Flow Entries Research Articles

Accurate and efficient elephant-flow classification based on co-trained models in evolved software-defined networks

Accurate early classification of elephant flows (elephants) is important for network management and resource optimization. Elephant models, mainly based on the byte count of flows, can always achieve high accuracy, but not in a time-efficient manner. The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks (SDNs) to achieve a better resource efficiency. This paper addresses this situation by combining co-training and Reinforcement Learning (RL) to enable a closed-loop classification approach that divides the entire classification process into episodes, each involving two elephant models. One predicts elephants and is retrained by a selection of flows automatically labeled online by the other. RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase. Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%, and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.

Determination of the required degree of surface perforation of collecting drainage pipelines

The operational characteristics of a collecting pressure pipeline, where the flow enters through holes and slots along its length in a turbulent mode, and a pressure drainage pipeline, where liquid enters through the side walls in a filtration mode, are investigated. Based on a comparison of analytical dependencies used to describe the flow entry laws into the pipelines in each case, relatively simple and practical calculation formulas have been derived to determine the required degree of the side walls perforation of the drainage pipeline. These formulas ensure that the desired water flow rate is achieved in the collector. The degree of perforation S in this work is defined as the ratio of the area of the perforation holes (slots) in the side walls of the drainage pipeline to the total area of its side surface. In the analysis of the pipelines considered, parameter A is used, which comprehensively accounts for both the design and filtration characteristics of the drainage pipelines. Relevant analytical dependencies and a graph for determining this parameter are presented. A series of calculations have been performed using the proposed formulas to determine the degree of perforation for these pipelines. It is shown that, in general, the magnitude of the perforation degree is influenced by both the structural characteristics of the pipeline itself and the filtration properties of the surrounding soil and the surface of the drainage pipes.

Utilizing TabNet Deep Learning for Elephant Flow Detection by Analyzing Information in First Packet Headers.

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments.

Dynamic Link Metric Selection for Traffic Aggregation and Multipath Transmission in Software-Defined Networks

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission.

Bleed air CFD modelling in aerodynamic simulation of A heavy duty gas turbine compressor

This study investigates the effects of different bleed air CFD modelling methods on the aerodynamic simulation of a heavy-duty gas turbine multistage compressor with bleed airflow. Two methods, the local source term method and the simplified bleed off-take geometry method, are compared. The results reveal that while overall compressor performance shows minimal variation between the methods, significant differences are observed near the blade tip region. The bleed source term method predicts a more uniform flow field in the bleed cavity, leading to a larger surge margin for the compressor. Therefore, in the analysis of multistage compressors, it is advisable to adopt simplified geometric modeling method for simulating bleed air extraction. Moreover, when utilizing this approach, careful consideration should be given to the choice of the slot inclination angle. This design parameter presents a crucial trade-off between losses within the exhaust structure and those arising from the entry of mainstream flow into the exhaust slot cavity.

A model transformation based security policy automatic management framework for software-defined networking

Software-defined networking (SDN) has been utilized to enforce the security of traditional networks. However, the existing SDN-based security enforcement mechanisms rely heavily on the security policies containing the underlying information of data plane, such as MAC address, IP address or switch ports. These security policies need to be specifically developed by network operators and loaded into the control plane manually. With increasing the scale of underlying network, the existing security policy management mechanisms confront more and more challenges. The security policy transformation for SDN networks is to research how to transform the high-level security policy without containing the underlying information into the practical flow entries used by Openflow switches automatically, thereby implementing the automatic management of security policies. To achieve this objective, we propose a model transformation based security policy automatic management framework for software-defined networking in this paper. Leveraging its functional modules, the framework can solve the problems of how to find a connected path for each access control rule of security policy model (SPM) in data plane, how to transform the connected path into the system model of flow entries, as well as how to generate the practical flow entries according to the system model of flow entries. In order to validate the effectiveness and performance of framework, we implement the framework by leveraging POX controller and Mininet emulator. The experimental results illustrate the framework can transform SPM into practical flow entries, synchronously perceive the modifications caused by cutting down one connected path or changing SPM, and continuously keep the data plane holding the security properties defined by SPM at runtime.

Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNs

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller–switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time.

Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach

Software-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things (IoT) systems exhibit shortcomings in accurately predicting outcomes and efficiently reducing table misses and associated performance metrics. This research introduces a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme utilizes the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is employed to select heavy and active flows. As a result, efficient flow rules for incoming flows can be dynamically implemented without controller intervention. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

Modified smell detection algorithm for optimal paths engineering in hybrid SDN

Optimal path engineering in hybrid software defined networking environments involves leveraging traditional networking and software defined capabilities to determine the most efficient paths for network traffic. In hybrid software defined networks, some parts of the network may be controlled by controllers, while others rely on traditional routing protocols. Leveraging the OpenFlow protocol, widely used in such deployments, to program flow entries in software defined switches is one approach to achieving optimal path engineering in hybrid software defined networks. By specifying specific rules and policies in the switches, we can control traffic forwarding behaviour and steer traffic along desired paths based on application requirements. In this work, the smell detection algorithm, a new variant of bio-inspired method, is adopted to explore its capability in routing. The new paradigm of smell detection agent, its architecture, protocols used, and the concept of hybrid defined network is also explained. A comprehensive experimental examination of within the controller, an algorithm based on smell detecting agents computes the most efficient route from where it began to the intended destination. Simulation results readily demonstrate the smell detection agent based technique's effective functioning in getting the optimum direction. In this work, bio-inspired algorithms outperformed conventional algorithms in terms of performance.

Design of a sheathed water condensation particle counter with variable saturation ratio

We consider a particle condensation device where a cold aerosol enters at one open end of a tube. The wall of the tube is porous beyond a certain section. A stream of cold air is injected radially and symmetrically through an initial focusing region of the porous wall, and a stream of warm humid air with a saturation ratio S is injected in a condensation region further downstream. The configuration offers several advantages over existing water condensation particle counters (WCPCs). First, S may be varied by mixing a dry gas stream with a humid stream prior to their passage through the porous tube wall. Second, radial entry of the humidified flow accelerates the penetration of the vapor into the core of the tube, such that the maximal saturation ratio is reached within axial distances comparable with the tube radius. Third, the aerosol is partially confined within the tube core by the radially entering dry and humid gases. The particles then sample the relatively uniform saturation field near the tube axis, resulting in sharp responses of the activation probability versus particle diameter. Calculations with various combinations of axial and radial Reynolds numbers are carried out in search of conditions resulting in the smallest possible variation of the maximal supersaturation reached along the various flow streamlines.

An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks

Software-defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and provides programmable network management. The control plane within SDN is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts performance of SDN. In this work, we utilize clustering algorithms to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. Initially, using the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the optimal location for placing the controller within each cluster. We implement the proposed work in the Mininet emulator with POX as the SDN controller. Furthermore, we compare our proposed approach to existing work in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.

Performance Assessment of QoS metrics in Software Defined Networking using Floodlight Controller

The quality of service is not the same in all parts of the network. Some areas experience a low level and others a higher level of fixed quality services. The shortcomings in legacy networks encouraged researchers to find a new paradigm of the network to obviate legacy networks' deficiencies. The effort to create network services is called Quality of Service (QoS). Software-Defined Networking (SDN) focuses on separating the control layer from the data layer, and their communication is done through a central controller named SDN controller. After separation, the data layer moves the packets through the network according to the commands it receives from the controller. The controller obtains applications (QoS requests), translates them to low-level instructions, and implements them in the data layer. In this paper, we create an infrastructure for Quality of Service (QoS) in tree topology using a meter table per flow in Software Defined Networking Floodlight open-source controller. Meters are introduced into the OpenFlow protocol version 1.3, which calculates the packet rates allocated to them and allows control of those packet rates. Meters are directly connected to flow entry. Any flow entry can determine a meter in its command collection, which calculates and supervises the sum of all flow entries to which it is connected. When we get statistics from the meter table in each switch, we manage the network and affect the routing algorithms.

Rheological Study of the Formation of Pullulan Hydrogels and Their Use as Carvacrol-Loaded Nanoemulsion Delivery Systems.

Hydrogels have been extensively studied as delivery systems for lipophilic compounds. Pullulan hydrogels were prepared, and their gelation kinetics were studied over time. Pullulan exhibited a relatively slow gelling reaction in basic medium (KOH) using trisodium metaphosphate (STMP) as a cross-linking agent, so capsules cannot be obtained by dripping as easily as in the case of alginate and chitosan. The kinetics of pullulan gelation were studied through rheological analysis over time. An optimal [Pullulan]/[KOH] ratio was found for a fixed [Pullulan]/[STMP] ratio. For this given relationship, gelling time measurements indicated that when the concentration of pullulan increased, the gelation time decreased from 60 min for 6% w/w pullulan to 10 min for 10% w/w. After the gel point, a hardening of the hydrogel was observed over the next 5 h. The formed hydrogels presented high degrees of swelling (up to 1800%). Freeze-dried gels were capable of being rehydrated, obtaining gels with rheological characteristics and visual appearance similar to fresh gels, which makes them ideal to be freeze-dried for storage and rehydrated when needed. The behavior of the hydrogels obtained as active ingredient release systems was studied. In this case, the chosen molecule was carvacrol (the main component of oregano oil). As carvacrol is hydrophobic, it was incorporated into the droplets of an oil-in-water nanoemulsion, and the nanoemulsion was incorporated into the hydrogel. The release of the oil was studied at different pHs. It was observed that as the pH increased (from pH 2 to pH 7), the released amount of carvacrol for the gel with pullulan 10% w/w reached 100%; for the other cases, the cumulative release amount was lower. It was attributed to two opposite phenomena in the porous structure of the hydrogel, where more porosity implied a faster release of carvacrol but also a higher degree of swelling that promoted a higher entry of water flow in the opposite direction. This flow of water prevented the active principle from spreading to the release medium.

The Politics History of the Entry of Large Flows of Immigrants Originating from Islamic Countries to the Netherlands

This research aims to the Politics History of the Entry of Large Flows of Immigrants Originating from Islamic Countries to the Netherlands Entering the 1990s, the existence of immigrants from Islamic countries began to raise pros and cons. One of the causes of the bad image of Islam in the Netherlands was the assumption that Islam was a lowly religion and culture and was not on par with European culture. A Dutch politician, Frits Bolkestein, who stated that Islam is a religion that often abuses women, besides that governments in Islamic countries implement an authoritarian system and limit the freedom of the population to speak and express opinions. The approach used in writing this research is a qualitative approach and the research basis is a case study and the author uses a descriptive analytical research type. The result show the conflicts that emerged made the Dutch government increasingly worried about the security and social conditions in the country. That's why the government is increasingly tightening the rules for anyone who wants to come to the Netherlands, especially for those who want to settle down or find work there.

Web-Based Information System of Financial Report (Case Study: CV. Jaya PPK Abadi Surakarta)

Financial statements are the most crucial information in controlling all financial activities of a company. Financial statements become very important because as a form of accountability and describing the conditions and controlling tools of the company. CV. Jaya PPK Abadi Surakarta is an agency engaged in advertising. The agency has problems in terms of preparing financial statements which are still carried out by collecting sales notes and purchase notes, then the total in one day is recorded on the financial statement book. This of course causes problems, including in terms of the past data search process and the risk of lost notes. The process of calculating financial statements is still carried out using a calculator and calculating the difference between all sales and purchases in one day, it causes problems when there is a miscalculation because it uses manual methods. The absence of preparing monthly financial statements also makes it difficult for leaders to see the details of sales, purchases and net profit of the company. This, of course, has a bad impact on the development of the company. The purpose of this study is to create a financial statement information system in CV. Jaya PPK Abadi Surakarta. The author uses the SDLC (System Development Life Cycle) method in the process of developing PHP systems and programming languages and using MySQL databases. The system that the author designed is expected to produce an application that can record incoming and outgoing financial data on the CV. Jaya PPK Abadi and can be accessed via the web so that leaders can immediately find out the entry and exit of financial flows.

DDOS ATTACK DETECTION AGAINST SDN CONTROLLER USING A SINGLE TRAFFIC FEATURE

The most widely used southbound API of the software-defined network is the Open Flow protocol. Each flow in Open Flow has a set of packet-forwarding rules, which are referred to as flow entries. The switch processes packets in the SDN operation that meet the flow entries. The Packet that doesn't match any entries is transmitted as a Packet_in message to the Controller. Therefore, sending a lot of Packet in messages in a short amount of time could bring down the controller, and as a result, the entire network consequently resulting in to distributed denial of Service Attacks ( DDOS). This study uses the rate of Packet_in as a single feature, monitor, extract and utilize it to identify DDOS attacks in SDN using Random Fores classifier. The result shows 99.8% Accuracy which is slightly better than the work of [24] with 99.7%.

FEAROL: Aging Flow Entries Based on Local Staircase Randomized Response for Secure SDN Flow Tables

Software-Defined Networking (SDN) systems are sensitive to the lifespans of flow entries in flow tables as such lifespans affect the overall network-forwarding latency and the flow table space usage, particularly when the systems are under cyber attack. Instead of developing extra approaches to reactively detect the attacks and mitigate the impact of the attacks, this paper views the lifespans of flow entries as the privacy of the SDN systems and proposes a Flow-Entry Aging RandOmization Layer (FEAROL), which applies the staircase randomized response mechanism in the flow-entry-aging process at switches. FEAROL locally perturbs the lifespan of each flow entry in the flow table. Since the true lifespan of each flow entry is different from the lifespan set in the entry by the controller and dynamically perturbed by FEAROL, Low-rate Denial-of-Service (LDoS) attacks based on the sniffed flow-entry timeouts cannot be effectively organized. FEAROL proactively prevents LDoS attacks from overflowing the flow tables and legitimate flow packets from being dropped due to the broken synchronization between attack flows and the network settings. FEAROL can adjust its aging policies and privacy budget based on the real-time monitored network performance. FEAROL is prototyped in an open-source soft switch (OpenVSwitch) and evaluated through simulations on real network traces. The results show that FEAROL increases the overhead of aging a flow entry. However, this overhead can be significantly reduced by adjusting the interval at which the aging process is triggered. FEAROL also effectively defends against flow table overflow LDoS attacks by significantly reducing the table usage of LDoS attack flows. When the aging strategy is carefully chosen, the table space used by attack flow entries can be reduced to 0.

SFTO-Guard: Real-time detection and mitigation system for slow-rate flow table overflow attacks

The simplified data plane of Software-Defined Network (SDN) should be able to process packets from the entire network. However, the flow table size constrains the data plane forwarding capacity and may cause malicious attacks. In this paper, we study the slow-rate flow table overflow (SFTO) attack, which causes flow table overflow by sending unmatched packets at a slow rate to trigger flow entry installation, occupying the flow table space. To protect the availability of flow tables and the forwarding efficiency of normal flows, we propose a real-time SFTO attack detection and mitigation system based on rule number prediction and adaptive eviction proportion called SFTO-Guard. The SFTO-Guard consists of three modules: rule prediction module, attack detection module and attack mitigation module. Rule prediction module monitors the rule number in the flow tables and makes real-time predictions. When the predicted value reaches the attack threshold, the module collects the rules in flow tables and extracts features, then starts the attack detection module. When SFTO attack is detected, the attack mitigation module adaptively calculates the rule eviction proportion based on the predicted rule number and the attack detection results, and evicts suspected flow entries to prevent flow table overflow. Experiments on SFTO-Guard show that the proposed system can mitigate SFTO attacks effectively with low system overhead and short response time, it can limit malicious rules in flow tables to less than 10% and it is practicable in SDN deployments.

Enhancing Software-Defined Networks with Intelligent Controllers to Improve First Packet Processing Period

Software-Defined Networking (SDN) has a detailed central model that separates the data plane from the control plane. The SDN controller is in charge of monitoring network security and controlling data flow. OpenFlow-enabled routers and switches work as packet-forwarding devices in the network system. At first, OpenFlow forwarding devices like routers and switches do not know how to handle the data packets transmitted by the host. This is because they do not have any security controls, policies, or information. These packets are sent to their destination. In this situation, the OpenFlow forwarding device sends the first data packet of a host to the SDN controller, which checks the control packets for the data packet and creates flow entries in the switch flow table to act on the following categories of data packets coming from the host. These activities at the SDN controller and switch levels are time-intensive, and the first data packet from the host always takes a longer time to reach its destination. In this article, we suggest an SDN controller with instant flow entries (SDN-CIFE) to reduce the amount of time it takes for the host to transmit its first data packet. Before traffic comes from the host, our method adds the necessary flow entries to the flow table of the OpenFlow switch. The technique was made in Python and tested on a Mininet network emulator using the RYU controller. The results of the experiment show that the time it takes to process the first data packet is reduced by more than 83%.