Saad Saleh

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Papers by Saad Saleh

On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions

IEEE Access, 2022

The high performance requirements of nowadays computer networks are limiting their ability to sup... more 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 nonvolatile 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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Towards Energy Efficient Memristor-based TCAM for Match-Action Processing

Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC), 2022

Match-action processors play a crucial role of communicating end-users in the Internet by computi... more 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, nonvolatility, 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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TCAmMCogniGron : Energy Efficient Memristor-Based TCAM for Match-Action Processing

Proceedings of the 7th International Conference on Rebooting Computing (ICRC), 2022

The Internet relies heavily on programmable matchaction processors for matching network packets a... more The Internet relies heavily on programmable matchaction 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 memristorbased TCAM architecture called TCAmM CogniGron , 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 Nbdoped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 µW and 1 µW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.

format_quoteTCAmM CogniGron's power consumption is dramatically lower at 16 µW for matches and 1 µW for mismatches, demonstrating superior energy efficiency over traditional architectures.format_quote

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Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research

by Saad Saleh and Usman Ilyas

Anonymity services have seen high growth rates with increased usage in the past few years. Among ... more 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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MuGKeG: Secure Multi-channel Group Key Generation Algorithm for Wireless Networks

by Saad Saleh and Usman Ilyas

The broadcast nature of communication channels in infrastructureless wireless networks poses chal... more 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(log N) 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.

format_quoteThe MuGKeG algorithm operates independently of channel conditions, enhancing security against eavesdroppers.format_quote

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Post Summarization of Microblogs of Sporting Events

by Saad Saleh and Usman Ilyas

Every day 645 million Twitter users generate approximately 58 million tweets. This motivates the ... more 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 mi-croblog 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.

$Table 5: Recall of key moments of IPL match using various clustering techniques. In the context of information retrieval, recall is the frac- tion of correctly classified instances to the total number of identified instances. Tab. 5 shows the recall of key mo- ments of IPL match. Results show that clustering using Eu- clidean distance has highest recall value for moments of all categories. Hybrid TF-IDF and phrase reinforcement have lower recall statistics at the moments capturing “Fours” and “Sixes”. Clustering using cosine similarity gave lower re- call statistics than Euclidian but higher recall than all other clustering techniques.$

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A Stochastic Model for Transit Latency in OpenFlow SDNs

by Azeem Iqbal, Azeem Iqbal, Saad Saleh, and Usman Ilyas

Software defined networks (SDNs) introduced the concept of decoupling control and data planes whi... more 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 R 2 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.

format_quoteWe measure and analyze the delay added by OpenFlow switches for numerous packet sizes and conditions, essential for network performance assessments.format_quote

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Analytical Modeling of End-to-End Delay in OpenFlow Based Networks

by Azeem Iqbal, Azeem Iqbal, Saad Saleh, and Usman Ilyas

OpenFlow enabled networks split and separate the data and control planes of traditional networks.... more OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design com-modifies 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. Since controllers are not necessarily co-located with switches that can significantly impact the forwarding delay incurred by packets in switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this work 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.