Current network functions consume a significant amount of energy and lack the capacity to support more expressive learning models like neuromorphic functions. The major reason is the underlying transistor-based components that require continuous energy-intensive data movements between the storage and computational units. In this research, we propose the use of a novel component, called Memristor, which can colocalize computation and storage, and provide computational capabilities. Building on memristors, we propose the concept of match-compute processing for supporting energy efficient network functions. Considering the analog processing of memristors, we propose a Probabilistic Content Addressable Memory (pCAM) abstraction which can provide analog match functions. pCAM provides deterministic and probabilistic outputs depending upon the closeness of match of an incoming query with the specified network policy. pCAM uses a crossbar array for line rate matrix multiplications on the match outputs. We proposed a match-compute packet processing architecture and developed the programming abstractions for a baseline network function, i.e., Active Queue Management, which drops packets based upon the higher-order derivatives of sojourn times and buffer sizes. The analysis of match-compute processing over a physically fabricated memristor chip showed only 0.01 fJ/bit/cell of energy consumption, which is 50 times less than the traditional match-action processing.

Active Queue Management (AQM) algorithms are heavily used in packet processors to maintain an optimal queue size and avoid issues like Bufferbloat. Despite the remarkable performance, the traditional AQM algorithms face a major challenge of estimating the accurate queue congestion due to bursty network conditions. The major reason is the use of baseline queue statistics for congestion estimation like delay and sojourn time for Random Early Detection (RED) and Controlled Delay (CoDel), respectively. In this paper, we propose a novel dAQM algorithm that uses advanced traffic statistics like three higher-order derivatives of sojourn time and buffer size along with the baseline sojourn time and buffer size for accurate congestion estimation. dAQM adjusts its drop rate based on the continuously varying congestion to cater to the needs of bursty traffic. We simulated dAQM in ns-3 and analyzed its performance for FTP traffic by variation in traffic load and packet sizes. The results showed that dAQM provides at least 25% and 39.7% reduction in packet loss ratio and flow completion time, respectively, as compared to the traditional AQM algorithms.

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.

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.

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.

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.

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.

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.