Architecture design and performance analysis of practical buffered-crossbar packet switches

Combined input crosspoint buffered (CICB) packet switches were introduced to relax inputoutput arbitration timing and provide high throughput under admissible traffic. However, the amount of memory required in the crossbar of an N x N switch is N2x k x L, where k is the crosspoint buffer size and needs to be of size RTT in cells, L is the packet size. RTT is the round-trip time which is defined by the distance between line cards and switch fabric. When the switch size is large or RTT is not negligible, the memory amount required makes the implementation costly or infeasible for buffered crossbar switches. To reduce the required memory amount, a family of shared memory combined-input crosspoint-buffered (SMCB) packet switches, where the crosspoint buffers are shared among inputs, are introduced in this thesis. One of the proposed switches uses a memory speedup of in and dynamic memory allocation, and the other switch avoids speedup by arbitrating the access of inputs to the crosspoint buffers. These two switches reduce the required memory of the buffered crossbar by 50% or more and achieve equivalent throughput under independent and identical traffic with uniform distributions when using random selections. The proposed mSMCB switch is extended to support differentiated services and long RTT. To support P traffic classes with different priorities, CICB switches have been reported to use N2x k x L x P amount of memory to avoid blocking of high priority cells.The proposed SMCB switch with support for differentiated services requires 1/mP of the memory amount in the buffered crossbar and achieves similar throughput performance to that of a CICB switch with similar priority management, while using no speedup in the shared memory. The throughput performance of SMCB switch with crosspoint buffers shared by inputs (I-SMCB) is studied under multicast traffic. An output-based shared-memory crosspoint buffered (O-SMCB) packet switch is proposed where the crosspoint buffers are shared by two outputs and use no speedup. The proposed O-SMCB switch provides high performance under admissible uniform and nonuniform multicast traffic models while using 50% of the memory used in CICB switches. Furthermore, the O-SMCB switch provides higher throughput than the I-SMCB switch. As SMCB switches can efficiently support an RTT twice as long as that supported by CICB switches and as the performance of SMCB switches is bounded by a matching between inputs and crosspoint buffers, a new family of CICB switches with flexible access to crosspoint buffers are proposed to support longer RTTs than SMCB switches and to provide higher throughput under a wide variety of admissible traffic models. The CICB switches with flexible access allow an input to use any available crosspoint buffer at a given output. The proposed switches reduce the required crosspoint buffer size by a factor of N , keep the service of cells in sequence, and use no speedup. This new class of switches achieve higher throughput performance than CICB switches under a large variety of traffic models, while supporting long RTTs. Crosspoint buffered switches that are implemented in single chips have limited scalability. To support a large number of ports in crosspoint buffered switches, memory-memory-memory (MMM) Clos-network switches are an alternative. The MMM switches that use minimum memory amount at the central module is studied. Although, this switch can provide a moderate throughput, MMM switch may serve cells out of sequence. As keeping cells in sequence in an MMM switch may require buffers be distributed per flow, an MMM with extended memory in the switch modules is studied. To solve the out of sequence problem in MMM switches, a queuing architecture is proposed for an MMM switch. The service of cells in sequence is analyzed

Detecting and Locating Man-in-the-Middle Attacks in Fixed Wireless Networks

We propose a novel method to detect and locate a Man-in-the-Middle attack in a fixed wireless network by analyzing round-trip time and measured received signal strength from fixed access points. The proposed method was implemented as a client-side application that establishes a baseline for measured round trip time (RTTs) and received signal strength (RSS) under no-threat scenarios and applies statistical measures on the measured RTT and RSS to detect and locate Man-in-the-Middle attacks.We show empirically that the presence of a Man-in-the-Middle attack incurs a significantly longer delay and larger standard deviation in measured RTT compared to that measured without a Man-in-the-Middle attack.We evaluated three machine learning algorithms on the measured RSS dataset to estimate the location of a Man-in-the-Middle attacker.Experimental results show that the proposed method can effectively detect and locate a Man-in-the-Middle attack and achieves a mean location estimation error of 0.8 meters in an indoor densely populated metropolitanenvironment.</p

How we learn social norms: a three-stage model for social norm learning

As social animals, humans are unique to make the world function well by developing, maintaining, and enforcing social norms. As a prerequisite among these norm-related processes, learning social norms can act as a basis that helps us quickly coordinate with others, which is beneficial to social inclusion when people enter into a new environment or experience certain sociocultural changes. Given the positive effects of learning social norms on social order and sociocultural adaptability in daily life, there is an urgent need to understand the underlying mechanisms of social norm learning. In this article, we review a set of works regarding social norms and highlight the specificity of social norm learning. We then propose an integrated model of social norm learning containing three stages, i.e., pre-learning, reinforcement learning, and internalization, map a potential brain network in processing social norm learning, and further discuss the potential influencing factors that modulate social norm learning. Finally, we outline a couple of future directions along this line, including theoretical (i.e., societal and individual differences in social norm learning), methodological (i.e., longitudinal research, experimental methods, neuroimaging studies), and practical issues