Packet Flow Research Articles

Enhancement of energy efficiency using a transition state mac protocol for MANET

A mobile ad hoc network (MANET) is outlined as a network of mobile devices that can coordinate and communicate among themselves without any prior administration. Energy efficient lifetime maximizing methods based on channel awareness in MANETs result in better performance of the networks until the node's energy is capable of handling control messages. In the existing approach, the node's lifetime and consistency of packet flow are defaced due to unplanned energy conservation methods. This results in a tradeoff between network throughput and node energy, resulting in post network failure. The post network failure results in limited TTL of the nodes and retarded network throughput with higher control overhead. To bridge the gap between network throughput and energy conservation under limited overhead, a Transition State supporting cooperative MAC broadcast protocol for both conserving node energy and to utilize available node in an effective manner prior to their energy drain is proposed in this paper. TSMP reduces the total energy consumption to a maximum extent of 14–21% higher than DPCMP and 24–33% than SPCMP. And comparatively, the routing overhead falls almost 45–52% than SPCMP and 27–31% than DPCMP.

A Hybrid Approach for Providing Improved Link Connectivity in SDN

Software-Defined Networking (SDN) is a unique approach to design and build networks. The networks services can be better handled by administrators with the abstraction that SDN provides. The problem of re-routing the packets with minimum overhead in case of link failure is handled in this work. Protection and restoration schemes have been used in the past to handle such issues by giving more priority to minimal response time or controller overhead based on the use case. A hybrid scheme has been proposed with per-link Bidirectional forwarding mechanism to handle the failover. The proposed method makes sure that the controller overhead does not impact the flow of packets, thereby decreasing the overall response time, even with guaranteed network resiliency. The computation of the next shortest backup path also guarantees that the subsequent routing of packets always chooses the shortest path available. The proposed method is compared with the traditional approaches and proven by results to perform better with minimal response time.

Industrial Internet of Things Driven by SDN Platform for Smart Grid Resiliency

Software-defined networking (SDN) is a key enabling technology of industrial Internet of Things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g., failures or natural disturbances) seems to be fundamentally shifting energy attitude toward emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multifunctionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources, and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller through IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN-based IIoT solutions can improve grid reliability for enhancing smart grid resilience.

MODELLING SELF-SIMILAR TRAFFIC OF MULTISERVICE NETWORKS

Simulation modelling is carried out, which allows adequate describing the traffic of multiservice networks with the commutation of packets with the characteristic of burstiness. One of the most effective methods for studying the traffic of telecommunications systems is computer simulation modelling. By using the theory of queuing systems (QS), computer simulation modelling of packet flows (traffic) in modern multi-service networks is performed as a random self-similar process. Distribution laws such as exponential, Poisson and normal-logarithmic distributions, Pareto and Weibull distributions have been considered. The distribution of time intervals between arrivals of packages and the service duration of service of packages at different system loads has been studied. The research results show that the distribution function of time intervals between packet arrivals and the service duration of packages is in good agreement with the Pareto and Weibull distributions, but in most cases the Pareto distribution prevails. The queuing systems with the queues M/Pa/1 and Pa/M/1 has been studied, and the fractality of the intervals of requests arriving have been compared by the properties of the estimates of the system load and the service duration. It has been found out that in the system Pa/M/1, with the parameter of the form a> 2, the fractality of the intervals of requests arriving does not affect the average waiting time and load factor. However, when 𝑎≤2, as in the M/Pa/1 system, both considered statistical estimates differ. The application of adequate mathematical models of traffic allows to correctly assess the characteristics of the quality of service (QoS) of the network.

Stability of a standard decentralised medium access

We consider a stochastic queueing system modelling the behaviour of a wireless network with nodes employing a discrete-time version of the standard decentralised medium access algorithm. The system is unsaturated " each node receives an exogenous flow of packets at the rate ! packets per time slot. Each packet takes one slot to transmit, but neighbouring nodes cannot transmit simultaneously. The algorithm we study is standard in that: a node with empty queue does not compete for medium access; the access procedure by a node does not depend on its queue length, as long as it is non-zero. Two system topologies are considered, with nodes arranged in a circle and in a line. We prove that, for either topology, the system is stochastically stable under condition ! < 2/5. This result is intuitive for the circle topology as the throughput each node receives in a saturated system (with infinite queues) is equal to the so-called parking constant, which is larger than 2/5. (This fact, however, does not help to prove our result.) The result is not intuitive at all for the line topology as in a saturated system some nodes receive a throughput lower than 2/5.

Modelling and Simulation of a Physical Internet of Transportation Hubs

A Physical Internet of hubs (‘PI-hubs’) is a network of logistics terminals that allow shipments to flow through, similar to the flow of Internet data packets. PI-hub networks are a recent proposal and their properties have not been studied in detail. In this paper we model PI-hub networks using the theory of queuing networks, particularly, Jackson queuing networks. Under certain assumptions, PI-hub networks behave like Jackson queuing networks, and thus analytical methods can be used to calculate important properties such as throughputs, processing times, traffic size etc. In this paper we analyse the performance characteristics (throughput, queue size) for each PI-hub and for the whole PI-hub network.

Flow caching effectiveness in packet forwarding applications

Routing algorithms are known to be a potential bottleneck for packet processing. Network flow caching can function as a genneral acceleration technique for packet processing workloads. The goal of this article is to evaluate the effectiveness of packet flow caching techniques in high-speed networks. The area of focus is data distribution characteristics that lead to effectiveness of caching of network flows (connections). Based on statistical analysis and simulations the article sets necessary conditions for effective use of caches in packet forwarding applications. Public domain network traces were examined and measured for data locality. Software simulations show a strong correlation between flow packet distance metric and cache hit rate.

An Efficient Forensics Architecture in Software-Defined Networking-IoT Using Blockchain Technology

A Potential solution for solving forensic is the use of blockchain in software-defined networking (SDN). The blockchain is a distributed peer-to-peer network that can be utilized on SDN-based Internet of Things (IoT) environments for security provisioning. Hence, to meet some challenges in digital forensics such as data integrity, evidence deletion or alteration, blockchain is used. However, some problems such as poor attack detection and slow processing existed in previous works. To address these issues, an efficient forensics architecture is proposed in SDN-IoT that establishes the Chain of Custody (CoC) in blockchain technology. The proposed SDN-based IoT architecture is initiated with flow table rules on switches for the three different traffics Voice over Internet Protocol (VoIP), File Transfer Protocol (FTP), and Hyper Text Transfer Protocol (HTTP). In this work, overloaded switches migrate the packets to nearby switches to balance the packet flow. The packets disobeying flow rules will be discarded by switches. The blockchain-based distributed controller in this forensic architecture is designed to use the Linear Homomorphic Signature (LHS) algorithm for validating users. Each controller is fed with a classifier that uses the Neuro Multi-fuzzy to classify malicious packets based on packet features. The logs of events are used and stored on the blockchain in the proposed SDN-IoT architecture. We evaluated the performance of our forensic architecture and compared it to the existing model using various performance measures. Our evaluation results demonstrate performance improvement by reducing delay, response time and processing time, increasing throughput, accuracy, and security parameters.

Improvement of Anomaly Detection Performance Using Packet Flow Regularity in Industrial Control Networks

Improvement of Anomaly Detection Performance Using Packet Flow Regularity in Industrial Control Networks

Improvement and research of the method of balanced queue management on interfaces of telecommunication network routers

In the work the improvement of a method for balanced queue management on the interface of a telecommunication network router is proposed. The novelty of the proposed method is to provide a consistent solution to the problems of aggregation and distribution of packet flows among queues (Congestion Management tasks), as well as problems of balancing the allocation of the interface band-width among the class queues (Resource Allocation Tasks) in accordance with the principles of the Traffic Engineering Queues concept. Unlike the known approaches, the proposed solution has a positive effect on the computational complexity of the calculations, which is connected, first of all, with the decrease in the size of optimization problems and their linear nature. This was conditioned by the fact that the consistency of the Congestion Management and Resource Allocation processes was ensured on the basis of their agreed solution, since the decision on aggregation and distribution of flows among queues was not connected with the results of the solutions of the Resource Allocation problem. The study of Congestion Management and Resource Allocation processes using the proposed method has confirmed its efficiency and adequacy of the obtained solutions. The aggregation and distribution of packet flows in corresponding queues were carried out in accordance with the values of their classes. While the optimal balanced allocation of the interface bandwidth among the formed class queues was carried out in accordance with the principles of the concept of Traffic Engi-neering Queues - the queue with a higher value of the class had a lower utilization factor.

Anomaly Detection in Distributed Denial of Service Attack using Map Reduce Improvised counter-based algorithm in Hadoop

A Distributed Denial of Service (DDOS) is one of the major threats in the cyber network and it causes the computers flooded with the Users Datagram Packet (UDP).This type of attack crashes the victim with large volume of traffic and the victim is not capable of performing normal communication and crashes it completely. To handle this DDOS attack the normal Intrusion Detection System is not suitable to hold and find the amount of the data in the network. Hadoop is a frame work that allows huge amount of data and it is used to processes the huge amount of data. A Map reduce program comprises of a Map task that performs filtering and sorting and a Reduce task that performs summary operation. The propose work focuses on the detection algorithm based on Map Reduce platform which uses the Improvised counter based (MRICB) algorithm to detect the DDOS flooding attacks. The MRICB algorithm is implemented with Map Reduce functionalities at the stage of verifying the Network IPS. This proposed algorithm also focuses on the UDP flooding attack using anomaly based intrusion detection technique that identifies the kind of packets and the flow of packet in the node is more that the set threshold and also identifies the source code causing UDP Flood attack . Thus it ensures the normal communication with large volume of traffic.

Modified secure AODV protocol to prevent wormhole attack in MANET

SummaryAn Adhoc network is a combination of mobile nodes that works without centralized infrastructure. Every mobile node acts as host and routers. Furthermore, it also forward packets to other mobile nodes in network which are not within the direct transmission range. Mobile Ad hoc networks are easily exposed to various network layer attacks that include black hole, wormhole, and DOS attack. Wormhole attack is one among the severe attacks in MANET. Wormhole attacker receives the packets at any particular location in the network and disturbs the flow of packet by tunneling them to another location. In this paper, advanced mechanism is presented against these wormhole attacks in a MANET. Existing methods use Quality of Service (QoS) for entire network to detect attacks. Our method makes use of the packet delivery ratio and round trip time for each node, and it also detects active and passive attacks. Thus, the complete identification of wormhole attack is possible using the proposed method.

IEEE NetSoft 2017 special issue: Softwarization sustaining a hyper‐connected world: En route to 5G

IEEE NetSoft 2017 special issue: Softwarization sustaining a hyper‐connected world: En route to 5G

МОДЕЛЬ І АЛГОРИТМ НАВЧАННЯ ДЕТЕКТОРА ШКІДЛИВОГО ТРАФІКУ НА ОСНОВІ МОДИФІКАЦІЇ ЗРОСТАЮЧОГО НЕЙРОННОГО ГАЗУ

It is proposed the model of the hierarchical convolutional extractor of malware traffic features. Image with resolution 28x28 pixels and 10-th channels formed on the basis of successive 10 network packet flows is considered as model input. It allows to describe the spatial-temporal statistical characteristics of the traffic. The feature extractor consists of two convolutional layers with three-dimensional filters, sub-sampling layers, and activation calculation layers based on the orthogonal matching pursuit algorithm and the ReLU function. It is proposed the model of decision rules of the malware traffic detector based on information-extreme classifier. It allows to receive computatially simple decision rules and evaluate the informational efficiency of the feature extractor in the condition of the limited volume of the relevant labeled training dataset. The classifier performs an adaptive feature discretization and construction of the optimal in the information sense of radial-basis containers of classes in binary Hamming space. An information criterion of learning efficiency is the modification of S. Kulbak's measure as a function of the frequency of errors of the first and second type. Growing neural gas algorithm for pretraining of the feature extractor is improved by modifying the mechanism of insertion and updating of neurons. It allows utilizing unlabeled training samples and obtaining the optimal distribution of neurons to cover the training sample. Modification of the mechanism of insertion of new neurons is to form a new neuron at the reach of the threshold, and not with a given frequency. It allows you to improve the stability of the learning process and regulate the generalization ability of the model. The modification of the mechanism for updating the weighting coefficients of the neurons is to use the of Oja's rule instead of the Hebb's rule, which allows to avoid uncontrolled growth of neuron weights and adapts convolutional filters for sparse coding of input observation. It is proposed meta-heuristic search algorithm of simulated annealing for the training of decision rules and fine-tuning high-level filters of feature extractor. Simulation results using CTU-Mixed and CTU-13 datasets confirm the effectiveness of the resulting decision rules for recognizing the malware traffic from test samples

An Accurate and Efficient Modeling Framework for the Performance Evaluation of DPDK-Based Virtual Switches

Data plane development kit (DPDK) works as a specialized library that enables virtual switches to accelerate the processing of incoming packets by, among other things, balancing the incoming flow of packets over all the CPU cores and processing packets by batches to make a better use of the CPU cache. Although DPDK has become a de facto standard, the performance modeling of a DPDK-based vSwitch remains a challenging problem. In this paper, we present an analytical queueing model to evaluate the performance of a DPDK-based vSwitch. Such a virtual equipment is represented by a complex polling system in which packets are processed by batches, i.e., a given CPU core processes several packets of one of its attached input queues before switching to the next one. To reduce the complexity of the associated model, we develop a general framework that consists in decoupling the polling system into several queueing subsystems, each one corresponding to a given CPU core. We resort to servers with vacation to capture the interactions between subsystems. Our proposed solution is conceptually simple, easy to implement and computationally efficient. Tens of comparisons against a discrete-event simulator show that our models typically deliver accurate estimates of the performance parameters of interest (e.g., attained throughput, packet latency or loss rate). We illustrate how our models can help in determining an adequate setting of the vSwitch parameters using several real-life case studies.

Dimensioning of store-and-transfer WDM networks with stratified ROADM node

The so called Store-and-Transfer WDM Network (STWN) can store data in source storage and provision lightpaths at an optimal time when wavelengths are clear of conflicts. Consequently, blocking of requests can be decreased and resource utilization of the network can be improved. If traffic can be classified into delay-sensitive packet flows and deadline-constrained large data transfers, we can use a stratified node architecture to reduce cost of STWN node by reducing port count of optical and electronic switches. In this work, we investigate dimensioning of a network of flexible grid reconfigurable optical add/drop multiplexers and compare the result with that obtain by dimensioning of STWN. We propose a two-step method to determine the amount of spectrum required to satisfy the demand of large data transfers, which is given as a load matrix with a deadline and blocking rate. We also obtain the overall utilization over all links. The method firstly uses Markov chain to model variable bandwidth allocation over a lightpath and searches for amount of required spectrum of all source-destination pairs, and secondly optimizes routing and spectrum assignment for the matrix of amount of required spectrum to minimize maximum amount of spectrum used over each link. Numerical results show the following with all numbers given as absolute values: If bandwidth allocation method Min is used, the utilization can reach 0.7 and the amount of required spectrum is 30% more than that required by STWN. If bandwidth allocation method Dyna is used, the utilization can reach 0.9 and the amount of required spectrum is similar to that required by STWN. Also, under the same load, if blocking increases from 0.001 to 0.01, the amount of required spectrum decreases and the utilization increases 0.08, which denotes over-provisioning of spectrum and allowing preemption of spectrum during hours of peak traffic can effectively decrease blocking.

Visual-MIMO for Software-Defined Vehicular Networks

The paradigm of software-defined network (SDN) is being applied to vehicle scenarios in order to eliminate this heterogeneity of vehicular network infrastructure and to manage packet flow in an application- and user-centrically flexible and efficient manner. However, owing to the random mobility of vehicles and the unpredictable road communication environment, efficient vehicle-based SDN development needs further research. In this study, we propose the concept of a sub-control plane for supporting and backing up, at the data plane level, various functions of the control plane, which plays a key role in SDN. The sub-control plane can be intuitively understood through the image processing techniques used in color-independent visual-MIMO (multiple input multiple output) networking, and the function of the control plane can be backed up through various vehicle-based recognition and tracking algorithms under the situation of disconnection between the data plane and the control plane. The proposed sub-control plane is expected to facilitate efficient management of the software-defined vehicular network (SDVN) and improve vehicular communication performance and service quality.

Towards Robust Admission Control in Delay-Constrained Routing Problems

Delay-Constrained Routing (DCR) problems require to route a new flow in a computer network subject to worst-case end-to-end delay guarantees. The delay of a packet flow has three components, one of which is the “queueing delay”, that depends on the scheduling algorithm implemented by the routers of the network. When flows are not independent of each other, i.e., admitting a new flow changes the delay of the existing ones, admission control policies are necessary to ensure that existing flows do not become latency-unfeasible. It has been recently shown that admission control runs contrary to the usual objective function employed in these models, i.e., minimization of the reserved rates, significantly worsening network performance. In this paper we investigate the phenomenon and propose a heuristic way to overcome the problem.

Content-Aware Delivery of Scalable Video in Network Coding Enabled Named Data Networks

We propose a novel network coding (NC) enabled named data networking (NDN) architecture for scalable video delivery. Our architecture utilizes NC in order to address the problem that arises in the original NDN architecture, where optimal use of the bandwidth and caching resources necessitates the coordination of the Interest forwarding decisions. To optimize the performance of the proposed NC-based NDN architecture and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interests sent by clients and intermediate nodes. The flow of Data packets achieved by this algorithm maximizes the average quality of the video delivered to the client population. To support the handling of Interest and Data packets when intermediate nodes perform NC, we introduce the use of Bloom filters, which store efficiently additional information about the Interest and Data packets, and modify accordingly the standard NDN architecture. We also devise an optimized Interest forwarding strategy that implements the target rate allocation. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme exploits optimally the available network resources.

Stability conditions for a discrete-time decentralised medium access algorithm

We consider a stochastic queueing system modelling the behaviour of a wireless network with nodes employing a discrete-time version of the standard decentralised medium access algorithm. The system is unsaturated—each node receives an exogenous flow of packets at the rate of $\lambda$ packets per time slot. Each packet takes one slot to transmit, but neighbouring nodes cannot transmit simultaneously. The algorithm we study is standard in the following sense: a node with an empty queue does not compete for medium access; the access procedure by a node does not depend on its queue length as long as it is nonzero. Two system topologies are considered, with nodes arranged in a circle and in a line. We prove that, for either topology, the system is stochastically stable under the condition $\lambda<2/5$. This result is intuitive for the circle topology as the throughput each node receives in the saturated system (with infinite queues) is equal to the so-called parking constant, which is larger than $2/5$. (This fact, however, does not help us to prove the result.) The result is not intuitive for the line topology as in the saturated system some nodes receive a throughput lower than $2/5$.