426 research outputs found
Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks
Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams.
This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness
Optimizing Flow Routing Using Network Performance Analysis
Relevant conferences were attended at which work was often presented and several papers were published in the course of this project.
• Muna Al-Saadi, Bogdan V Ghita, Stavros Shiaeles, Panagiotis Sarigiannidis. A novel approach for performance-based clustering and management of network traffic flows, IWCMC, ©2019 IEEE.
• M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: Unsupervised Machine Learning-Based Elephant and Mice Flow Identification, Computing Conference 2021.
• M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning, Network, 3(1), pp.218-238, 2023.The main task of a network is to hold and transfer data between its nodes. To achieve this task, the network needs to find the optimal route for data to travel by employing a particular routing system. This system has a specific job that examines each possible path for data and chooses the suitable one and transmit the data packets where it needs to go as fast as possible. In addition, it contributes to enhance the performance of network as optimal routing algorithm helps to run network efficiently. The clear performance advantage that provides by routing procedures is the faster data access. For example, the routing algorithm take a decision that determine the best route based on the location where the data is stored and the destination device that is asking for it. On the other hand, a network can handle many types of traffic simultaneously, but it cannot exceed the bandwidth allowed as the maximum data rate that the network can transmit. However, the overloading problem are real and still exist. To avoid this problem, the network chooses the route based on the available bandwidth space. One serious problem in the network is network link congestion and disparate load caused by elephant flows. Through forwarding elephant flows, network links will be congested with data packets causing transmission collision, congestion network, and delay in transmission. Consequently, there is not enough bandwidth for mice flows, which causes the problem of transmission delay.
Traffic engineering (TE) is a network application that concerns with measuring and managing network traffic and designing feasible routing mechanisms to guide the traffic of the network for improving the utilization of network resources. The main function of traffic engineering is finding an obvious route to achieve the bandwidth requirements of the network consequently optimizing the network performance [1]. Routing optimization has a key role in traffic engineering by finding efficient routes to achieve the desired performance of the network [2]. Furthermore, routing optimization can be considered as one of the primary goals in the field of networks. In particular, this goal is directly related to traffic engineering, as it is based on one particular idea: to achieve that traffic is routed according to accurate traffic requirements [3]. Therefore, we can say that traffic engineering is one of the applications of multiple improvements to routing; routing can also be optimized based on other factors (not just on traffic requirements). In addition, these traffic requirements are variable depending on analyzed dataset that considered if it is data or traffic control. In this regard, the logical central view of the Software Defined Network (SDN) controller facilitates many aspects compared to traditional routing. The main challenge in all network types is performance optimization, but the situation is different in SDN because the technique is changed from distributed approach to a centralized one. The characteristics of SDN such as centralized control and programmability make the possibility of performing not only routing in traditional distributed manner but also routing in centralized manner. The first advantage of centralized routing using SDN is the existence of a path to exchange information between the controller and infrastructure devices. Consequently, the controller has the information for the entire network, flexible routing can be achieved. The second advantage is related to dynamical control of routing due to the capability of each device to change its configuration based on the controller commands [4].
This thesis begins with a wide review of the importance of network performance analysis and its role for understanding network behavior, and how it contributes to improve the performance of the network. Furthermore, it clarifies the existing solutions of network performance optimization using machine learning (ML) techniques in traditional networks and SDN environment. In addition, it highlights recent and ongoing studies of the problem of unfair use of network resources by a particular flow (elephant flow) and the possible solutions to solve this problem. Existing solutions are predominantly, flow routing-based and do not consider the relationship between network performance analysis and flow characterization and how to take advantage of it to optimize flow routing by finding the convenient path for each type of flow. Therefore, attention is given to find a method that may describe the flow based on network performance analysis and how to utilize this method for managing network performance efficiently and find the possible integration for the traffic controlling in SDN. To this purpose, characteristics of network flows is identified as a mechanism which may give insight into the diversity in flow features based on performance metrics and provide the possibility of traffic engineering enhancement using SDN environment. Two different feature sets with respect to network performance metrics are employed to characterize network traffic. Applying unsupervised machine learning techniques including Principal Component Analysis (PCA) and k-means cluster analysis to derive a traffic performance-based clustering model. Afterward, thresholding-based flow identification paradigm has been built using pre-defined parameters and thresholds. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding the best flow routing based on the type of flow, to be evaluated against a number of traffic data sources and different performance experiments. The validation process of the novel framework performance has been done by making a performance comparison between SDN-Ryu controller and the proposed SDN-external application based on three factors: throughput, bandwidth,and data transfer rate by conducting two experiments. Furthermore, the proposed method has been validated by using different Data Centre Network (DCN) topologies to demonstrate the effectiveness of the network traffic management solution. The overall validation metrics shows real gains, the results show that 70% of the time, it has high performance with different flows. The proposed routing SDN traffic-engineering paradigm for a particular flow therefore, dynamically provisions network resources among different flow types
Increasing the Reliability of Power and Communication Networks via Robust Optimization
Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability.
Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms.
This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints.
Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures.
We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Supporting heterogenous traffic on top of point-to-multipoint light-trees †
† This manuscript is the extension of the conference paper: Iqbal, M.; Ruiz, M.; Costa, N.; Napoli, A.; Pedro, J.; Velasco, L. Dynamic and Efficient Point-to-Point and Point-to-Multipoint Communications by Slicing the Optical Constellation. In Proceedings of the Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 6–10 March 2022.New 5 G and beyond services demand innovative solutions in optical transport to increase efficiency and flexibility and reduce capital (CAPEX) and operational (OPEX) expenditures to support heterogeneous and dynamic traffic. In this context, optical point-to-multipoint (P2MP) connectivity is seen as an alternative to provide connectivity to multiple sites from a single source, thus potentially both reducing CAPEX and OPEX. Digital subcarrier multiplexing (DSCM) has been shown as a feasible candidate for optical P2MP in view of its ability to generate multiple subcarriers (SC) in the frequency domain that can be used to serve several destinations. This paper proposes a different technology, named optical constellation slicing (OCS), that enables a source to communicate with multiple destinations by focusing on the time domain. OCS is described in detail and compared to DSCM by simulation, where the results show that both OCS and DSCM provide a good performance in terms of the bit error rate (BER) for access/metro applications. An exhaustive quantitative study is afterwards carried out to compare OCS and DSCM considering its support to dynamic packet layer P2P traffic only and mixed P2P and P2MP traffic; throughput, efficiency, and cost are used here as the metrics. As a baseline for comparison, the traditional optical P2P solution is also considered in this study. Numerical results show that OCS and DSCM provide a better efficiency and cost savings than traditional optical P2P connectivity. For P2P only traffic, OCS and DSCM are utmost 14.6% more efficient than the traditional lightpath solution, whereas for heterogeneous P2P + P2MP traffic, a 25% efficiency improvement is achieved, making OCS 12% more efficient than DSCM. Interestingly, the results show that for P2P only traffic, DSCM provides more savings of up to 12% than OCS, whereas for heterogeneous traffic, OCS can save up to 24.6% more than DSCM.This research was funded by European Commission through the H2020 MSCA REAL-NET (G.A. 813144) and the HORIZON ALLEGRO (G.A. 101092766) projects, by the AEI through the IBON project (PID2020-114135RB-I00), and by the ICREA institution.Peer ReviewedPostprint (published version
Towards Zero Touch Next Generation Network Management
The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques
VISUAL MODELING AND SIMULATION OF CRYPTOGRAPHIC PROTOCOLS UNDER CONTESTED ENVIRONMENTS
With an ever-evolving battlefield in cyberspace, it is essential to stay abreast of current and developing security protocols that will maintain a state of authenticity, confidentiality, and integrity between communicating entities in information-contested environments. The Department of Defense is interested in transitioning its mission objective goals to establishing and maintaining a reliable security posture between communicating command-and-control platforms. However, the current security protocol visualizations need to cater more to military users and decision-makers to help decide which security protocols would best accommodate various operational environments. This research designed and developed a two-dimensional protocol visualization tool (ProVis) that simulates various security protocol interactions in non-contested and contested operational study environments that assist with understanding how security protocols work in the context of military-related usage. A user study was conducted to examine users’ understanding, accuracy, and overall benefit of ProVis concerning the visualization of the Transport Layer Security, Message Layer Security, and Pre-Shared Key protocols. The findings are highly satisfactory: the user subjects were able to easily interface with ProVis and complete the tasks given. The user subjects found ProVis to be a helpful tool in understanding security protocols quickly. This research provides an alternative to current visualization tools.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Outstanding ThesisLieutenant Commander, United States NavyApproved for public release. Distribution is unlimited
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