Multi-engine packet classification hardware accelerator

As line rates increase, the task of designing high performance architectures with reduced power consumption for the processing of router traffic remains important. In this paper, we present a multi-engine packet classification hardware accelerator, which gives increased performance and reduced power consumption. It follows the basic idea of decision-tree based packet classification algorithms, such as HiCuts and HyperCuts, in which the hyperspace represented by the ruleset is recursively divided into smaller subspaces according to some heuristics. Each classification engine consists of a Trie Traverser which is responsible for finding the leaf node corresponding to the incoming packet, and a Leaf Node Searcher that reports the matching rule in the leaf node. The packet classification engine utilizes the possibility of ultra-wide memory word provided by FPGA block RAM to store the decision tree data structure, in an attempt to reduce the number of memory accesses needed for the classification. Since the clock rate of an individual engine cannot catch up to that of the internal memory, multiple classification engines are used to increase the throughput. The implementations in two different FPGAs show that this architecture can reach a searching speed of 169 million packets per second (mpps) with synthesized ACL, FW and IPC rulesets. Further analysis reveals that compared to state of the art TCAM solutions, a power savings of up to 72% and an increase in throughput of up to 27% can be achieved

Learning Gaussian Graphical Models with Observed or Latent FVSs

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles. Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes. We propose efficient structure learning algorithms for two cases: 1) All nodes are observed, which is useful in modeling social or flight networks where the FVS nodes often correspond to a small number of high-degree nodes, or hubs, while the rest of the networks is modeled by a tree. Regardless of the maximum degree, without knowing the full graph structure, we can exactly compute the maximum likelihood estimate in $O(kn^2+n^2\log n)$ if the FVS is known or in polynomial time if the FVS is unknown but has bounded size. 2) The FVS nodes are latent variables, where structure learning is equivalent to decomposing a inverse covariance matrix (exactly or approximately) into the sum of a tree-structured matrix and a low-rank matrix. By incorporating efficient inference into the learning steps, we can obtain a learning algorithm using alternating low-rank correction with complexity $O(kn^{2}+n^{2}\log n)$ per iteration. We also perform experiments using both synthetic data as well as real data of flight delays to demonstrate the modeling capacity with FVSs of various sizes

Ultra-high throughput string matching for deep packet inspection

Deep Packet Inspection (DPI) involves searching a packet's header and payload against thousands of rules to detect possible attacks. The increase in Internet usage and growing number of attacks which must be searched for has meant hardware acceleration has become essential in the prevention of DPI becoming a bottleneck to a network if used on an edge or core router. In this paper we present a new multi-pattern matching algorithm which can search for the fixed strings contained within these rules at a guaranteed rate of one character per cycle independent of the number of strings or their length. Our algorithm is based on the Aho-Corasick string matching algorithm with our modifications resulting in a memory reduction of over 98% on the strings tested from the Snort ruleset. This allows the search structures needed for matching thousands of strings to be small enough to fit in the on-chip memory of an FPGA. Combined with a simple architecture for hardware, this leads to high throughput and low power consumption. Our hardware implementation uses multiple string matching engines working in parallel to search through packets. It can achieve a throughput of over 40 Gbps (OC-768) when implemented on a Stratix 3 FPGA and over 10 Gbps (OC-192) when implemented on the lower power Cyclone 3 FPGA

Heavy Vehicle Performance During Recovery From Forced-Flow Urban Freeway Conditions Due To Incidents, Work Zones and Recurring Congestion

Information contained in the Highway Capacity Manual on the influence heavy vehicles have on freeway traffic operations has been based on few field data collection efforts and relied mostly on traffic simulation efforts. In the 2010 Manual heavy vehicle impact is evaluated based on “passenger car equivalent” values for buses, recreational vehicles and trucks. These values were calibrated for relatively uncongested freeway conditions (levels of service A through C) since inadequate field data on heavy vehicle behavior under congested conditions were available. A number of field data collection efforts, that were not included in deriving the passenger car equivalent values used in the Highway Capacity Manual, indicated that heavy vehicle impacts on traffic operations may increase as freeway congestion levels increase and freeways operate under unstable flow conditions. The goal of the present effort was to collect and analyze field data with an emphasis on heavy vehicle behavior under lower speeds and derive passenger car equivalent values under such conditions

Morphologies in a Cluster of Extremely Red Galaxies with Old Stellar Populations at z=1.34

We have identified a clustering of red galaxies from deep optical/IR images obtained as part of the Institute for Astronomy Deep Survey. Photometric spectral-energy distributions indicate that most of these galaxies comprise nearly pure old stellar populations at a redshift near 1.4, and Keck spectroscopy of the three brightest red galaxies confirm this interpretation and give redshifts ranging from 1.335 to 1.338. Four of the galaxies are close together on the sky and less than 30" from an R=13.5 star, and we have obtained deep adaptive-optics imaging of this group. Detailed analysis and modeling of the surface-brightness profiles of these galaxies shows that two are normal ellipticals, one is an S0, and one appears to be an essentially pure disk of old stars, with no significant bulge. All four are highly relaxed, symmetric systems. While the old, bulgeless disk galaxy represents a type that is rare at the present epoch, the other three galaxies have structural parameters that are essentially indistinguishable from those of present-day galaxies and differ only in the age of their stellar populations.Comment: Accepted by ApJ. 10 pages including 9 figure