Tuple Space Search Research Articles

Elastically accelerating lookup on virtual SDN flow tables for software-defined cloud gateways

In recent years, Software-Defined Networking has been gradually applied to cloud gateways to provide efficient, reliable and flexible data transmission services for various large-scale cloud platforms. However, massive concurrent accessing produces huge network traffic from tenants to cloud platforms, which aggregates at cloud gateways and brings serious performance bottlenecks regarding packet classification. To solve this problem, this paper proposes an elastically accelerated lookup method of virtual SDN flow tables for software-defined cloud gateways. The method caches active exact flows in virtue of network traffic locality, enabling most packets to directly hit the cache and bypass tuple space search, which significantly accelerates flow table lookup. Focusing on network traffic jitters, the cache adaptively adjusts its capacity according to the dynamic changes of the number of active exact flows to maintain high cache hit rates, aiming to achieve elastic acceleration of flow table lookup. Furthermore, we theoretically derive the performance metrics of our proposed method such as cache hit rates, cache yield rates and average search length, based on the Zipf distribution model of network traffic. Eventually, we evaluate the performance of our proposed elastically accelerated lookup method by experiments with real network traffic traces. Experimental results indicate that our proposed method outperforms existing cache-accelerated methods with stable cache hit rates around 80% and the speedup of average search length up to 2.84 even under network traffic jitters.

FPGA-Based Updatable Packet Classification Using TSS-Combined Bit-Selecting Tree

OpenFlow switches are being deployed in SDN to enable a wide spectrum of non-traditional applications. As a promising alternative to brutal force TCAMs, FPGA-based packet classification is being actively investigated. However, none of the existing FPGA designs can achieve high performance on both search and update for large-scale rule sets. To address this issue, we propose TcbTree, an FPGA-based algorithmic scheme for packet classification. Specifically, at the algorithmic side, i) a two-stage framework consisting of heterogeneous algorithms is proposed, where most rules can be mapped into several balanced trees without rule replications, ii) for the remaining few rules, a centralized TSS (Tuple Space Search) architecture together with a real-time feedback scheme is designed to enhance the efficiency of TSS search on FPGA, and iii) a tree dilution method is designed to equalize rule distribution in trees, so that the latency of tree search can be reduced. At the hardware side, i) an efficient data structure set is designed to convert tree traversal to addressing process, which breaks the constraints of limited tree depth and imbalanced node distribution, and ii) distinct from fully pipelined designs, multiple levels of parallelism are efficiently explored with multi-core, multi-search-engine and coarse-grained pipelines herein. Experimental results using ClassBench show that, with the implementation of TcbTree on FPGA, the average classification throughputs for 1k, 10k, 32k and 100k rule sets achieve 788.8 MPPS, 404.3 MPPS, 237 MPPS and 41.8 MPPS, respectively, and the update throughput for all benchmark rule sets is above 1 MUPS.

A Learned Bloom Filter-Assisted Scheme for Packet Classification in Software-Defined Networking

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.

Improving Open Virtual Switch Performance Through Tuple Merge Relaxation in Software Defined Networks

Open vSwitch (OVS) is a widely used virtual switch designed to provide virtual network capabilities in virtualized environments. As the core of OVS, the packet classification task is time-consuming with the implementation of Tuple Space Search (TSS), a classical hash table-based algorithm that can achieve fast rule updating but at the cost of the reduced packet classification throughput. However, because of the central role of OVS in a virtualized environment, its performance is of utmost importance and we need mechanisms that can achieve high update rate along with high packet classification throughput. In this paper, we compare the performance of several classification algorithms and show that Tuple Merge Relaxation (TMR) is able to achieve the highest sustainable classification throughput while dealing with updates. After integrating it into OVS and evaluating its in vivo performance, we observe that TMR-OVS can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$24.7\times $ </tex-math></inline-formula> higher throughput compared with native OVS. Moreover, we show that TMR-OVS is also effective against Tuple Space Explosion attack effectively and maintains OVS throughput under this attack.

Fast Online Packet Classification With Convolutional Neural Network

Packet classification is a critical component in network appliances. Software Defined Networking and cloud computing update the rulesets frequently for flexible policy configuration. Tuple Space Search (TSS), implemented in Open vSwitch (OVS), achieves fast rule updating at the sacrifice of the classification rate. In TSS, each tuple is managed by a hash table and classifying a packet needs to go through all hash tables. Merging tuples can reduce the number of hash tables, but inevitably increases the hash conflicts that may even worsen the classification performance in some cases. No existing algorithm meets the need of both fast packet classification and online rule updating. In this paper, we propose Convolutional Neural Network (CNN)-based Range Partition (CRP) to achieve fast packet classification and online update simultaneously. CRP exploits CNN-based image recognition to quickly partition tuples into range spaces upon the change of ruleset distribution, which reduces hash operations while avoiding rule overlapping caused by hashing many rules to the same location of the hash table. Experimental results demonstrate that CRP achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula> classification speed and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.2\times $ </tex-math></inline-formula> update speed on average compared with state-of-the-art algorithms. We also implement CRP in OVS. The throughput of CRP-OVS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> that of native OVS.

DynamicTuple: The dynamic adaptive tuple for high-performance packet classification

The frequent rule updating in Software Defined Networking and cloud computing requires the packet classification algorithm to perform fast packet matching and flow table updating simultaneously. The existing algorithms, e.g. decision-tree-based or hash-based, achieve fast packet classification at the sacrifice of the rule updating or vice versa. We propose the Dynamic Adaptive Tuple (DynamicTuple) for both fast packet classification and rule updating simultaneously. DynamicTuple builds a performance model of tuple based packet classification and exploits dynamic programming to find the appropriate tuple formulation to minimize the lookup time. When the rules keep updating, DynamicTuple can retain the proper tuples and rebuild the improper tuples, thus the tuple in DynamicTuple is dynamic adaptive. The experimental results demonstrate that DynamicTuple achieves 33.1x, 5.6x, 5.7x classification speed and 2.9x, 11.0x, 8.5x updating speed compared to Tuple Space Search, TupleMerge, PartitionSort. By implementing DynamicTuple, the OpenFlow table of Open vSwitch achieves 26.6x classification speed.

Efficient Flow Processing in 5G-Envisioned SDN-Based Internet of Vehicles Using GPUs

In the 5G-envisioned Internet of vehicles (IoV), a significant volume of data is exchanged through networks between intelligent transport systems (ITS) and clouds or fogs. With the introduction of Software-Defined Networking (SDN), the problems mentioned above are resolved by high-speed flow-based processing of data in network systems. To classify flows of packets in the SDN network, high throughput packet classification systems are needed. Although software packet classifiers are cheaper and more flexible than hardware classifiers, they could only deliver limited performance. A key idea to resolve this problem is parallelizing packet classification on graphical processing units (GPUs). In this paper, we study parallel forms of Tuple Space Search and Pruned Tuple Space Search algorithms for the flow classification suitable for GPUs using CUDA (Compute Unified Device Architecture). The key idea behind the offered methodology is to transfer the stream of packets from host memory to the global memory of the CUDA device, then assigning each of them to a classifier thread. To evaluate the proposed method, the GPU-based versions of the algorithms were implemented on two different CUDA devices, and two different CPU-based implementations of the algorithms were used as references. Experimental results showed that GPU computing enhances the performance of Pruned Tuple Space Search remarkably more than Tuple Space Search. Moreover, results evinced the computational efficiency of the proposed method for parallelizing packet classification algorithms.

Efficient parallelisation of the packet classification algorithms on multi‐core central processing units using multi‐threading application program interfaces

The categorisation of network packets according to multiple parameters such as sender and receiver addresses is called packet classification. Packet classification lies at the core of Software-Defined Networking (SDN)-based network applications. Due to the increasing speed of network traffic, there is an urgent need for packet classification at higher speeds. Although it is possible to accelerate packet classification algorithms through hardware implementation, this solution imposes high costs and offers limited development capacity. On the other hand, current software methods to solve this problem are relatively slow. A practical solution to this problem is to parallelise packet classification using multi-core processors. In this study, the Thread, parallel patterns library (PPL), open multi-processing (OpenMP), and threading building blocks (TBB) libraries are examined and implemented to parallelise three packet classification algorithms, i.e. tuple space search, tuple pruning search, and hierarchical tree. According to the results, the type of algorithm and rulesets may influence the performance of parallelisation libraries. In general, the TBB-based method shows the best performance among parallelisation libraries due to using a theft mechanism and can accelerate the classification process up to 8.3 times on a system with a quad-core processor.

Common non-wildcard portion-based partitioning approach to SDN many-field packet classification

Many-field packet classification is a challenging function of the devices in software-defined networking. In this paper, we propose a new algorithm, which partitions a ruleset in a simple way based on non-wildcard portions of the rules. A portion can be a field or a sub-field. The algorithm uses hash tables as the base data structure. In a partition, all the members have a common non-wildcard portion, which is used as the hash key. It means that only a portion of the rules and headers is used for hashing. It simplifies using the hash table for packet classification, which deals with the ternary vectors. The proposed algorithm supports fast updating as a required feature for most of today’s networks. Extensive simulations are conducted to evaluate the algorithm and compare it with well-known algorithms. Results show that the proposed algorithm has a 196% higher throughput and 81% faster update than Tuple Space Search as the base classification algorithm of OpenVSwitch.

Tuple Space Assisted Packet Classification With High Performance on Both Search and Update

Software switches are being deployed in SDN to enable a wide spectrum of non-traditional applications. The popular Open vSwitch uses a variant of Tuple Space Search (TSS) for packet classifications. Although it has good performance on rule updates, it is less efficient than decision trees on lookups. In this paper, we propose a two-stage framework consisting of heterogeneous algorithms to adaptively exploit different characteristics of the rule sets at different scales. In the first stage, partial decision trees are constructed from several rule subsets grouped with respect to their small fields . This grouping eliminates rule replications at large scales, thereby enabling very efficient pre-cuttings. The second stage handles packet classification at small scales for non-leaf terminal nodes , where rule replications within each subspace may lead to inefficient cuttings. A salient fact is that small space means long address prefixes or less nesting levels of ranges, both indicating a very limited tuple space. To exploit this favorable property, we employ a TSS-based algorithm for these subsets following tree constructions. Experimental results show that our work has comparable update performance to TSS in Open vSwitch, while achieving almost an order-of-magnitude improvement on classification performance over TSS.

Clustering-based many-field packet classification in Software-Defined Networking

Packet classification is one of the main core functions of networking. With the advent of Software-Defined Networking, packet classification has become more challenging by introducing many-field rulesets. In this paper, we propose an algorithm, Clustering-Based Packet Classification (CBPC), which divides ruleset into some clusters using a new hybrid clustering method, based on an innovative bit-level view. Those rules that have more common wildcard and non-wildcard bit positions are put into the same cluster. Each cluster uses common non-wildcard positions of its rules to produce keys for a hash table insertion and query stages. This makes possible to use hash tables without involving difficulties with inserting and querying ternary vectors, because our algorithm converts it to simple binary operations. In fact, we ignore a portion of information of the rules in key production for the hash tables. It overcomes the problem of extending wildcards to all of the possible values. It is true that some information is lost but it is covered by full matching at the hash table entries. We propose two versions for CBPC, online and offline. The online version supports update, which is an important requirement for today's packet classification algorithms. The proposed algorithm is evaluated and compared with some well-known and state-of-the-art algorithms by extensive simulations. The results show that Online-CBPC achieves 197% higher throughput and 64% faster update than Tuple Space Search, OpenVSwitch standard algorithm, while using almost the same amount of memory.

TupleMerge: Fast Software Packet Processing for Online Packet Classification

Packet classification is an important part of many networking devices, such as routers and firewalls. Software-defined networking (SDN) heavily relies on online packet classification which must efficiently process two different streams: incoming packets to classify and rules to update. This rules out many offline packet classification algorithms that do not support fast updates. We propose a novel online classification algorithm, TupleMerge (TM), derived from tuple space search (TSS), the packet classifier used by Open vSwitch (OVS). TM improves upon TSS by combining hash tables which contain rules with similar characteristics. This greatly reduces classification time preserving similar performance in updates. We validate the effectiveness of TM using both simulation and deployment in a full-fledged software router, specifically within the vector packet processor (VPP). In our simulation results, which focus solely on the efficiency of the classification algorithm, we demonstrate that TM outperforms all other state of the art methods, including TSS, PartitionSort (PS), and SAX-PAC. For example, TM is 34% faster at classifying packets and 30% faster at updating rules than PS. We then experimentally evaluate TM deployed within the VPP framework comparing TM against linear search and TSS, and also against TSS within the OVS framework. This validation of deployed implementations is important as SDN frameworks have several optimizations such as caches that may minimize the influence of a classification algorithm. Our experimental results clearly validate the effectiveness of TM. VPP TM classifies packets nearly two orders of magnitude faster than VPP TSS and at least one order of magnitude faster than OVS TSS.

A Sorted-Partitioning Approach to Fast and Scalable Dynamic Packet Classification

The advent of software-defined networking (SDN) leads to two key challenges for packet classification on the dramatically increased dynamism and dimensionality. Although packet classification is a well-studied problem, no existing solution satisfies these new requirements without sacrificing classification speed. Decision tree methods, such as HyperCuts, EffiCuts, and SmartSplit can achieve high-speed packet classification, but support neither fast updates nor high dimensionality. The tuple space search (TSS) algorithm used in Open vSwitch achieves fast updates and high dimensionality but not high-speed packet classification. In this paper, we propose a hybrid approach, PartitionSort, that combines the benefits of both TSS and decision trees achieving high-speed packet classification, fast updates, and high dimensionality. A key to PartitionSort is a novel notion of ruleset sortability that provides two key benefits. First, it results in far fewer partitions than the TSS. Second, it allows the use of multi-dimensional interval trees to achieve logarithmic classification and update time for each sortable ruleset partition. Our extensive experimental results show that The PartitionSort is an order of magnitude faster than the TSS in classifying packets while achieving comparable update time. The PartitionSort is a few orders of magnitude faster in construction time than SmartSplit, a state-of-the-art decision tree classifier, while achieving a competitive classification time. Finally, the PartitionSort is scalable to an arbitrary number of fields and requires only linear space.

Multi-match Packet Classification Scheme Combining TCAM with an Algorithmic Approach

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wirespeed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multimatch classifications.

A Bloom Filter with the Integrated Hash Table Using an Additional Hashing Function

A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. In recent years, Bloom filters have increased in popularity in database and networking applications. A Bloom filter has two steps that called programming and membership query. In this paper, we introduce a new approach to integrate a hash table with Bloom filter to decrease the hash table access time. This means that when a Bloom filter for an incoming item is programmed, the incoming item simultaneously is stored in a hash table. In addition in the membership query step, if the query is successful, simultaneously the address of item in the hash table is generated. Furthermore, we analyze the average bucket size, maximum search length and number of collisions for the proposed approach and compare to the fast hash table (FHT) approach. We implemented our approach in a software packet classifier based on tuple space search with the $H3$ class of universal hashing functions. Our results show that our approach is able to reduce the average bucket size, maximum search length and number of collisions when compared to a FHT.

New Fields in Classifying Algorithms for Content Awareness

The content aware (CA) packet classification and processing at network level is a new approach leading to significant increase of delivery quality of the multimedia traffic in Internet. This paper presents a solution for a new multi-dimensional packet classifier of an edge router, based on content - related new fields embedded in the data packets. The technique is applicable to content aware networks. The classification algorithm is using three new packet fields named Virtual Content Aware Network (VCAN), Service Type (STYPE), and U (unicast/multicast) which are part of the Content Awareness Transport Information (CATI) header. A CATI header is inserted into the transmitted data packets at the Service/Content Provider server side, in accordance with the media service definition, and enables the content awareness features at a new overlay Content Aware Network layer. The functionality of the CATI header within the classification process is then analyzed. Two possibilities are considered: the adaptation of the Lucent Bit vector algorithm and, respectively, of the tuple space search, in order to respond to the suggested multi-fields classifier. The results are very promising and they prove that theoretical model of inserting new packet fields for content aware classification can be implemented and can work in a real time classifier.

Comparison Study Of Packet Classification Algorithms In Wired Networks

The packet classification technique play an important role for many internet advanced services that require the capability to distinguish traffic in different flows, such as routers and security services like firewalls and packet filters. This paper provide a brief introduction for packet classification process. Beside, the categorization of the algorithms that classify packets to different flow. Also, this paper give a brief description of two homogenous algorithms (algorithms that belongs to same category), these algorithms are Tuple Space Search (TSS) and Recursive Flow Classification (RFC). Furthermore, this paper provides a comparative evaluation of these two algorithms (TSS & RFC). The evaluation of these techniques are done under Linux REDHAT 5.0 platform in order to analyze and compare their performance between each other. In particular, the comparison focuses on achieving a good classification time in classification process and, low memory consumption rate.

A Cache Architecture for Counting Bloom Filters: Theory and Application

Within packet processing systems, lengthy memory accesses greatly reduce performance. To overcome this limitation, network processors utilize many different techniques, for example, utilizing multilevel memory hierarchies, special hardware architectures, and hardware threading. In this paper, we introduce a multilevel memory architecture for counting Bloom filters. Based on the probabilities of incrementing of the counters in the counting Bloom filter, a multi-level cache architecture called the cached counting Bloom filter (CCBF) is presented, where each cache level stores the items with the same counters. To test the CCBF architecture, we implement a software packet classifier that utilizes basic tuple space search using a 3-level CCBF. The results of mathematical analysis and implementation of the CCBF for packet classification show that the proposed cache architecture decreases the number of memory accesses when compared to a standard Bloom filter. Based on the mathematical analysis of CCBF, the number of accesses is decreased by at least 53%. The implementation results of the software packet classifier are at most 7.8% (3.5% in average) less than corresponding mathematical analysis results. This difference is due to some parameters in the packet classification application such as number of tuples, distribution of rules through the tuples, and utilized hashing functions.

Performance Improvement of Packet Classification for Enabling Differentiated Services

In differentiated services, packet classification is used to categorize incoming packets into multiple forwarding classes based on pre-defined filters and make information accessible for quality of service. Although numerous algorithms have presented novel data structures to improve the search performance of packet classification, the performance of these algorithms are usually limited by the characteristics of filter databases. In this paper, we use a different approach of filter preprocessing to enhance the search performance of packet classification. Before generating the searchable data structures, we cluster filters in a bottom-up manner. The procedure of the filter clustering merges filters with high degrees of similarity. The experimental results show that the technique of filter clustering could significantly improve the search performance of Pruned Tuple Space Search, a notable hash-based algorithm. As compared to the prominent existing algorithms, our enhanced Pruned Tuple Space Searchalso has superior performance in terms of speed and space.

Scalable packet classification using a compound algorithm

AbstractIn next‐generation networks, packet classification is used to categorize incoming packets into multiple forwarding classes based on pre‐defined filters and make information accessible for quality of service or security handling in the network. To pursue better search performance, numerous algorithms of packet classification have been proposed and optimized for certain filter databases; however, these algorithms may not scale in either storage or speed performance or both for the filter databases with different characteristics. This paper presents an efficient algorithm by combining two complementary algorithms,Cross‐productingandPruned Tuple Space Search, to make packet classification both fast and scalable. Unlike the existing algorithms whose performance is contingent on the filter database attributes, our algorithm shows better scalability and feasibility. We evaluate the performance of our scheme with filter databases of varying sizes and characteristics. The experimental results demonstrate that the new algorithm improves the speed and storage performance simultaneously. We also introduce the procedure of incremental updates. Copyright © 2009 John Wiley &amp; Sons, Ltd.