Analysis of exhaustive limited service for token ring networks

Token ring operation is well-understood in the cases of exhaustive, gated, gated limited, and ordinary cyclic service. There is no current data, however, on queueing models for the exhaustive limited service type. This service type differs from the others in that there is a preset maximum (omega) on the number of packets which may be transmitted per token reception, and packets which arrive after token reception may still be transmitted if the preset packet limit has not been reached. Exhaustive limited service is important since it closely approximates a timed token service discipline (the approximation becomes exact if packet lengths are constant). A method for deriving the z-transforms of the distributions of the number of packets present at both token departure and token arrival for a system using exhaustive limited service is presented. This allows for the derivation of a formula for mean queueing delay and queue lengths. The method is theoretically applicable to any omega. Fortunately, as the value of omega becomes large (typically values on the order of omega = 8 are considered large), the exhaustive limited service discipline closely approximates an exhaustive service discipline

Hybrid token-CDMA MAC protocol for wireless networks.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2009.Ad hoc networks are commonly known to implement IEEE 802.11 standard as their medium access control (MAC) protocol. It is well known that token passing MAC schemes outperform carrier-sense-multiple-access (CSMA) schemes, therefore, token passing MAC protocols have gained popularity in recent years. In recent years, the research extends the concept of token passing ' scheme to wireless settings since they have the potential of achieving higher channel utilization than CSMA type schemes. In this thesis, a hybrid Token-CDMA MAC protocol that is based on a token passing scheme with the incorporation of code division multiple access (CDMA) is introduced. Using a dynamic code distribution algorithm and a modified leaky-bucket policing system, the hybrid protocol is able to provide both Quality of Service (QoS) and high network resource utilization, while ensuring the stability of a network. This thesis begins with the introduction of a new MAC protocol based on a token-passing strategy. The input traffic model used in the simulation is a two-state Markov Modulated Poisson Process (MMPP). The data rate QoS is enforced by implementing a modified leaky bucket mechanism in the proposed MAC scheme. The simulation also takes into account channel link errors caused by the wireless link by implementing a multi-layered Gilbert-Elliot model. The performance of the proposed MAC scheme is examined by simulation, and compared to the performance of other MAC protocols published in the literature. Simulation results demonstrate that the proposed hybrid MAC scheme is effective in decreasing packet delay and significantly shortens the length of the queue. The thesis continues with the discussion of the analytical model for the hybrid Token CDMA protocol. The proposed MAC scheme is analytically modelled as a multiserver multiqueue (MSMQ) system with a gated service discipline. The analytical model is categorized into three sections viz. the vacation model, the input model and the buffer model. The throughput and delay performance are then computed and shown to closely match the simulation results. Lastly, cross-layer optimization between the physical (PHY) and MAC layers for the hybrid token-CDMA scheme is discussed. The proposed joint PHY -MAC approach is based on the interaction between the two layers in order to enable the stations to dynamically adjust the transmission parameters resulting in reduced mutual interference and optimum system performance

Journal of Telecommunications and Information Technology, 2004, nr 2

kwartalni

Scalable Extraction of Training Data from (Production) Language Models

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization

Excellence at Work: Policy Option Papers for the National Governors\u27 Association

State-level initiatives are proposed that address key issues affecting the competitiveness of the U.S. economy.https://research.upjohn.org/up_press/1201/thumbnail.jp