We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53-88% in both wide area and data center networks compared to prior methods.

A primary challenge in exploiting Cognitive Radio Networks (CRNs), known as the rendezvous problem, is for the users to find each other in the dynamic open spectrum. We study blind rendezvous, where users search for each other without any infrastructural aid. Previous work in this area have focused on efficient blind rendezvous algorithms for two users but the solution for multiple users is still far from optimal. In particular, when two users encounter, one user inherits the other's hopping sequence but the sequence is never shortened or split among the encountering users. We denote this class of algorithms as uncoordinated channel hopping algorithms. In this paper, we introduce a new class of distributed algorithms for multi-user blind rendezvous, called Coordinated Channel Hopping (CCH), where users adjust, or coordinate, the sequence of channels being hopped as they rendezvous pairwise. Compared to existing rendezvous algorithms, our algorithms achieve 80% lower Time To Rendezvous (TTR) in case of multiple users.

A primary challenge in multicasting video in a wireless LAN is to deal with the client diversity -clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine scalable video coding techniques such as MRC or MDC, which divide a video stream into multiple substreams, with inter-layer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. In [7], the authors showed that inter-layer NC indeed helps the delivery of MRC coded media over the WiFi, and proposed how to efficiently search for the optimal coding strategies online.In this paper, we study (1) how NC can help with WiFi delivery of MDC media, and (2) in particular, due to the different decoding requirements of MDC from MRC, whether WiFi delivery of MDC media can benefit more from NC compared to that of MRC media. Our simulation results are somewhat surprising. Even though MDC is generally shown to outperform MRC in lossy channels, most of the benefit of MDC over MRC is lost after applying NC to both schemes.

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Recent neuroimaging evidence has emerged suggesting that there exists a unique individual-specific functional connectivity pattern consistent across tasks. The objective of our study is to utilize functional connectivity patterns to identify an individual using a supervised machine learning approach. To this end, we use two previously published data sets that comprises resting-state and task-based fMRI responses. We use static functional connectivity measures as input to a linear classifier to evaluate its performance. We additionally extend this analysis to capture dynamic functional connectivity using two approaches: the common sliding window approach and the more recent phase synchronybased measure. We found that the classification models using dynamic functional connectivity patterns as input outperform their static analysis counterpart by a significant margin for both data sets. Furthermore, sliding window-based analysis proved to capture more individual-specific brain connectivity patterns than phase synchrony measures for restingstate data while the reverse pattern was observed for the task-based data set. Upon investigating the effects of feature reduction, we found that feature elimination significantly improved results upto a point with near-perfect classification accuracy for the task-based data set while a gradual decrease in the accuracy was observed for resting-state data set. The implications of these findings are discussed. The results we have are promising and present a novel direction to investigate further.

Prevention of pollution from combined sewer overflows (CSOs) is currently a major concern due to their impact on aquatic and human environment. With the stringent regulations related to the CSOs treatment, it is necessary to develop an efficient, fast and low cost treatment technique that meets the applicable criteria. In this work, the comprehensive study was done to determine the efficiency of ferrate (VI) for the treatment of CSOs. At a Fe (VI) dose of 0.24 mg/l, TCOD, SCOD, TBOD 5 , SBOD 5 , TSS, VSS, TP, TN and soluble TN removal efficiencies of 71%, 75%, 69%, 68%, 72%, 83%, 64%, 38% and 36% respectively were achieved. Kinetic studies revealed that a contact time of only 15 minutes is sufficient to achieve secondary effluent criteria. An innovative technique of using primary sludge (PS) and thickened waste activated sludge (TWAS) as a source for the in-situ synthesis of ferrate was explored. A comparative study of treatment efficiencies achieved by Fe (VI) generated from different sources was done. At 0.1 mg/l dose of Fe (VI) synthesized from PS, TCOD, SCOD, TSS, VSS, TP and TN removal efficiencies of 60%, 62%, 63%, 67%, 30% and 25% respectively were achieved.

No abstract

Load balancing is a foundational function of datacenter infrastructures and is critical to the performance of online services hosted in datacenters. As the demand for cloud services grows, expensive and hard-to-scale dedicated hardware load balancers are being replaced with software load balancers that scale using a distributed data plane that runs on commodity servers. Software load balancers offer low cost, high availability and high flexibility, but suffer high latency and low capacity per load balancer, making them less than ideal for applications that demand either high throughput, or low latency or both. In this paper, we present Duet, which offers all the benefits of software load balancer, along with low latency and high availability -- at next to no cost. We do this by exploiting a hitherto overlooked resource in the data center networks -- the switches themselves. We show how to embed the load balancing functionality into existing hardware switches, thereby achieving organic scalability at no extra cost. For flexibility and high availability, Duet seamlessly integrates the switch-based load balancer with a small deployment of software load balancer. We enumerate and solve several architectural and algorithmic challenges involved in building such a hybrid load balancer. We evaluate Duet using a prototype implementation, as well as extensive simulations driven by traces from our production data centers. Our evaluation shows that Duet provides 10x more capacity than a software load balancer, at a fraction of a cost, while reducing latency by a factor of 10 or more, and is able to quickly adapt to network dynamics including failures.