fluid-oriented representation offers high simulation efficiency at the price of losing packet-level details. Multi-resolution modeling techniques exploit the advantages of both representations by integrating them in the same simulation framework. This dissertation presents solutions to the problems regarding the efficiency, accuracy, and scalability of the traffic simulation models in this framework. The ``ripple effect\u27\u27 is a well-known problem inherent in event-driven fluid-oriented traffic simulation, causing explosion of fluid rate changes. Integrating multi-resolution traffic representations requires estimating arrival rates of packet-oriented traffic, calculating the queueing delay upon a packet arrival, and computing packet loss rate under buffer overflow. Real time simulation of a large or ultra-large network demands efficient background traffic simulation. The dissertation includes a rate smoothing technique that provably mitigates the ``ripple effect\u27\u27, an accurate and efficient approach that integrates traffic models at multiple abstraction levels, a sequential algorithm that achieves real time simulation of the coarse-grained traffic in a network with 3 tier-1 ISP (Internet Service Provider) backbones using an ordinary PC, and a highly scalable parallel algorithm that simulates network traffic at coarse time scales

Discrete-Event Fluid Modeling of Background TCP Traffic

TCP is the most widely used transport layer protocol used in the internet today. A TCP session adapts the demands it places on the network to observations of bandwidth availability on the network. Because TCP is adaptive, any model of its behavior that aspires to be accurate must be influenced by other network traffic. This point is especially important in the context of using simulation to evaluate some new network algorithm of interest (e.g. reliable multi-cast) in an environment where the background traffic affects---and is affected by---its behavior. We need to generate background traffic efficiently in a way that captures the salient features of TCP, while the reference and background traffic representations interact with each other. This paper describes a fluid model of TCP and a switching model that has flows represented by fluids interacting with packet-oriented flows. We describe conditions under which a fluid model produces exactly the same behavior as a packet-oriented model, and we quantify the performance advantages of the approach both analytically and empirically. We observe that very significant speedups may be attained while keeping high accuracy

A Correlation Attack Against User Mobility Privacy in a Large-scale WLAN network

User association logs collected from real-world wireless LANs have facilitated wireless network research greatly. To protect user privacy, the common practice in sanitizing these data before releasing them to the public is to anonymize users\u27 sensitive information such as the MAC addresses of their devices and their exact association locations. In this work,we demonstrate that these sanitization measures are insufficient in protecting user privacy from a novel type of correlation attack that is based on CRF (Conditional Random Field). In such a correlation attack, the adversary observes the victim\u27s AP (Access Point) association activities for a short period of time and then infers her corresponding identity in a released user association dataset. Using a user association log that contains more than three thousand users and millions of AP association records, we demonstrate that the CRF-based technique is able to pinpoint the victim\u27s identity exactly with a probability as high as 70%

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided many-to-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration

Frequency and Influencing Factors of Rubber Dam Usage in Tianjin: A Questionnaire Survey

Objective. To investigate the frequency and influencing factors of rubber dam usage for endodontic procedures among general dentistry practitioners and specialized practitioners (endodontist) in Tianjin. Methods. Three hundred questionnaires were distributed among practitioners from 3 different types of medical institutions in Tianjin. Data were collected and analysed using Chi-square tests. Results. There were 63.3% of respondents who have used rubber dam (response rate 82.7%, valid response rate 76.3%). However, only 0.4% and 3.1% of them recognized using rubber dam "every time" during caries direct restoration and root canal therapy, respectively. There was no significant difference in rubber dam usage between male and female practitioners. Among the respondents, practitioners with working experience between 5 and 10 years showed the highest usage rate (76.3%), while practitioners working more than 20 years showed the lowest (53.2%). The endodontists gained the highest and the most frequent usage rate and the best rubber dam technique mastering skills. Practitioners working in those stomatological departments of general hospitals showed the lowest rubber dam usage rate. Conclusions. The prevalence of rubber dam usage in Tianjin city is still low. The practitioner's gender, years of professional experience, general or specialized field, and the type of dental setting they work for are the factors that need to be considered during making policy and executing training

ZeRO++: Extremely Efficient Collective Communication for Giant Model Training

Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume reduction techniques, which we collectively refer to as ZeRO++, targeting each of the communication collectives in ZeRO. First is block-quantization based all-gather. Second is data remapping that trades-off communication for more memory. Third is a novel all-to-all based quantized gradient averaging paradigm as replacement of reduce-scatter collective, which preserves accuracy despite communicating low precision data. Collectively, ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better throughput at 384 GPU scale.Comment: 12 page

Time-dependent density-functional theory for open systems

By introducing the self-energy density functionals for the dissipative interactions between the reduced system and its environment, we develop a time-dependent density-functional theory formalism based on an equation of motion for the Kohn-Sham reduced single-electron density matrix of the reduced system. Two approximate schemes are proposed for the self-energy density functionals, the complete second order approximation and the wide-band limit approximation. A numerical method based on the wide-band limit approximation is subsequently developed and implemented to simulate the steady and transient current through various realistic molecular devices. Simulation results are presented and discussed.Comment: 16 pages, 12 figure