Statistical approach to NoC design

Chip multiprocessors (CMPs) combine increasingly many general-purpose processor cores on a single chip. These cores run several tasks with unpredictable communication needs, resulting in uncertain and often-changing traffic patterns. This unpredictability leads network-on-chip (NoC) designers to plan for the worst-case traffic patterns, and significantly over-provision link capacities. In this paper, we provide NoC designers with an alternative statistical approach. We first present the traffic-load distribution plots (T-Plots), illustrating how much capacity over-provisioning is needed to service 90%, 99%, or 100% of all traffic patterns. We prove that in the general case, plotting T-Plots is #P-complete, and therefore extremely complex. We then show how to determine the exact mean and variance of the traffic load on any edge, and use these to provide Gaussian-based models for the T-Plots, as well as guaranteed performance bounds. Finally, we use T-Plots to reduce the network power consumption by providing an efficient capacity allocation algorithm with predictable performance guarantees. © 2008 IEEE

Statistical approach to networks-on-chip

Chip multiprocessors (CMPs) combine increasingly many general-purpose processor cores on a single chip. These cores run several tasks with unpredictable communication needs, resulting in uncertain and often-changing traffic patterns. This unpredictability leads network-on-chip (NoC) designers to plan for the worst case traffic patterns, and significantly overprovision link capacities. In this paper, we provide NoC designers with an alternative statistical approach. We first present the traffic-load distribution plots (T-Plots), illustrating how much capacity overprovisioning is needed to service 90, 99, or 100 percent of all traffic patterns. We prove that in the general case, plotting T-Plots is #P-complete, and therefore extremely complex. We then show how to determine the exact mean and variance of the traffic load on any edge, and use these to provide Gaussian-based models for the T-Plots, as well as guaranteed performance bounds. We also explain how to practically approximate T-Plots using random-walk-based methods. Finally, we use T-Plots to reduce the network power consumption by providing an efficient capacity allocation algorithm with predictable performance guarantees. © 2006 IEEE

FIB efficiency in distributed platforms

© 2016 IEEE.The Internet routing ecosystem is facing substantial scalability challenges due to continuous, significant growth of the state represented in the data plane. Distributed switch architectures introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives. In this work we explore efficient FIB representations in common distributed switch architectures. Our approach introduces substantial savings in memory footprint transparently for existing hardware. Our results are supported by an extensive simulation study on real IPv4 and IPv6 FIBs

Accommodating stake effects under prospect theory

One of the stylized facts underlying prospect theory is a four-fold pattern of risk preferences. People have been shown to be risk seeking for small probability gains and large probability losses, while being risk averse for large probability gains and small probability losses. Another fourfold pattern of risk preferences over outcomes, postulated by Harry Markowitz in 1952, has received much less attention and is currently not integrated into prospect theory. In two experiments, we show that risk preferences may change over outcomes. While we find people to be risk seeking for small outcomes, this turns to risk neutrality and later risk aversion as stakes increase. We then show how a one-parameter logarithmic utility function fits such stake effects significantly better under prospect theory than the power or exponential functions mostly used when fitting prospect theory models. We further investigate the extent to which the use of ill-suited functional forms to represent utility may result in violations of prospect theory, and whether such violations disappear when using logarithmic utility

Empirical Distributions of F-ST from Large-Scale Human Polymorphism Data

Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright’s FST that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-FST may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically FST analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global FST distribution closely follows an exponential distribution. Third, although the overall FST distribution is similarly shaped (inverse J), FST distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-FST of these groups is linear in allele frequency. These results suggest that investigating the extremes of the FST distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection

FIB efficiency in distributed platforms

© 2016 IEEE.The Internet routing ecosystem is facing substantial scalability challenges due to continuous, significant growth of the state represented in the data plane. Distributed switch architectures introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives. In this work we explore efficient FIB representations in common distributed switch architectures. Our approach introduces substantial savings in memory footprint transparently for existing hardware. Our results are supported by an extensive simulation study on real IPv4 and IPv6 FIBs

FIB efficiency in distributed platforms

© 2016 IEEE.The Internet routing ecosystem is facing substantial scalability challenges due to continuous, significant growth of the state represented in the data plane. Distributed switch architectures introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives. In this work we explore efficient FIB representations in common distributed switch architectures. Our approach introduces substantial savings in memory footprint transparently for existing hardware. Our results are supported by an extensive simulation study on real IPv4 and IPv6 FIBs

Exploiting Order Independence for Scalable and Expressive Packet Classification

Efficient packet classification is a core concern for network services. Traditional multi-field classification approaches, in both software and ternary content-addressable memory (TCAMs), entail tradeoffs between (memory) space and (lookup) time. TCAMs cannot efficiently represent range rules, a common class of classification rules confining values of packet fields to given ranges. The exponential space growth of TCAM entries relative to the number of fields is exacerbated when multiple fields contain ranges. In this work, we present a novel approach which identifies properties of many classifiers which can be implemented in linear space and with worst-case guaranteed logarithmic time and allows the addition of more fields including range constraints without impacting space and time complexities. On real-life classifiers from Cisco Systems and additional classifiers from ClassBench [11] (with real parameters), 90-95 % of rules are thus handled, and the other 5-10 % of rules can be stored in TCAM to be processed in parallel

FIB efficiency in distributed platforms

© 2016 IEEE.The Internet routing ecosystem is facing substantial scalability challenges due to continuous, significant growth of the state represented in the data plane. Distributed switch architectures introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives. In this work we explore efficient FIB representations in common distributed switch architectures. Our approach introduces substantial savings in memory footprint transparently for existing hardware. Our results are supported by an extensive simulation study on real IPv4 and IPv6 FIBs