scholarly journals Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 604 ◽  
Author(s):  
Rashid Amin ◽  
Nadir Shah ◽  
Waqar Mehmood
Keyword(s):  
Decision Tree ◽  
Policy Implementation ◽  
Network Architecture ◽  
Edge Weight ◽  
Network Congestion ◽  
Trade Offs ◽  
Minimum Number ◽  
And Control ◽  
Unwanted Traffic ◽  
Hybrid Sdn

Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Design and Control of Drones

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mark W. Mueller ◽  
Seung Jae Lee ◽  
Raffaello D’Andrea
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Recent Progress ◽  
Scaling Laws ◽  
Autonomous Systems ◽  
Annual Review ◽  
Publication Date ◽  
Trade Offs ◽  
Active Research ◽  
And Control

The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Neural network architecture for adaptive system modeling and control

1991 ◽  
Vol 4 (2) ◽  
pp. 185-191 ◽  
Author(s):  
Esther Levin ◽  
Raanan Gewirtzman ◽  
Gideon F. Inbar
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Adaptive System ◽  
System Modeling ◽  
Neural Network Architecture ◽  
Modeling And Control ◽  
And Control

scholarly journals Crafting Efficient Neural Graph of Large Entropy

2019 ◽  
Author(s):  
Minjing Dong ◽  
Hanting Chen ◽  
Yunhe Wang ◽  
Chang Xu
Keyword(s):  
Information Flow ◽  
Network Architecture ◽  
High Performance ◽  
Global Information ◽  
High Quality ◽  
Network Pruning ◽  
Initial Network ◽  
Trade Offs ◽  
Graph Properties ◽  
Deep Cnn

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.

scholarly journals Analysis and Control of Autonomous Mobility-on-Demand Systems

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Gioele Zardini ◽  
Nicolas Lanzetti ◽  
Marco Pavone ◽  
Emilio Frazzoli
Keyword(s):  
Performance Metrics ◽  
Transportation Systems ◽  
Demand Systems ◽  
Annual Review ◽  
Publication Date ◽  
Systematic Analysis ◽  
On Demand ◽  
Trade Offs ◽  
Autonomous Mobility ◽  
And Control

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The European Parliament and the Implementation of EU Environmental Policies: the Case of the Spanish Water Plan

2005 ◽  
Vol 2 (3) ◽  
pp. 204-214
Author(s):  
Jan Ceyssens
Keyword(s):  
Policy Implementation ◽  
Environmental Law ◽  
Control Policy ◽  
Environmental Policies ◽  
European Parliament ◽  
Democratic Accountability ◽  
Eu Law ◽  
Effective Implementation ◽  
Eu Directives ◽  
And Control

AbstractThis article examines the European Parliament's ability to scrutinize and control the implementation of EU Environmental law by national authorities, taking as an example the Spanish Water Plan - a major infrastructure plan which allegedly infringed several EU Directives and was ultimately abandoned last year. The article gives an overview of the European Parliament's main powers to scrutinize and control policy implementation, and analyses how Members of the European Parliament used them to control the implementation of EU Environmental law in the case of the Spanish Water Plan. It concludes that the Parliament's activities contributed to ensuring the effective implementation of EU law and thus to a sensible enhancement of democratic accountability in this area.

Unwanted Traffic Detection and Control Based on Trust Management

2016 ◽  
pp. 77-109 ◽  
Author(s):  
Zheng Yan ◽  
Raimo Kantola ◽  
Lifang Zhang ◽  
Yutan Ma
Keyword(s):  
Trust Management ◽  
And Control ◽  
Traffic Detection ◽  
Unwanted Traffic

scholarly journals Stability of Optimal Closed-Loop Cleaning Scheduling and Control with Application to Heat Exchanger Networks under Fouling

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1623
Author(s):  
Federico Lozano Santamaria ◽  
Sandro Macchietto
Keyword(s):  
Heat Exchanger ◽  
Closed Loop ◽  
Operating Cost ◽  
Optimization Procedure ◽  
Loop Model ◽  
Loop Scheduling ◽  
Loop Optimization ◽  
Trade Offs ◽  
Heat Exchanger Networks ◽  
And Control

Heat exchanger networks subject to fouling are an important example of dynamic systems where performance deteriorates over time. To mitigate fouling and recover performance, cleanings of the exchangers are scheduled and control actions applied. Because of inaccuracy in the models, as well as uncertainty and variability in the operations, both schedule and controls often have to be revised to improve operations or just to ensure feasibility. A closed-loop nonlinear model predictive control (NMPC) approach had been previously developed to simultaneously optimize the cleaning schedule and the flow distribution for refinery preheat trains under fouling, considering their variability. However, the closed-loop scheduling stability of the scheme has not been analyzed. For practical closed-loop (online) scheduling applications, a balance is usually desired between reactivity (ensuring a rapid response to changes in conditions) and stability (avoiding too many large or frequent schedule changes). In this paper, metrics to quantify closed-loop scheduling stability (e.g., changes in task allocation or starting time) are developed and then included in the online optimization procedure. Three alternative formulations to directly include stability considerations in the closed-loop optimization are proposed and applied to two case studies, an illustrative one and an industrial one based on a refinery preheat train. Results demonstrate the applicability of the stability metrics developed and the ability of the closed-loop optimization to exploit trade-offs between stability and performance. For the heat exchanger networks under fouling considered, it is shown that the approach proposed can improve closed-loop schedule stability without significantly compromising the operating cost. The approach presented offers the blueprint for a more general application to closed-loop, model-based optimization of scheduling and control in other processes.

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