779 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments
Despite the impressive progress of telepresence systems for room-scale scenes
with static and dynamic scene entities, expanding their capabilities to
scenarios with larger dynamic environments beyond a fixed size of a few
square-meters remains challenging.
In this paper, we aim at sharing 3D live-telepresence experiences in
large-scale environments beyond room scale with both static and dynamic scene
entities at practical bandwidth requirements only based on light-weight scene
capture with a single moving consumer-grade RGB-D camera. To this end, we
present a system which is built upon a novel hybrid volumetric scene
representation in terms of the combination of a voxel-based scene
representation for the static contents, that not only stores the reconstructed
surface geometry but also contains information about the object semantics as
well as their accumulated dynamic movement over time, and a point-cloud-based
representation for dynamic scene parts, where the respective separation from
static parts is achieved based on semantic and instance information extracted
for the input frames. With an independent yet simultaneous streaming of both
static and dynamic content, where we seamlessly integrate potentially moving
but currently static scene entities in the static model until they are becoming
dynamic again, as well as the fusion of static and dynamic data at the remote
client, our system is able to achieve VR-based live-telepresence at close to
real-time rates. Our evaluation demonstrates the potential of our novel
approach in terms of visual quality, performance, and ablation studies
regarding involved design choices
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
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
Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology
L'abstract è presente nell'allegato / the abstract is in the attachmen
Content-aware QoE Optimization in MEC-assisted Mobile Video Streaming
The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm
Ensembles of Pruned Deep Neural Networks for Accurate and Privacy Preservation in IoT Applications
The emergence of the AIoT (Artificial Intelligence of Things) represents the powerful convergence of Artificial Intelligence (AI) with the expansive realm of the Internet of Things (IoT). By integrating AI algorithms with the vast network of interconnected IoT devices, we open new doors for intelligent decision-making and edge data analysis, transforming various domains from healthcare and transportation to agriculture and smart cities.
However, this integration raises pivotal questions: How can we ensure deep learning models are aptly compressed and quantised to operate seamlessly on devices constrained by computational resources, without compromising accuracy? How can these models be effectively tailored to cope with the challenges of statistical heterogeneity and the uneven distribution of class labels inherent in IoT applications? Furthermore, in an age where data is a currency, how do we uphold the sanctity of privacy for the sensitive data that IoT devices incessantly generate while also ensuring the unhampered deployment of these advanced deep learning models?
Addressing these intricate challenges forms the crux of this thesis, with its contributions delineated as follows:
Ensyth: A novel approach designed to synthesise pruned ensembles of deep learning models, which not only makes optimal use of limited IoT resources but also ensures a notable boost in predictability. Experimental evidence gathered from CIFAR-10, CIFAR-5, and MNIST-FASHION datasets solidify its merit, especially given its capacity to achieve high predictability.
MicroNets: Venturing into the realms of efficiency, this is a multi-phase pruning pipeline that fuses the principles of weight pruning, channel pruning. Its objective is clear: foster efficient deep ensemble learning, specially crafted for IoT devices. Benchmark tests conducted on CIFAR-10 and CIFAR-100 datasets demonstrate its prowess, highlighting a compression ratio of nearly 92%, with these pruned ensembles surpassing the accuracy metrics set by conventional models.
FedNets: Recognising the challenges of statistical heterogeneity in federated learning and the ever-growing concerns of data privacy, this innovative federated learning framework is introduced. It facilitates edge devices in their collaborative quest to train ensembles of pruned deep neural networks. More than just training, it ensures data privacy remains uncompromised. Evaluations conducted on the Federated CIFAR-100 dataset offer a testament to its efficacy.
In this thesis, substantial contributions have been made to the AIoT application domain. Ensyth, MicroNets, and FedNets collaboratively tackle the challenges of efficiency, accuracy, statistical heterogeneity arising from distributed class labels, and privacy concerns inherent in deploying AI applications on IoT devices. The experimental results underscore the effectiveness of these approaches, paving the way for their practical implementation in real-world scenarios. By offering an integrated solution that satisfies multiple key requirements simultaneously, this research brings us closer to the realisation of effective and privacy-preserved AIoT systems
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