Computer Networks and Communications, Computer Science, Artificial Intelligence
131
Scopus Publications
7277
Scholar Citations
35
Scholar h-index
96
Scholar i10-index
Scopus Publications
Traffic-Aware Design for Multi-dimensional Lookup and Forwarding: From IP Routing to Packet Classification Xinyi Zhang, Qianrui Qiu, Zhiyuan Xu, Peng He, Guangxing Zhang, Luyiyun Li, Jianer Zhou, Kavé Salamatian, Gaogang Xie IEEE Transactions on Networking, 2026 Packet processing in modern routers and switches relies on rule matching, primarily performed by two core modules: IP prefix lookup for next-hop determination and packet classification for multi-field policy enforcement. However, most existing algorithms are rule-centric and assume uniform rule access, overlooking the highly skewed nature of real-world network traffic. Such mismatch between static rule organization and dynamic traffic behavior leads to inefficiency in both lookup and classification. To address this limitation, we propose a Traffic-aware Lookup and Forwarding (TLF) framework that leverages traffic measurement with lookup operations, enabling online adaptation to dynamic traffic patterns and frequent rule updates. Experimental results demonstrate that TLF provides 1.04×–3.37× speedups for lookup and forwarding over state-of-the-art algorithms, while substantially reducing both memory overhead and construction time. Furthermore, integrating TLF into Vector Packet Processor (VPP) and Open vSwitch (OVS) results in throughput improvements of 2.61× and 4.88×, respectively.
NPC: Rethinking Dataplane through Network-aware Packet Classification Xinyi Zhang, Qianrui Qiu, Zhiyuan Xu, Peng He, Xilai Liu, Kavé Salamatian, Changhua Pei, Gaogang Xie SIGCOMM 2025 ACM SIGCOMM 2025 Conference, 2025 Packet classification is a critical component for accurately categorizing traffic in network systems. The efficiency of packet classification algorithms is primarily determined by two key factors: the classifier's data structure and the characteristics of the traffic being classified. While significant efforts have been made to optimize data structures, the potential of leveraging traffic characteristics remains underexplored. In this study, we revisit the network dataplane by integrating the network measurement module with the packet classification module. We propose an innovative Network-aware Packet Classification system (NPC) that utilizes sketch techniques to extract network traffic features. These features guide the construction of decision trees, enabling efficient and adaptable packet classification across diverse network environments. Experimental results demonstrate that the NPC achieves speedups ranging from 1.86× to 23.88× over state-of-the-art algorithms, while significantly reducing memory overhead and construction time, highlighting its practical value in real-world scenarios. Furthermore, integrating NPC into Open vSwitch (OVS) yields throughput improvements of 10.71× to 13.01× compared to the native OVS.
Enhancing Privacy and Robustness in Federated Learning with Local Data Distribution Invariance and Byzantine-Resilient Aggregation Bakary Dolo, Faiza Loukil, Khouloud Boukadi, Kavé Salamatian Proceedings International Symposium on Software Reliability Engineering ISSRE, 2025 Federated Learning (FL) has emerged as a promising paradigm for decentralized machine learning, enabling multiple clients to collaboratively train a global model without sharing their raw data. Despite its privacy-preserving design, FL remains vulnerable to privacy leakage through inference attacks, such as membership inference, and to integrity threats like Byzantine behaviors that can degrade model reliability. To address these risks, we propose Local Data Privatization Preprocessing (LDPP), a lightweight client-side method that enforces differential privacy while preserving the statistical properties of local data. LDPP operates through a three-stage process: (i) transforming data into a standardized representation (normal or uniform), (ii) injecting calibrated noise using differential privacy mechanisms, such as Laplace or Gaussian distributions, and (iii) applying an inverse transformation to asymptotically recover the original data distribution. We formally prove that LDPP satisfies $\epsilon$-differential privacy and maintains key distributional characteristics. Additionally, LDPP can be combined with robust aggregation techniques, such as Krum, to strengthen defense against adversarial tampering. Comprehensive experiments in EMNIST and MedMNIST datasets demonstrate that LDPP significantly reduces the success of membership inference attacks and improves robustness under label-flipping scenarios while preserving high model accuracy. These findings position LDPP as a scalable and practical solution to improve both privacy and robustness in federated learning frameworks.
Towards a More Efficient Sinkhorn Distance Computation in Neural Topic Models Pierre Dardouillet, Kavé Salamatian, Hervé Verjus, Faiza Loukil, David Telisson, Olivier Le Van Proceedings of the International Joint Conference on Neural Networks, 2025 In natural language processing, topic modeling aims to extract a corpora latent structure. In recent years, optimal transport distances have improved the topic extraction capabilities of Neural Topic Models (NTMs). More precisely, the Sinkhorn-Knopp algorithm is used to compute the blurred Wasserstein distance with relatively low complexity and is fully differentiable. This algorithm ease of implementation and advantages are thus particularly interesting for enforcing desired properties in NTMs. However, the algorithm can be unstable and inefficient under low blur setups, hence hindering overall topic model performances. In this article, we first assess the stability and efficiency of the Sinkhorn-Knopp algorithm in NTM scenarios. We compare five of the most relevant variations of this algorithm, and three distinct usages in NTMs. We evaluate each specific Sinkhorn-Knopp algorithm variation and topic model architecture independently, under various quantitative and qualitative metrics. Furthermore, we propose a novel method that focuses on the Sinkhorn-Knopp algorithm initialization, by reusing its dual variables from previous model updates as warm-start values. Our experiments reveal that our method can drastically improve the computation efficiency of the algorithm by reducing its number of iterations by up to 70%, and is easily applicable to any topic model using the Sinkhorn distance.
IEcons: A New Consensus Approach Using Multi-Text Representations for Clustering Task Karima Boutalbi, Rafika Boutalbi, Hervé Verjus, Kave Salamatian, David Telisson, Olivier Le Van International Conference on Information and Knowledge Management Proceedings, 2024 Today we are able to generate a large set of text representations from the simple Bag-of-word (BOW) to the recent transformers capturing the semantic and the contextual text meaning. It was proven that there is no best text representation for text clustering task. Consequently, some works combined text representations using a consensus clustering approach. Two consensus approach types exist, namely explicit and implicit consensus. In the explicit consensus, also known asensemble clustering, the consensus function is applied a posterior after obtaining cluster labels from each text representation clustering allowing to capture global mutual information between the partitions of all text representations. On the other hand, implicit consensus uses tensor clustering to optimize the clustering consensus partition that deals with similarity matrices of text representations.
Hierarchical Tensor Clustering for Multiple Graphs Representation Karima Boutalbi, Rafika Boutalbi, Hervé Verjus, Kave Salamatian Www 2024 Companion Companion Proceedings of the ACM Web Conference, 2024 Graph clustering is a challenging task, especially when there is a hierarchical structure. The availability of multiple graphs (or relational graphs), in the multi-graph setting, provides additional information that can be leveraged to improve clustering results. This paper aims to develop a new hierarchical clustering algorithm for multi-graphs, the HTGM algorithm. This algorithm represents the set of graphs in the multi-graph as a 3-way tensor, and maximizes a modularity measure, extending the modularity-based graph clustering algorithm to multi-graphs and tensor structures. We evaluate the proposed algorithm over synthetic and real-world datasets and show the effectiveness of the proposed algorithm by benchmarking it to alternative clustering algorithms.
Strategic Integration of Context for Fine-Tuning Topic Model Performance Pierre Dardouillet, Kavé Salamatian, Hervé Verjus, Faiza Loukil, David Telisson, Olivier Le van Proceedings 2024 IEEE 48th Annual Computers Software and Applications Conference Compsac 2024, 2024 Issue Tracking Systems software serves as an interface between a company and its customers. Customers can report bugs and seek assistance, among other demands. Reported issues include textual description, along with company defined metadata, aim at simplifying issue treatment by experts. In the context of the rapid growth of customer-reported issues, the manual treatment process becomes tedious and time-consuming. As a result, more and more studies focus on automating parts of this process, using semantic extraction and topic modeling approaches to automatically classify issues. To this end, most approaches consider the issue of textual description along with metadata, which can be a source of uncertainty and misleading in many real-world scenarios. Besides, knowledge from the company experts is often neglected. In this paper, we propose a general taxonomy of information incorporation into topic models. This aims to assemble all existing techniques, to further detect literature gaps. In addition, we propose a technique to incorporate expert knowledge into neural topic models. We evaluate our techniques and others in the literature on a real-world dataset coming from the JIRA software of a French HR management company. Results show a significant increase of more than 22% in classification performances when using expert knowledge, in addition to the issue textual description. The results validate our approach's effectiveness in improving the automatic classification of issues.
LLM-centric pipeline for information extraction from invoices Faiza Loukil, Sarah Cadereau, Hervé Verjus, Mattéo Galfre, Kavé Salamatian, David Telisson, Quentin Kembellec, Olivier Le Van 2024 2nd International Conference on Foundation and Large Language Models Fllm 2024, 2024 Extracting information from digital documents is an evolving area of research, especially with the recent advances in artificial intelligence and computer vision. Recently, Large Language Models (LLMs) have shown remarkable performance in various natural language processing tasks, including data extraction from documents. However, the accuracy of these models can be significantly affected when dealing with large or complicated documents due to the inherent complexity and variability of rich formats. In this paper, we target a specific type of complex document: financial invoices.OCR technology extracts editable and searchable data from different types of documents transformed into an image, e.g., scanned documents, and PDFs. However, OCR is highly sensitive to noise and image mis-alignment that frequently results into wrong extraction of texts. Moreover, OCR cannot understand the structure of a document, and leverage it to understand the semantic of the document’s content to extract structured information from document. OCR is therefore considered as a preprocessing step that need to be completed with further processing. In this paper, we use text-based LLMs to enrich the outputs of Optical Character Recognition (OCR) applied to documents to extract structured information from financial invoices. We show here, that by fusing OCR engines, including Tesseract and DocTR, with the two open-source LLM models, Llama3 and Mistral, we significantly improve the accuracy and reliability of information extraction operations on two datasets featuring business documents: SROIE and FATURA datasets.
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction Dong Wang, Kavé Salamatian, Yunqing Xia, Weiwei Deng, Qi Zhang Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2023 Although deep pre-trained language models have shown promising benefit in a large set of industrial scenarios, including Click-Through-Rate (CTR) prediction, how to integrate pre-trained language models that handle only textual signals into a prediction pipeline with non-textual features is challenging.
Misconfiguration-Free Compositional SDN for Cloud Networks Heng Pan, Zhenyu Li, Penghao Zhang, Penglai Cui, Kave Salamatian, Gaogang Xie IEEE Transactions on Dependable and Secure Computing, 2023 Cloud computing provides a new paradigm to offer flexible IT infrastructures. In IaaS clouds, tenants deploy software-defined networking (SDN) policies to simplify network management and customize network behaviors. However, programming SDN networks is error-prone no matter using low-level APIs or high-level programming languages. Specifically, SDN policies may contain misconfigurations that do not break the pre-defined network invariants (e.g., black holes), but either degrade the deployment efficiency or mistakenly translate tenants intents. Prior studies for checking either traditional access control policies or network-wide invariants, are thus fail to detect these misconfigurations. To address this gap, this paper presents PMM, a misconfiguration checking tool for compositional SDN that works at the data plane of cloud networks. We first propose a new data structure, minimal interval set, to represent the match patterns of rulesets. This representation serves the basis for composition algebra construction and misconfiguration checking. We then propose the principles, algorithms and also optimisations for fast and accurate checking. We finally implement PMM in Covisor. Experiments with both real-world rulesets and synthetic rulesets show that PMM can detect misconfigurations of SDN policies in cloud networks within hundreds of milliseconds.
A Clustering Approach Combining Lines and Text Detection for Table Extraction Karima Boutalbi, Visar Sylejmani, Pierre Dardouillet, Olivier Le Van, Kave Salamatian, Hervé Verjus, Faiza Loukil, David Telisson Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
Web Tracking Cartography with DNS Records Jingxiu Su, Zhenyu Li, Stephane Grumbach, Muhammad Ikram, Kave Salamatian, Gaogang Xie 2018 IEEE 37th International Performance Computing and Communications Conference Ipccc 2018, 2018
Toward a polymorphic future Internet: A networking science approach International Telecommunication Union Proceedings of the 2010 ITU T Kaleidoscope Academic Conference Beyond the Internet Innovations for Future Networks and Services, 2010
Scan surveillance in internet networks Khadija Ramah Houerbi, Kavé Salamatian, Farouk Kamoun Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Measurement of P2PTV applications on both sides of the world Cfip 2009 Francophone Symposium on Protocol Engineering, 2009
Understanding the characteristics of online commenting Jong Gun Lee, Kave Salamatian Proceedings of 2008 ACM Conext Conference 4th International Conference on Emerging Networking Experiments and Technologies Conext 08, 2008
Vulnerabilities in epidemic forwarding Alaeddine el Fawal, Jean-Yves le Boudec, Kave Salamatian 2007 IEEE International Symposium on A World of Wireless Mobile and Multimedia Networks Wowmom, 2007
Measuring P2P IPTV traffic on both sides of the world Thomas Silverston, Olivier Fourmaux, Kavé Salamatian, Kenjiro Cho Proceedings of 2007 ACM Conext Conference 3rd International Conference on Emerging Networking Experiments and Technologies Conext, 2007
Flexible grid-based clustering Marc-Ismaël Akodjènou-Jeannin, Kavé Salamatian, Patrick Gallinari Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Securing internet coordinate embedding systems Mohamed Ali Kaafar, Laurent Mathy, Chadi Barakat, Kave Salamatian, Thierry Turletti, Walid Dabbous ACM SIGCOMM 2007 Conference on Computer Communications, 2007
Distribution of traffic among applications as measured in the French METROPOLIS project Annales Des Telecommunications Annals of Telecommunications, 2007
Securing internet coordinate systems Dali Kaafar, Laurent Mathy, Kavé Salamatian, Chadi Barakat, Thierry Turletti, Walid Dabbous Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Early application identification Laurent Bernaille, Renata Teixeira, Kave Salamatian Proceedings of Conext 06 2nd Conference on Future Networking Technologies, 2006
Research challenges in QoS routing X. Masip-Bruin, M. Yannuzzi, J. Domingo-Pascual, A. Fonte, M. Curado, E. Monteiro, F. Kuipers, P. Van Mieghem, S. Avallone, G. Ventre, P. Aranda-Gutiérrez, M. Hollick, R. Steinmetz, L. Iannone, K. Salamatian Computer Communications, 2006
Traffic classification on the fly Laurent Bernaille, Renata Teixeira, Ismael Akodkenou, Augustin Soule, Kave Salamatian Computer Communication Review, 2006
On the achievability of cut-set bound for a class of erasure relay channels 2004 International Workshop on Wireless Ad Hoc Networks, 2005
On the capacity of multiple input erasure relay channels: The Non-degraded case 43rd Annual Allerton Conference on Communication Control and Computing 2005, 2005
An information theory for erasure channels 43rd Annual Allerton Conference on Communication Control and Computing 2005, 2005
Cross-layer routing in wireless mesh networks 1st International Symposium on Wireless Communication Systems 2004 Proceedings Iswcs 04, 2004
Fast flow classification over Internet A. Oveissian, K. Salamatian, A. Soule, N. Taft Proceedings Second Annual Conference on Communication Networks and Services Research, 2004
Traffic-Aware Design for Multi-dimensional Lookup and Forwarding: From IP Routing to Packet Classification X Zhang, Q Qiu, Z Xu, P He, G Zhang, L Li, J Zhou, K Salamatian, G Xie IEEE Transactions on Networking , 2026 2026
Clustering Tensoriel Hiérarchique pour la Représentation de Graphes Multiples K Boutalbi, R Boutalbi, H Verjus, K Salamatian LA 26ÈME CONFÉRENCE FRANCOPHONE SUR L'EXTRACTION ET LA GESTION DES CONNAISSANCES , 2026 2026
Ten Years of Event-Driven BGP Evolution in India and Bangladesh N Ahmad Rauf, FS Choudhry, A Mirza, MS Ilyas, Q Lone, JH Cowie, ... Proceedings of the 20th Asian Internet Engineering Conference, 61-69 , 2025 2025
Enhancing Privacy and Robustness in Federated Learning with Local Data Distribution Invariance and Byzantine-Resilient Aggregation B Dolo, F Loukil, K Boukadi, K Salamatian 2025 IEEE 36th International Symposium on Software Reliability Engineering … , 2025 2025
NPC: Rethinking Dataplane through Network-aware Packet Classification X Zhang, Q Qiu, Z Xu, P He, X Liu, K Salamatian, C Pei, G Xie Proceedings of the ACM SIGCOMM 2025 Conference, 677-691 , 2025 2025 Citations: 2
Towards a More Efficient Sinkhorn Distance Computation in Neural Topic Models P Dardouillet, K Salamatian, H Verjus, F Loukil, D Telisson, O Le Van 2025 International Joint Conference on Neural Networks (IJCNN), 1-10 , 2025 2025
LLM-centric pipeline for information extraction from invoices F Loukil, S Cadereau, H Verjus, M Galfre, K Salamatian, D Telisson, ... International Conference on Foundation and Large Language Models (FLLM2024) , 2024 2024 Citations: 10
Iecons: A new consensus approach using multi-text representations for clustering task K Boutalbi, R Boutalbi, H Verjus, K Salamatian, D Telisson, O Le Van Proceedings of the 33rd ACM International Conference on Information and … , 2024 2024 Citations: 2
Ironing the graphs: Toward a correct geometric analysis of large-scale graphs S Naama, K Salamatian, F Bronzino arXiv preprint arXiv:2407.21609 , 2024 2024 Citations: 2
Strategic Integration of Context for Fine-Tuning Topic Model Performance P Dardouillet, K Salamatian, H Verjus, F Loukil, D Telisson 2024 IEEE 48th Annual Computers, Software, and Applications Conference … , 2024 2024
Hierarchical tensor clustering for multiple graphs representation K Boutalbi, R Boutalbi, H Verjus, K Salamatian Companion Proceedings of the ACM Web Conference 2024, 613-616 , 2024 2024 Citations: 3
Digital routes and borders in the Middle East: the geopolitical underpinnings of Internet connectivity F Douzet, L Pétiniaud, K Salamatian, JL Samaan Territory, Politics, Governance 11 (6), 1059-1080 , 2023 2023 Citations: 16
A Clustering Approach Combining Lines and Text Detection for Table Extraction K Boutalbi, V Sylejmani, P Dardouillet, O Le Van, K Salamatian, H Verjus, ... International Conference on Document Analysis and Recognition, 139-145 , 2023 2023 Citations: 2
Bert4ctr: An efficient framework to combine pre-trained language model with non-textual features for ctr prediction D Wang, K Salamatian, Y Xia, W Deng, Q Zhang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and … , 2023 2023 Citations: 24
Machine learning for text anomaly detection: A systematic review K Boutalbi, F Loukil, H Verjus, D Telisson, K Salamatian 2023 IEEE 47th Annual Computers, Software, and Applications Conference … , 2023 2023 Citations: 10
Strategic and geopolitical implication of Networks K Limonier, P Barford, K Salamatian The networking channel , 2022 2022
Learning supplementary NLP features for CTR prediction in sponsored search D Wang, S Yan, Y Xia, K Salamatian, W Deng, Q Zhang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and … , 2022 2022 Citations: 15
Misconfiguration-free compositional SDN for cloud networks H Pan, Z Li, P Zhang, P Cui, K Salamatian, G Xie IEEE Transactions on Dependable and Secure Computing 20 (3), 2484-2499 , 2022 2022 Citations: 8
Cross-Talk Between Intramolecular and Intermolecular Amino Acid Networks Orchestrates the Assembly of the Cholera Toxin B Pentamer via the Residue His94 M Achoch, G Feverati, K Salamatian, L Vuillon, C Lesieur International Conference on Advanced Intelligent Systems for Sustainable … , 2022 2022
Improving open virtual switch performance through tuple merge relaxation in software defined networks X Zhang, K Salamatian, G Xie IEEE Transactions on Network and Service Management 19 (3), 2078-2091 , 2022 2022 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Traffic classification on the fly L Bernaille, R Teixeira, I Akodkenou, A Soule, K Salamatian ACM SIGCOMM Computer Communication Review 36 (2), 23-26 , 2006 2006 Citations: 885
Traffic matrix estimation: Existing techniques and new directions A Medina, N Taft, K Salamatian, S Bhattacharyya, C Diot ACM SIGCOMM Computer Communication Review 32 (4), 161-174 , 2002 2002 Citations: 830
Early application identification L Bernaille, R Teixeira, K Salamatian Proceedings of the 2006 ACM CoNEXT conference, 6 , 2006 2006 Citations: 606
Combining filtering and statistical methods for anomaly detection A Soule, K Salamatian, N Taft Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement, 31-31 , 2005 2005 Citations: 459
Traffic matrices: balancing measurements, inference and modeling A Soule, A Lakhina, N Taft, K Papagiannaki, K Salamatian, A Nucci, ... Proceedings of the 2005 ACM SIGMETRICS international conference on … , 2005 2005 Citations: 296
Applying PCA for traffic anomaly detection: Problems and solutions D Brauckhoff, K Salamatian, M May IEEE INFOCOM 2009, 2866-2870 , 2009 2009 Citations: 248
Anomaly extraction in backbone networks using association rules D Brauckhoff, X Dimitropoulos, A Wagner, K Salamatian Proceedings of the 9th ACM SIGCOMM conference on Internet measurement, 28-34 , 2009 2009 Citations: 216
Cross-layer routing in wireless mesh networks L Iannone, R Khalili, K Salamatian, S Fdida 1st International Symposium onWireless Communication Systems, 2004., 319-323 , 2004 2004 Citations: 191
Research challenges in QoS routing X Masip-Bruin, M Yannuzzi, J Domingo-Pascual, A Fonte, M Curado, ... Computer communications 29 (5), 563-581 , 2006 2006 Citations: 181
Hidden markov modeling for network communication channels K Salamatian, S Vaton ACM SIGMETRICS Performance Evaluation Review 29 (1), 92-101 , 2001 2001 Citations: 171
An approach to model and predict the popularity of online contents with explanatory factors JG Lee, S Moon, K Salamatian 2010 IEEE/WIC/ACM International Conference on Web Intelligence and … , 2010 2010 Citations: 152
A pragmatic definition of elephants in internet backbone traffic K Papagiannaki, N Taft, S Bhattacharyya, P Thiran, K Salamatian, C Diot Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment, 175-176 , 2002 2002 Citations: 144
Traffic analysis of peer-to-peer IPTV communities T Silverston, O Fourmaux, A Botta, A Dainotti, A Pescapé, G Ventre, ... Computer Networks 53 (4), 470-484 , 2009 2009 Citations: 112
Traffic matrix tracking using kalman filters A Soule, K Salamatian, A Nucci, N Taft ACM SIGMETRICS Performance Evaluation Review 33 (3), 24-31 , 2005 2005 Citations: 96
Flow classification by histograms: or how to go on safari in the internet A Soule, K Salamatia, N Taft, R Emilion, K Papagiannaki Proceedings of the joint international conference on Measurement and … , 2004 2004 Citations: 91
A new analytic approach to evaluation of packet error rate in wireless networks R Khalili, K Salamatian 3rd Annual Communication Networks and Services Research Conference (CNSR'05 … , 2005 2005 Citations: 90
Modeling and predicting the popularity of online contents with Cox proportional hazard regression model JG Lee, S Moon, K Salamatian Neurocomputing 76 (1), 134-145 , 2012 2012 Citations: 88
Meta-algorithms for software-based packet classification P He, G Xie, K Salamatian, L Mathy 2014 IEEE 22nd International Conference on Network Protocols, 308-319 , 2014 2014 Citations: 82
Securing internet coordinate embedding systems MA Kaafar, L Mathy, C Barakat, K Salamatian, T Turletti, W Dabbous ACM SIGCOMM Computer Communication Review 37 (4), 61-72 , 2007 2007 Citations: 80
Adaptive path isolation for elephant and mice flows by exploiting path diversity in datacenters W Wang, Y Sun, K Salamatian, Z Li IEEE Transactions on Network and Service Management 13 (1), 5-18 , 2016 2016 Citations: 72
