Anonymity and Fairness in Packet Scheduling: A Quantitative Tradeoff PROJECT TITLE :Anonymity and Fairness in Packet Scheduling: A Quantitative TradeoffABSTRACT:Fairness among multiple users sharing a standard resource is an important criterion in the planning and evaluation of scheduling algorithms in networks. Anonymous NetWorking, where sources of transmitted packets are undecipherable to an eavesdropper, requires packets arriving at routers from multiple sources to be randomly reordered previous to transmission, that works against the notion of temporal fairness in packet scheduling. Consequently, it's vital to understand the relationship between temporal fairness and achievable anonymity. In this paper, this relationship is investigated for 3 truthful scheduling paradigms: First-Return–First-Serve (FCFS), Truthful Queuing, and also the Proportional Methodology. Using an data-theoretic metric for anonymity and a common temporal fairness index that measures the degree of out-of-order transmissions, the anonymity achievable beneath these scheduling paradigms is characterised and their anonymity-fairness tradeoffs are compared. The FCFS and Honest Queuing algorithms have little inherent anonymity, and a vital improvement in anonymity is achieved by relaxing their respective fairness paradigms. The analysis of the relaxed FCFS criterion, in specific, is accomplished by modeling the matter as a stochastic Control System that is solved using dynamic programming. The proportional methodology of scheduling, whereas unpopular in networks these days, is shown to outperform the opposite fair scheduling algorithms when trading temporal fairness for anonymity, and is also proven to be asymptotically optimal because the buffer size of the scheduler is increased. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Hardware-Acceleration of Short-Read Alignment Based on the Burrows-Wheeler Transform TCAM-Based Multi-Match Packet Classification Using Multidimensional Rule Layering