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نتیجه جستجو - Data networks

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
دو دهه AI4NETS - AI / ML برای شبکه های داده: چالش ها و دستورالعمل های تحقیق-2020
The popularity of Artificial Intelligence (AI) – and of Machine Learning (ML) as an approach to AI, has dramatically increased in the last few years, due to its outstanding performance in various domains, notably in image, audio, and natural language processing. In these domains, AI success-stories are boosting the applied field. When it comes to AI/ML for data communication Networks (AI4NETS), and despite the many attempts to turn networks into learning agents, the successful application of AI/ML in networking is limited. There is a strong resistance against AI/ML-based solutions, and a striking gap between the extensive academic research and the actual deployments of such AI/ML-based systems in operational environments. The truth is, there are still many unsolved complex challenges associated to the analysis of networking data through AI/ML, which hinders its acceptability and adoption in the practice. In this positioning paper I elaborate on the most important show-stoppers in AI4NETS, and present a research agenda to tackle some of these challenges, enabling a natural adoption of AI/ML for networking. In particular, I focus the future research in AI4NETS around three major pillars: (i) to make AI/ML immediately applicable in networking problems through the concepts of effective learning, turning it into a useful and reliable way to deal with complex data-driven networking problems; (ii) to boost the adoption of AI/ML at the large scale by learning from the Internet-paradigm itself, conceiving novel distributed and hierarchical learning approaches mimicking the distributed topological principles and operation of the Internet itself; and (iii) to exploit the softwarization and distribution of networks to conceive AI/ML-defined Networks (AIDN), relying on the distributed generation and re-usage of knowledge through novel Knowledge Delivery Networks (KDNs).
Index Terms: Machine Learning | Artificial Intelligence | Data Communication Networks | Data-driven networking | Knowledge Delivery Networks (KDNs) | AI/ML-defined networking (AIDN)
مقاله انگلیسی
2 Application of deep reinforcement learning to intrusion detection for supervised problems
کاربرد یادگیری تقویتی عمیق برای تشخیص نفوذ برای مسائل تحت نظارت-2020
The application of new techniques to increase the performance of intrusion detection systems is crucial in modern data networks with a growing threat of cyber-attacks. These attacks impose a greater risk on network services that are increasingly important from a social end economical point of view. In this work we present a novel application of several deep reinforcement learning (DRL) algorithms to intru- sion detection using a labeled dataset. We present how to perform supervised learning based on a DRL framework. The implementation of a reward function aligned with the detection of intrusions is extremely diffi- cult for Intrusion Detection Systems (IDS) since there is no automatic way to identify intrusions. Usually the identification is performed manually and stored in datasets of network features associated with in- trusion events. These datasets are used to train supervised machine learning algorithms for classifying intrusion events. In this paper we apply DRL using two of these datasets: NSL-KDD and AWID datasets. As a novel approach, we have made a conceptual modification of the classic DRL paradigm (based on interaction with a live environment), replacing the environment with a sampling function of recorded training intrusions. This new pseudo-environment, in addition to sampling the training dataset, generates rewards based on detection errors found during training. We present the results of applying our technique to four of the most relevant DRL models: Deep Q- Network (DQN), Double Deep Q-Network (DDQN), Policy Gradient (PG) and Actor-Critic (AC). The best results are obtained for the DDQN algorithm. We show that DRL, with our model and some parameter adjustments, can improve the results of intrusion detection in comparison with current machine learning techniques. Besides, the classifier ob- tained with DRL is faster than alternative models. A comprehensive comparison of the results obtained with other machine learning models is provided for the AWID and NSL-KDD datasets, together with the lessons learned from the application of several design alternatives to the four DRL models.
Keywords: Intrusion detection | Data networks | Deep reinforcement learning
مقاله انگلیسی
3 Greening big data networks: velocity impact
گرمایش شبکه های داده های بزرگ: تاثیر سرعت-2018
The authors investigate the impact of big datas velocity on greening IP over WDM networks. They classify the processing velocity of big data into two modes: expedited-data and relaxed-data modes. Expedited-data demands higher amount of computational resources to reduce the execution time compared with the relaxed-data. They developed a mixed integer linear programming model to progressively process big data at strategic locations, dubbed processing nodes (PNs), built into the network along the path from the source to the destination. The extracted information from the raw traffic is smaller in volume compared with the original traffic each time the data is processed, hence, reducing network power consumption. The results showed that up to 60% network power saving is achieved when nearly 100% of the data required relaxed processing. In contrast, only 15% of network power saving is gained when nearly 100% of the data required expedited processing. The authors obtained around 33% power saving in the mixed modes (i.e. when ∼50% of the data is processed in the relaxed mode and 50% of the data is processed in expedited mode), compared with the classical approach where all the processing is achieved inside the centralised data centres only.
مقاله انگلیسی
4 Simulation methodology and performance analysis of network coding based transport protocol in wireless big data networks
روش شبیه سازی و تجزیه و تحلیل کارایی پروتکل انتقال مبتنی بر کدگذاری شبکه در شبکه های داده های بزرگ بی سیم-2018
The Multi-Path, Multi-Hop (MPMH) communications have been extensively used in wire less network. It is especially suitable to big data transmissions due to its high throughput. To provide congestion and end-to-end reliability control, two types of transport layer pro tocols have been proposed in the literature: the TCP-based protocols and the rateless cod ing based protocols. However, the former is too conservative to explore the capacity of the MPMH networks, and the latter is too aggressive in filling up the communication capac ity and performs poorly when dealing with congestions. To overcome their drawbacks, a novel network coding scheme, namely, Adjustable Batching Coding (ABC), was proposed by us, which uses redundancy coding to overcome random loss and uses retransmissions and window size shrink to relieve congestion. The stratified congestion control strategy makes the ABC scheme especially suitable for big data transmissions. However, there is no simu lation platform built so far that can accurately test the performance of the network coding based transport protocols. We have built a modular, easy-to-customize simulation system based on an event-based programming method, which can simulate the ABC-based MPMH transport layer behaviors. Using the proposed simulator, the optimal parameters of the protocol can be fine-tuned, and the performance is superior to other transport layer pro tocols under the same settings. Furthermore, the proposed simulation methodology can be easily extended to other variants of MPMH communication systems by adjusting the ABC parameters.
Keywords: Network simulator ، Wireless big data networks ، Multi-path multi-hop communications ، Transport layer ، Network coding
مقاله انگلیسی
5 Exploiting Industrial Big Data Strategy for Load Balancing in Industrial Wireless Mobile Networks
استراتژی بهره برداری از داده های بزرگ صنعتی برای تعادل بار در شبکه های موبایل بی سیم صنعتی-2018
In the era of big data, traditional industrial mobile wireless networks cannot effectively handle the new requirements of mobile wireless big data networks arising from the spatio-temporal changes of a nodes traffic load. From the perspective of load balancing and energy efficiency, industrial big data (IBD) brings new transmission challenges to industrial wireless mobile networks (IWMNs). Previous research works have not considered dynamic changes related to the traffic and mobility of IWMNs. In this paper, using an IBD technique, we propose a novel second-deployment and sleep-scheduling strategy (SDSS) for balancing load and increasing energy efficiency, while taking the dynamic nature of the network into consideration. SDSS can be divided into two stages. In the first stage, changes in the traffic of every network grid and its maximum traffic load at different times are calculated using big data analysis techniques. In the second stage, a second-deployment method for the cluster head nodes (CHNs), based on each grids maximum traffic load, is adopted. To save energy, based on their position and traffic states, a sleep-wake scheduling is presented for the CHNs. Simulations results verify the effectiveness of this methodology to save energy and obtain a traffic balance, which is more efficient than obtained through traditional methods.
INDEX TERMS: Load balancing, energy efficiency, sleep scheduling, second-deployment, industrial wireless networks, big data networks
مقاله انگلیسی
6 Bridge the Gap Between ADMM and Stackelberg Game: Incentive Mechanism Design for Big Data Networks
پل شکاف بین ADMM و Stackelberg بازی: طراحی مکانیسم انگیزه برای شبکه های داده های بزرگ-2017
Alternating direction method of multipliers (ADMM) has been well recognized as an efficient optimization approach due to its fast convergence speed and variable decomposition property. However, in big data networks, the agents may not feedback the variables as the centralized controller expects. In this paper, we model the problem as a Stackelberg game and design a Stackelberg game based ADMM to deal with the contradiction between the centralized objective of the controller and the individual objectives from the agents. The Stackelberg game based ADMM can converge linearly, which is not dependent on the number of agents. The case study verifies the fast convergence of our game-based incentive mechanism.
Index Terms: ADMM | big data | game theory | large-scale network
مقاله انگلیسی
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