MBitCuts: optimal bit-level cutting in geometric space packet classification

Abbasi, Mahdi; Fazel, Saeideh Vesaghati; Rafiee, Milad

doi:10.1007/s11227-019-03090-3

Cited by 17 publications

(7 citation statements)

References 28 publications

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“…Software-based methods have been widely researched. Many studies have sought to reduce the time and frequency of memory access by making use of certain techniques for developing algorithms and data structures [16,17].…”

Section: Rtu Rtumentioning

confidence: 99%

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

et al. 2022

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The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.

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Section: Rtu Rtumentioning

confidence: 99%

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

et al. 2022

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“…In addition, when dividing the multidimensional space where the rule is located, a rule may need to be copied to multiple rule groups, resulting in a large amount of storage space for the method. Efficuts, SmartSplit and other methods [22][23][24] have some improvements in the depth of constructing decision tree compared with classical methods, but they do not fundamentally solved the problem of sequential matching after rule replication and matching to leaf nodes. From the perspective of solving these two problems, a decision tree packet classification method Uscuts based on cell space division is proposed.…”

Section: Decision-tree-based Methodsmentioning

confidence: 99%

MpFPC–A Parallelization Method for Fast Packet Classification

Cheng

Shi

2022

IEEE Access

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Packet classification is the core technology of network layer and an important means to ensure the security of network system. With the rapid development of network technology, higher requirements are put forward for the speed of network packet classification. This paper improves the traditional single thread package classification framework, A new parallelization method for fast packet classification (MpFPC) based on distributed computing is proposed, the method adopts the packet classification idea based on decision tree, but compared with the traditional algorithm, a rule mapping preprocessing process is added before constructing the classification decision tree, which effectively removes the rule redundancy and conflict, so as to avoid the rule replication problem of the traditional decision-tree-based method. In addition, the method can group the rules and data packets at the same time, which improves the packet classification efficiency. Experimental results show that MpFPC method has high classification efficiency and has obvious speed advantage compared with Uscuts method with time complexity of O(klogn). In addition, the test results also show that the classification speed of MpFPC will increase with the increasing number of computing nodes, which provides a new possible way to meet the classification wire-speed requirement.

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“…This tool meets the needs of the developers of packet classification algorithms to genuine and heterogeneous filters used in firewalls, IP chains, and access control lists. Due to lack of real datasets, in the majority of the studies [44][45][46][47][48][49][50], the ClassBench tool has been used for producing the required data structure due to a need for filters and packets that are close to reality in terms of structural characteristics and statistical distribution. We produced a set of filters corresponding to the ACL2 parameter containing 1k and 10k filters with 1k to 64k packets to assess the proposed method.…”

Section: Methodsmentioning

confidence: 99%

Investigating the efficiency of multithreading application programming interfaces for parallel packet classification in wireless sensor networks

Abbasi¹,

Rafiee²,

Khosravi³

2020

Turk J Elec Eng & Comp Sci

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This paper investigates the most appropriate application programming interface (API) that best accelerates the flow-based applications on the wireless sensor networks (WSNs). Each WSN include many sensor nodes which have limited resources. These sensor nodes are connected together using base stations. The base stations are commonly network systems with conventional processors which are responsible for handling a large amount of communicated data in flows of network packets. For this purpose, classification of the communicated packets is considered the primary process in such systems. With the advent of high-performance multicore processors, developers in the network industry have considered these processors as a striking choice for implementing a wide range of flow-based wireless sensor networking applications. The main challenge in this field is choosing and exploiting an API which best allows multithreading; i.e. one which maximally hides the latency of performing complex operations by threads and increases the overall efficiency of the cores. This paper assesses the efficiency of Thread, Open Multiprocessing, and Threading Building Blocks (TBB) libraries in multithread implementation of set-pruning and grid-of-tries packet classification algorithms on dual-core and quad-core processors. In all cases, the speed and throughput of all parallel versions of the classification algorithms are much more than the corresponding serial versions. Moreover, for parallel classification of a sufficiently large number of packets by both classification algorithms, TBB library results in higher throughput and performance than the other libraries due to its automatic scheduling and internal task stealing mechanism.

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MBitCuts: optimal bit-level cutting in geometric space packet classification

Cited by 17 publications

References 28 publications

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

MpFPC–A Parallelization Method for Fast Packet Classification

Investigating the efficiency of multithreading application programming interfaces for parallel packet classification in wireless sensor networks

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