Lookup Scheme Research Articles

Upgraded LauePt4 for rapid recognition and fitting of Laue patterns from crystals with unknown orientations.

The LauePt program is a popular and easy-to-use crystallography tool for indexing and simulating X-ray Laue patterns, but its previous versions lack search functions for recognizing Laue patterns taken from crystals with unknown orientations. To overcome this obstacle, a major upgrade of the program, called LauePt4, is presented with three robust search schemes implemented: (i) crystal rotation along a single diffraction vector, (ii) a look-up method to search for reflection pairs matching the interplanar angle of two selected diffraction spots, and (iii) a more efficient look-up scheme to search for reflection triplets matching three interplanar angles. Extensive tests show that all these schemes, together with the convenient graphical user interfaces and highly optimized computing algorithms, are reliable and powerful for recognizing and fitting Laue patterns of any crystal taken under any diffraction geometry.

PR-Trie: A Hybrid Trie with Ant Colony Optimization Based Prefix Partitioning for Memory-Efficient IPv4/IPv6 Route Lookup

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

String Stability of Connected Vehicle Platoons Under Lossy V2V Communication

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.

Complex queries over decentralised systems for geodata retrieval

Decentralised systems have been proved to be quite effective to allow for trusted and accountable data sharing, without the need to resort to a centralised party that collects all the information. While complete decentralisation provides important advantages in terms of data sovereignty, absence of bottlenecks and reliability, it also adds some issues concerned with efficient data lookup and the possibility to implement complex queries without reintroducing centralised components. In this paper, we describe a system that copes with these issues, thanks to a multi-layer lookup scheme based on Distributed Hash Tables that allows for multiple keyword-based searches. The service of peer nodes participating in this discovery service is controlled and rewarded for their contribution. Moreover, the governance of this process is completely automated through the use of smart contracts, thus building a Decentralised Autonomous Organization (DAO). Finally, we present a use case where road hazards are collected in order to test the goodness of our system for geodata retrieval. Then, we show results from a performance evaluation that confirm the viability of the proposal.

Scalable name identifier lookup for Industrial Internet

Name identifiers are considered more effective than IP addresses in routing and forwarding billions of heterogeneous resource-restrained Industrial Internet objects. Although the design of name-based longest prefix matching lookup has been developed for nearly a decade, it is still attractive to design an efficient name lookup scheme for the Industrial Internet due to hierarchical variable-length names, large-scale forwarding tables, and frequent updates. In this paper, we present a memory-optimized binary search (MOBS) algorithm in the counting Bloom filter (CBF), which decreases memory consumption by reducing the storage of prefix marker entries while maintaining the fast lookup efficiency of binary search. Furthermore, we propose CBF-HT, an efficient and scalable name lookup method that combines counting Bloom filter and hash table to facilitate content forwarding. CBF-HT consists of two stages. Firstly, the longest prefix matching results are obtained through binary search in the counting Bloom filter with MOBS, and then the final lookup results are acquired by linear backtracking in the hash table. CBF-HT can achieve high-speed lookups and fast updates, and significantly reduces memory consumption caused by marker entries through MOBS. We have implemented and evaluated CBF-HT with 10 billion name identifiers. Experimental results show that, compared with the existing binary search on hash table algorithm (BS-HT), CBF-HT increases the forwarding throughput by 45%, eliminates 73% and 90% of memory consumption, and improves the update performance by 48.1%, at the cost of a 7.8% increase in the average number of memory accesses.

A minimal resource high‐speed routing lookup mechanism for servers with NetFPGAs

AbstractThis paper studies how high‐performance computer servers, equipped with Field‐Programmable Gate Arrays (FPGAs), can perform high‐speed routing. In particular, routing table lookup is considered since it is a key packet processing bottleneck for future routers that need to operate at Petabits‐per‐second. The paper defines and evaluates three routing lookup schemes used by routers/switches, using Xilinx NetFPGA SUME programmable data plane hardware boards. Two existing schemes, namely IP address‐based Longest Prefix Matching and Multi‐Protocol Label Switching tag‐based exact matching are implemented. In addition, the paper presents a new routing scheme called No Lookup Forwarding Scheme (NLFS) that does not use conventional lookup tables. The NLFS approach adds a custom routing tag that does not require any form of (forwarding) table lookup, and independent of the Binary/Ternary Content Associate Memory (CAM) capacity in the switch/router. The experimental results show that the NLFS scheme utilizes less FPGA resources, while reducing packet lookup latency by up to 18% against longest prefix match which is the de facto lookup mechanism used in switches. The key contribution of this work is that it enables the use of NetFPGAs to reduce CAM capacity requirements in switches/routers to help reduce memory costs. The proposed scheme has several use cases, including provision of on‐demand increase of packet routing capacity in data center networks and support for Network Function Virtualization‐based systems.

Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie

Named data networking (NDN) is a content-centric network for the future of the internet. An NDN a packet is delivered based on a content name instead of a destination IP address. The name lookup for packet forwarding is challenging because the content name is variable, and its space is unbounded. This paper proposes a novel name lookup scheme that employs a hashing technique combined with Patricia tries. In this scheme, hash tables are dynamically maintained according to the hierarchical structure of the name, so the name can effectively accommodate variable and unbounded content names. Unlike chaining, a Patricia trie compares a key string only once at a leaf node, so it provides a fast name lookup. The proposed lookup scheme is implemented and evaluated by using an Intel Core i7-2600 CPU and 4GB of memory. Experimental results show that our scheme gives good scalability and a high throughput of name lookup with a controlled memory consumption.

Genetic Algorithm Based Prefix Partitioning for Memory-Efficient IPv4/6 Lookup

IP address lookup speed in core routers has a significant impact on service quality in communications in the Internet. The rapid growth of traffic on the Internet and the development of communication links working at speeds of several gigabits per second make IP lookup a challenge for IP routers. There are several IP lookup schemes based on counts of set bits of a bit-vector that encode the routing information in structures of a constant size that guarantee fast IP lookups. However, these schemes have exponential memory complexity and then prefix partitioning schemes are used to save memory consumption, performing the IP lookup in stages. In this work, we propose a prefix-partitioning scheme that optimizes the available memory usage in IP lookups in accordance with the prefix distribution in forwarding tables and an IP lookup scheme based in a precalculated count of set bits of a bit-vector. A complete IPv4/6 lookup has an instruction cost of two memory accesses in the best case and twice the number of stages in the worst case. The proposed prefix-partitioning scheme is based on heuristic search using genetic algorithms to find an optimal partitioning. Our proposal can be implemented in general purpose hardware for IPv4/6 lookups.

A power-saving pre-classifier for TCAM-based IP lookup

Ternary Content Addressable Memory (TCAM) is widely used for designing high-throughput forwarding engines on most of today's high-end routers. Despite its capability for line-speed queries, it is very power hungry and space inefficient. By making use of a pre-classifier to activate TCAM blocks selectively, MEET-IP, a recently proposed TCAM based IP lookup scheme, significantly improves the utilization of TCAMs. However, it suffers from performance degradation because it uses a two-level pre-classifier. In this paper, we propose SplitIP, a memory and power efficient TCAM-based scheme for IP routing table lookup. We first transform the IP lookup problem to a point location problem through a routing table projection. Based on the projection, we propose a top-down splitting algorithm to separate routing table prefixes evenly into TCAM blocks. Finally, a simpler one-level classifier is constructed for fast pre-classification using improved range encoding techniques. The top-down prefix partitioning algorithm combined with the database independent encoding scheme provides an incremental update for SplitIP. Experimental results show that our design achieves more than 97% power reduction with a TCAM storage overhead of less than 3% on average.

Composite Quantization.

This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$M) elements, each of which is selected from a different dictionary, to accurately approximate a $D$D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to $O(M)$O(M) from $O(D)$O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.

Query Learning-Based Scheme for Pertinent Resource Lookup in Mobile P2P Networks

P2P networking has grasped an increasing interest worldwide among both researchers and computer networking professionals. As a witness, several P2P applications mainly used for file sharing over the Internet have been proposed. Considering the great success of mobile devices in recent years, P2P applications have also been deployed over mobile networks such as mobile ad-hoc networks (MANETs). However, the mismatch between the P2P overlay and the MANET underlay topologies makes the resources lookup mechanism in mobile P2P applications very difficult. Therefore, this downside is the main hindrance to the deployment of such applications over MANETs. To overcome the mismatch issue, we propose in this paper RLSM-P2P a cross-layer resource lookup scheme for Mobile P2P applications. The main thrust of RLSM-P2P consists of building an efficient unstructured P2P overlay that closely matches the underlay physical network and swiftly adapts to its volatility and dynamicity by considering different MANET constraints. Furthermore, RLSM-P2P relies on a query learning resource lookup mechanism for locating pertinent resources to user queries. The performed experiments show that RLSM-P2P outperforms its competitors in terms of effectiveness and efficiency.

A general framework for shiftable position-based dual-image reversible data hiding

This paper proposes an improved method for shiftable position-based dual-image reversible data hiding. During the procedure of embedding data, the total number of shiftable coordinates is set as the parameter to make a trade-off between distortion and embedding rate. First, the optimal parameter is sought and the one-to-one code table is generated according to the expected embedding rate. Then, the message data is embedded into the cover image through a simple and effective code table lookup scheme to generate two visually similar stego images. During the procedure of data extraction, the message can be extracted in a reverse manner. The experimental results demonstrate that the proposed method has higher PSNR (peak signal-to-noise ratio) values than our previous work in the case of high embedding rates.

An Optical Content Addressable Memory Cell for Address Look-Up at 10 Gb/s

We propose and experimentally demonstrate the first all-optical content addressable memory (CAM) cell that comprises an all-optical monolithically integrated InP flip-flop and an optical XOR gate. The experimental results reveal error-free operation at 10 Gb/s for both content addressing and content writing operations. The potential of these memory architectures to allow for up to 40-Gb/s operation could presumably lead to fast CAM-based routing applications by enabling all-optical address look-up schemes.

High-performance IPv6 address lookup in GPU-accelerated software routers

Due to the ever-increasing physical link speed, routing table size and internet traffic, modern routers have been the major bottleneck to process packages with a high throughout. As the most time-consumption task of routers, designing efficient IP lookup schemes for IPv6 face new challenges. In this paper, we design parallel bloom filter for IPv6, and implement it on Graphics Processing Unit to develop a novel GPU-accelerated software router, GRv6. Moreover, we design two schemes to support dynamic prefix update, i.e., dynamic prefix insert scheme and dynamic prefix delete scheme. To evaluate the performance of GRv6, we implement it with NVIDIA GeForce GTX 580 and utilize 5 real-life IPv6 routing tables to demonstrate that the IP lookup engine could achieve 60Gbps for static routing tables, and 40Gbps for dynamic routing tables with 3000 updates per second.

A novel table look-up scheme based on GFScom and its application

This work considers Mamdani fuzzy systems constructed from finite input–output data pairs using the generalized fuzzy sets with contradictory, opposite and medium negation (GFScom). Considering that the information available often consists of a set of finite numerical data pairs, a new table look-up scheme for constructing Mamdani fuzzy systems is presented. The designed fuzzy system is proved to be capable of approximating any real continuous function on a compact set to arbitrary degree of accuracy. We use this fuzzy modeling method for the truck back-upper control problem. The effectiveness of the proposed method is demonstrated by a comparison with the traditional table look-up scheme.

IPersea: Towards improving the Sybil-resilience of social DHT

P2P systems are highly susceptible to Sybil attacks, in which an attacker creates a large number of identities and uses them to control a substantial fraction of the system. Persea is the most recent approach towards designing a social network based Sybil-resistant DHT. Unlike prior Sybil-resistant P2P systems based on social networks, Persea does not rely on two key assumptions: (i) that the social network is fast mixing, and (ii) that there is a small ratio of attack edges to honest peers. Both assumptions have been shown to be unreliable in real social networks. The hierarchical distribution of node IDs in Persea confines a large attacker botnet to a considerably smaller region of the ID space than in a normal P2P system and its replication mechanism lets a peer to retrieve the desired results even if a given region is occupied by attackers. However, Persea system suffers from certain limitations, since it cannot handle the scenario, where the malicious target returns an incorrect result instead of just ignoring the lookup request. In this paper, we address this major limitation of Persea through a Sybil detection mechanism built on top of Persea system, which accommodates inspection lookup, a specially designed lookup scheme to detect the Sybil nodes based on their responses to the lookup query. We design a scheme to filter those detected Sybils to ensure the participation of honest nodes on the lookup path during regular DHT lookup. Since the malicious nodes are opt-out from the lookup path in our system, they cannot return any incorrect result during regular lookup. We evaluate our system in simulations with social network datasets and the results show that catster, the largest network in our simulation with 149,700 nodes and 5,449,275 edges, gains 100% lookup success rate, even when the number of attack edges is equal to the number of benign peers in the network.

Fuzzy Lane-Changing Models with Socio-Demographics and Vehicle-to- Infrastructure System Based on a Simulator Test

Objective: We investigated the impacts of drivers’ socio-demographic factors on these lane-changing models, and developed Fuzzy logic-based lane-changing models associated the drivers’ demographic factors. Methods: Forty drivers were recruited for a driving simulator test to collect their driving behaviors of changing lane in a work zone with/without the aid of Driver’s Smart Advisory System (DSAS), a Vehicle-to-Infrastructure (V2I) communication system. Fuzzy table look-up scheme was selected to model Lane-Changing Reaction Time (LCRT) and Lane-Changing Reaction Distance (LCRD) incorporated with drivers' socio-demographic information and the collected driving behaviors. Findings: Without DSAS messages, the elders slowly responded to a static traffic guide of lane-change, but they didn’t change lane at the last minute. The highly educated and young drivers changed lane the earliest. When DSAS messages were provided, all drivers’ LCRT became shorter while their LCRD longer. Conclusion: Drivers’ age and the level of education are essential socio-demographic factors in lane-changing process. The DSAS is able to instruct all drivers to take earlier lane-changing actions. The developed models can predict drivers’ LCRT and LCRD accurately.

Shell: A Spatial Decomposition Data Structure for Ray Traversal on GPU

Shared memory many-core processors such as GPUs have been extensively used in accelerating computation-intensive algorithms and applications. When porting existing algorithms from sequential or other parallel architecture models to shared memory many-core architectures, non-trivial modifications are often needed to match the execution patterns of the target algorithms with the characteristics of many-core architectures. Ray traversal is a fundamental process in many applications, and is commonly accelerated by spatial decomposition schemes captured in hierarchical data structures (e.g., kd-trees). However, ray traversal using hierarchical data structures needs to conduct repeated hierarchical searches. Such search process is time-consuming on shared memory many-core architectures since it incurs considerable amounts of expensive memory accesses and execution divergence. In this paper, we propose a novel spatial decomposition based data structure, called Shell, which completely avoids hierarchical search for ray traversal. In Shell, a structure is built on the boundary of each region in the decomposed space, which allows any ray traversing in a region to find the next neighboring region to traverse using table lookup schemes, without any hierarchical search. While our ray traversal approach works for other spatial decomposition paradigms and many-core processors in higher dimensional scenes, we illustrate it using kd-tree on GPU for 3D scenario and compare with the fastest known kd-tree searching algorithms for ray traversal. Experimental results in graphics ray tracing and radiation dose calculation show that our approach improves the performance by 3.5-5.5 $\times$ over the fastest known kd-tree based approaches.

Helix: IP lookup scheme based on helicoidal properties of binary trees

In this paper, we propose an IP lookup scheme, called Helix, that performs parallel prefix matching at the different prefix lengths and uses the helicoidal properties of binary trees to reduce tree height. The reduction of the tree height is achieved without performing any prefix modification. Helix minimizes the amount of memory used to store long and numerous prefixes and achieves IP lookup and route updates in a single memory access. We evaluated the performance of Helix in terms of the number of memory accesses and amount of memory required for storing large IPv4 and IPv6 routing tables with up to 512,104 IPv4 and 389,956 IPv6 prefixes, respectively. In all the tested routing tables, Helix performs lookup in a single memory access while using very small memory amounts. We also show that Helix can be implemented on a single field-programmable gate array (FPGA) chip with on-chip memory for the IPv4 and IPv6 tables considered herein, without requiring external memory. Specifically, Helix uses up to 72% of the resources of an FPGA to accommodate the most demanding routing table, without performance penalties. The implementation shows that Helix may achieve lookup speeds beyond 1.2 billion packets per second (Gpps).

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Considering the fact that the signs of performance deterioration in the main components of gas turbines vary in different operating points, thus designing a Fault Detection and Identification (FDI) system using a single fault pattern for all engine operating ranges can reduce the ability of the diagnosis system to identify the intensity or even the type of any potential degradation. So, in this article, with the aid of the “load” parameter as an augmented input and using the fault patterns obtained at different part-load conditions, a fuzzy-based FDI system is proposed for an industrial two-shaft gas turbine with the ability to use in both the full and part-load operations. In the proposed FDI system, fuzzy rule base is generated by a table look-up scheme and by employing the available input/output data extracted from fault signature table. Moreover, a global optimization technique is used to determine some database parameters, such as the number of membership functions of input variables and their standard deviations. The optimization is carried out to make a compromise between the diagnosis accuracy and robustness against measurement noise. In the present work, the performance of the proposed FDI system is evaluated against the most common cause of gas turbine performance deterioration i.e. fouling and erosion in terms of the success rate and the estimation accuracy at different levels of sensor noise. The results obtained indicate that the proposed FDI system can considerably reduce the average estimation error by 0.1–0.75% and increase the success rate by 10–20% compared with the diagnosis systems designed for a specific operating point. The results also demonstrate that the proposed FDI system has robust performance against measurement uncertainties, and moreover smearing effects are rarely seen in the results.