Distributed Systems

Saad Saleh

  1. Analog In-Network Computing through Memristor-based Match-Compute Processing (, , , and ), In Proceedings of the 43rd International Conference on Computer Communications (INFOCOM 2024), IEEE, .

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  2. dAQM: Derivative-based Active Queue Management (, and ), In Proceedings of the 23rd International Federation for Information Processing (IFIP) Networking Conference (IFIP NETWORKING 2024), IEEE, .

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  3. Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size (, and ), In Proceedings of the 37th Network Operations and Management Symposium (NOMS 2024), IEEE, .

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  4. Beyond Digital! Memristor-based Energy Efficient Analog Network Functions ( and ), In ICT.OPEN - CompSys Research for a Responsibly Digitalised Society, NWO ICT.OPEN, .

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  5. Towards Analog In-Network Computing for Supporting Cognitive and Energy-Efficient Network Functions ( and ), In 6th International Conference on Applications of Intelligent Systems (APPIS 2024), University of Las Palmas de Gran Canaria, Spain, .

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  6. The Future is Analog: Energy-Efficient Cognitive Network Functions over Memristor-Based Analog Computations ( and ), In Proceedings of the 22nd ACM SIGCOMM Workshop on Hot Topics in Networks (HotNets 2023), ACM, .

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    Current network functions build heavily on fixed programmed rules and lack capacity to support more expressive learning models, e.g. brain-inspired Cognitive computational models using neuromorphic computations. The major reason for this shortcoming is the huge energy consumption and limitation in expressiveness by the underlying TCAM-based digital packet processors. In this research, we show that recent emerging technologies from the analog domain have a high potential in supporting network functions with energy efficiency and more expressiveness, so called cognitive functions. We propose an analog packet processing architecture building on a novel technology named Memristors. We develop a novel analog match-action memory called Probabilistic Content-Addressable Memory (pCAM) for supporting deterministic and probabilistic match functions. We develop the programming abstractions and show the support of pCAM for an active queue management-based analog network function. The analysis over an experimental dataset of a memristor chip showed only 0.01 fJ/bit/cell of energy consumption for corresponding analog computations which is 50 times less than digital computations.

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  7. Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models ( and ), In Proceedings of the 18th International Symposium on Nanoscale Architectures (NanoArch 2023), ACM, .

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    The Internet makes use of high performance network switches in order to route network traffic from end users to servers. Despite line-rate performance, the current switches consume huge energy and cannot support more expressive learning models, like cognitive functions using neuromorphic computations. The major reason is the use of transistors in the underlying Ternary Content-Addressable Memory (TCAM) which is volatile and supports digital computations only. These shortcomings can be bypassed by developing network memories building on novel components, like Memristors, due to their nonvolatile, nanoscale and analog storage/processing characteristics. In this paper, we propose the use of a novel memristor-based Probabilistic Associative Memory, PAmM, which provides both digital (deterministic) and analog (probabilistic) outputs for supporting cognitive computational models in network switches. The traditional digital operations can be supported by a memristor-based energy efficient TCAM, called TCAmMCogniGron. Building on PAmM and TCAmMCogniGron, we propose a novel network switching architecture and analyze its energy efficiency over the experimental dataset of a Nb-doped SrTiO3 memristive device. The results show that the proposed network switching architecture consumes only 0.01 fJ/bit/cell energy for analog compute operations which is at least 50 times less than the digital operations.

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  8. PAmM: Memristor-based Probabilistic Associative Memory for Neuromorphic Network Functions (, , and ), In Proceedings of the Non-Volatile Memory Technology Symposium (NVMTS 2023), IEEE, .

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  9. Memristor-based Probabilistic Content Addressable Memory for Cognitive Network Functions (, , and ), In Neuromorphic Computing Netherlands (NCN 2023) Workshop [Posters], .

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  10. Memristor-based Cognitive and Energy-Efficient Analog In-network Computing ( and ), In Neuromorphic Summer School [Posters], Kiel CRC Neurotronics and CogniGron, .

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  11. On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions ( and ), In IEEE Access, IEEE, volume 10, .

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    The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named Memristor . A memristor is a computational component characterized by non-volatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristor-based Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).

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  12. TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing (, , and ), In Proceedings of the 7th International Conference on Rebooting Computing (ICRC 2022), IEEE, .

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    The Internet relies heavily on programmable match-action processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistor-based CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the ‘Memristor’, for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture called TCAmMCogniGron, built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nb-doped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 uW and 1 uW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.

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  13. Towards Energy Efficient Memristor-based TCAM for Match-Action Processing (, , and ), In Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC 2022), IEEE, .

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    Match-action processors play a crucial role of communicating end-users in the Internet by computing network paths and enforcing administrator policies. The computation process uses a specialized memory called Ternary Content Addressable Memory (TCAM) to store processing rules and use header information of network packets to perform a match within a single clock cycle. Currently, TCAM memories consume huge amounts of energy resources due to the use of traditional transistor-based CMOS technology. In this article, we motivate the use of a novel component, the memristor, for the development of a TCAM architecture. Memristors can provide energy efficiency, non-volatility, and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture built upon the voltage divider principle for energy efficient match-action processing. Moreover, we have tested the performance of the memristor-based TCAM architecture using the experimental data of a novel Nb-doped SrTiO3 memristor. Energy analysis of the proposed TCAM architecture for given memristor shows promising power consumption statistics of 16 μW for a match operation and 1 μW for a mismatch operation.

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  14. Using Memristors for Energy Efficient Cognitive Network Functions (, , and ), In Symposium on Physics of Information in Matter [Poster Session], AMOLF, .

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  15. In-Network Computing Over Memristor-Based Cognitive Network Functions (, , and ), In Brain-Inspired Concepts and Materials for Information Processing (Brainspiration) Conference [Poster Session], University of Twente, .

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  16. Memristor-Based Cognitive Network Packet Processors ( and ), In Neuromorphic Computing Netherlands (NCN 2022) Workshop [Abstracts, Talks and Posters], Radboud University, .

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  17. Memristor-Based Cognitive and Energy Efficient In-Network Processing (, , and ), In Workshop on Bio-Inspired Information Pathways [Abstracts and Posters], CRC-1461 Neurotronics, University of Kiel, .

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  18. Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research (, and ), In Journal of Network and Computer Applications, Elsevier, volume 114, .

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    Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that ‘security’ and ‘anonymity’ are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on ‘deanonymization’ the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.

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  19. MuGKeG: Secure Multi-channel Group Key Generation Algorithm for Wireless Networks (, , and ), In Wireless Personal Communications, Springer, volume 96, .

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    The broadcast nature of communication channels in infrastructureless wireless networks poses challenges to security. In this paper, we propose a novel technique namely secure multi-channel group key generation (MuGKeG) algorithm. We utilize the available channels switching behaviour between multiple nodes to hide our key from eavesdropper. We provide descriptions for an illustrative base case of three users and one eavesdropper and expand it for the case of N users with C channels and M eavesdroppers. Repeated application of the MuGKeG algorithm on the order of O(logN) allows scaling the size of the group in the order of millions. We provide an analytical closed-form solution for the entropy of the secret group key generated when eavesdroppers follow an optimal attack strategy, and verify it by ns-3 simulations. Comparison with previous state-of-the-art schemes suggests that MuGKeG can provide upto 20 kbps increase in secrecy rate with a scalable key size.

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  20. A Stochastic Model for Transit Latency in OpenFlow SDNs (, , , and ), In Computer Networks, Elsevier, volume 113, .

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    Software defined networks (SDNs) introduced the concept of decoupling control and data planes which is a paradigm shift. The OpenFlow protocol is one of a number of technologies that enables this decoupling and, in effect, commodifies network equipment. As of now, there is still limited work that has been done towards modeling the transit delay across OpenFlow switches experienced by network traffic. In this work we develop a stochastic model for the path latency in Open vSwitch (used together with a POX controller) based on measurements made in experiments performed on three different platforms which include 1) Mininet, 2) MikroTik RouterBoard 750GL and 3) GENI testbed softswitch. We propose a log-normal mix model (LNMM) and show that it offers a R2 value of greater than 0.90 for most of our experiments. We also demonstrate how the M/M/1 models proposed in earlier studies is a poor fit.

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  21. Post Summarization of Microblogs of Sporting Events (, , , , and ), In Proceedings of the 26th International Conference on World Wide Web Companion, .

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    Every day 645 million Twitter users generate approximately 58 million tweets. This motivates the question if it is possible to generate a summary of events from this rich set of tweets only. Key challenges in post summarization from microblog posts include circumnavigating spam and conversational posts. In this study, we present a novel technique called lexi-temporal clustering (LTC), which identifies key events. LTC uses k-means clustering and we explore the use of various distance measures for clustering using Euclidean, cosine similarity and Manhattan distance. We collected three original data sets consisting of Twitter microblog posts covering sporting events, consisting of a cricket and two football matches. The match summaries generated by LTC were compared against standard summaries taken from sports sections of various news outlets, which yielded up to 81% precision, 58% recall and 62% F-measure on different data sets. In addition, we also report results of all three variants of the recall-oriented understudy for gisting evaluation (ROUGE) software, a tool which compares and scores automatically generated summaries against standard summaries.

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  22. Analytical Modeling of End-to-End Delay in OpenFlow Based Networks (, , , , and ), In IEEE Access, IEEE, volume 5, .

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    OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design commodifies network switches and enables centralized control of the network. Control decisions are made by an OpenFlow controller, and locally cached by switches, as directed by controllers. This can significantly impact the forwarding delay incurred by packets in switches, because controllers are not necessarily co-located with switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this paper, we develop a stochastic model for the end to end delay in OpenFlow switches based on measurements made in Internet-scale experiments performed on three different platforms, i.e., Mininet, the GENI testbed, and the OF@TEIN testbed.

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  23. Sentiment Classification of Tweets using Hierarchical Classification (, , , and ), In International Conference on Communications, .

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    This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral. As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all three classes. The developed classifier has been made available for online use.

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  24. Detecting National Political Unrest on Twitter (, , , and ), In International Conference on Communications, .

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    The popular uprisings in a number of countries in the Middle East and North Africa in the Spring of 2011 were broadcasted live and enabled by local populations' access to social networking services such as Twitter and Facebook. The goal of this paper is to study the flow characteristics of the information flow of these broadcasts on Twitter. We have used language independent features of Twitter traffic to identify differences in information flows on Twitter mentioning countries experiencing some form of unrest, compared to traffic mentioning countries with peaceful political situations. We used these features to identify countries with political unstable situation. For empirical analysis, we collected several data sets of countries that were experiencing political unrest, as well as a set of countries in a control group that were not subject to such socio-political condition. Several different methods are used to model the flow of information between Twitter users in data sets as graphs, called information cascades. By using the dynamic properties of information cascades, naïve Bayes and SVM classifiers both achieve true positives rates of 100%, with false positives rates of 3% and 0%, respectively.

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  25. Improving QoS of IPTV and VoIP over IEEE 802.11n (, and ), In Computers & Electrical Engineering, Elsevier, volume 43, .

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    Tremendous growth rates of Internet Protocol Television (IPTV) and Voice over Internet Protocol (VoIP) have demanded the shift of paradigm from wired to wireless applications. Increased packet loss with continuously varying wireless conditions make the transmission a challenging task in wireless environment. Our study investigates and proposes improvement in the transmission of combined IPTV and VoIP over the IEEE 802.11n WLAN. Our major contributions include the analytical and experimental investigations of (1) transport layer protocol UDP/TFRC for IPTV and VoIP, (2) optimal physical layer parameters for IPTV and VoIP, (3) proposition of wireless enhancement of TFMCC (W-TFMCC) to enhance the capacity and Quality of Service (QoS) of wireless IPTV and VoIP. Analytical and experimental evaluations show a 25% increase in capacity using TFRC with 167% more bandwidth share to TCP. Our study shows that use of W-TFMCC with optimal parameters can enhance IPTV and VoIP capacity by 44%.

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  26. IM Session Identification by Outlier Detection in Cross-correlation Functions (, , , and ), In 49th Annual Conference on Information Sciences and Systems, .

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    The identification of encrypted Instant Messaging (IM) channels between users is made difficult by the presence of variable and high levels of uncorrelated background traffic. In this paper, we propose a novel Cross-correlation Outlier Detector (CCOD) to identify communicating end-users in a large group of users. Our technique uses traffic flow traces between individual users and IM service provider's data center. We evaluate the CCOD on a data set of Yahoo! IM traffic traces with an average SNR of -6.11dB (data set includes ground truth). Results show that our technique provides 88% true positives (TP) rate, 3% false positives (FP) rate and 96% ROC area. Performance of the previous correlation-based schemes on the same data set was limited to 63% TP rate, 4% FP rate and 85% ROC area.

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  27. Predicting New Collaborations in Academic Citation Networks of IEEE and ACM Conferences (, , , , , , , and ), In International Conference on Social Computing, .

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    In this paper we study the time evolution of academic collaboration networks by predicting the appearance of new links between authors. The accurate prediction of new collaborations between members of a collaboration network can help accelerate the realization of new synergies, foster innovation, and raise productivity. For this study, the authors collected a large data set of publications from 630 conferences of the IEEE and ACM of more than 257, 000 authors, 61, 000 papers, capturing more than 818, 000 collaborations spanning a period of 10 years. The data set is rich in semantic data that allows exploration of many features that were not considered in previous approaches. We considered a comprehensive set of 98 features, and after processing identified eight features as significant. Most significantly, we identified two new features as most significant predictors of future collaborations; 1) the number of common title words, and 2) number of common references in two authors’ papers. The link prediction problem is formulated as a binary classification problem, and three different supervised learning algorithms are evaluated, i.e. Na¨ıve Bayes, C4.5 decision tree and Support Vector Machines. Extensive efforts are made to ensure complete spatial isolation of information used in training and test instances, which to the authors’ best knowledge is unprecedented. Results were validated using a modified form of the classic 10-fold cross validation (the change was necessitated by the way training, and test instances were separated). The Support Vector Machine classifier performed the best among tested approaches, and correctly classified on average more than 80% of test instances and had a receiver operating curve (ROC) area of greater than 0.80.

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  28. Breaching IM Session Privacy Using Causality (, , , , , , , and ), In Global Communications Conference, .

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    The breach of privacy in encrypted instant messenger (IM) service is a serious threat to user anonymity. Performance of previous de-anonymization strategies was limited to 65%. We perform network de-anonymization by taking advantage of the cause-effect relationship between sent and received packet streams and demonstrate this approach on a data set of Yahoo! IM service traffic traces. An investigation of various measures of causality shows that IM networks can be breached with a hit rate of 99%. A KCI Causality based approach alone can provide a true positive rate of about 97%. Individual performances of Granger, Zhang and IGCI causality are limited owing to the very low SNR of packet traces and variable network delays.

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  29. Capacity Analysis of Combined IPTV and VoIP over IEEE 802.11n (, and ), In 38th Annual IEEE Conference on Local Computer Networks, .

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    Internet Protocol Television (IPTV) and Voice over Internet Protocol (VoIP) have gained unprecedented growth rates in the past few years. Data rate and high coverage area of IEEE 802.11n motivate the concept of combined IPTV and VoIP over IEEE 802.11n. Transmission of combined IPTV and VoIP over a wireless network is a challenging task. In this paper, we deal with the capacity evaluation of combined IPTV and VoIP over IEEE 802.11n. We evaluate the use of Datagram Congestion Control Protocol (DCCP) at transport layer of IPTV and VoIP. Our study shows that DCCP can enhance capacity of IPTV by 25%. Our study confirms that performance of DCCP deteriorates severely in presence of any other UDP flow because of congestion-less mechanism of UDP. Our fairness analysis with TCP traffic shows that IPTV and VoIP using DCCP provides fair share in bandwidth to TCP with 19% increase in combined capacity. We study the effect of IEEE 802.11n parameters and obtain optimal values. We show the optimal values and trends of Access Point (AP) parameters including Queue size, Transmission Opportunity, Aggregation and Block ACK etc. Our study shows that nearly 9 more VoIP users are supported with a queue size of 70 packets and transmission opportunity of 9. Our study concludes that selection of DCCP and optimized parameters over IEEE 802.11n can enhance the capacity of IPTV and VoIP by atleast 25% and 19% respectively as compared to the use of UDP.

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  30. IPTV Capacity Analysis using DCCP over IEEE 802.11n (, and ), In 78th Vehicular Technology Conference, .

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    Internet Protocol Television (IPTV) has gained an enormous growth rate by revolutionizing personal entertainment. High data rates with increased coverage radius of IEEE 802.11n Wireless Local Area Networks (WLANs) motivate the concept of wireless IPTV. Streaming of Television contents over highly pervasive wireless environment with satisfactory Quality of Service (QoS) is a challenging task. Focusing on wireless IPTV, our work deals with the capacity evaluation of IPTV users over IEEE 802.11n. We first present an upper capacity limit for supporting maximum number of users over IEEE 802.11n. We then propose that 4-times packet size is the optimal frame aggregation size for IPTV which maximizes users capacity and QoS. Finally, we suggest the use of Datagram Congestion Control Protocol (DCCP) at transport layer for IPTV. We show that DCCP capacity for IPTV increases upto 35% by reducing packet losses at Access Point (AP), compared to User Datagram Protocol (UDP). We further evaluate fairness of IPTV traffic in the presence of Transmission Control Protocol (TCP) traffic in the network. Our study concludes that IPTV using DCCP over IEEE 802.11n not only provides increased user's capacity but also co-exists fairly with TCP traffic.

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