Achieving Energy Efficiency on Networking Systems with Optimization Algorithms and Compressed Data Structures

To cope with the increasing quantity, capacity and energy consumption of transmission and routing equipment in the Internet, energy efficiency of communication networks has attracted more and more attention from researchers around the world. In this dissertation, we proposed three methodologies to achieve energy efficiency on networking devices: the NP-complete problems and heuristics, the compressed data structures, and the combination of the first two methods. We first consider the problem of achieving energy efficiency in Data Center Networks (DCN). We generalize the energy efficiency networking problem in data centers as optimal flow assignment problems, which is NP-complete, and then propose a heuristic called CARPO, a correlation-aware power optimization algorithm, that dynamically consolidate traffic flows onto a small set of links and switches in a DCN and then shut down unused network devices for power savings. We then achieve energy efficiency on Internet routers by using the compressive data structure. A novel data structure called the Probabilistic Bloom Filter (PBF), which extends the classical bloom filter into the probabilistic direction, so that it can effectively identify heavy hitters with a small memory foot print to reduce energy consumption of network measurement. To achieve energy efficiency on Wireless Sensor Networks (WSN), we developed one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. Based on the Open Vehicle Routing problem, EDAL exploits the topology requirements of Compressive Sensing (CS), then implement CS to save more energy on sensor nodes

A neural data structure for novelty detection

Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems

Course Manual: International Workshop-cum-Training Programme on "Fisheries and Aquaculture"

Marine fisheries contribute to food, nutrition, employment and income generation in India. The sector supports about four million people for their livelihood and nearly one million fishermen by way of employment and contributes significantly to the export earnings of the country and balance of trade. The sector contributes to an economic wealth valued at nearly US$10 billion annually. The marine fisheries of the country consist of small-scale and artisanal fishers belonging mechanized, motorized and non-mechanized sectors and a range of other stakeholders, including governmental and nongovernmental agencies. Though India is not a leading producer in true mariculture we are second in aquaculture production after China. Coastal aquaculture of shrimp has a major role in aquaculture production and export in India. Even though there is vast scope, recently only India has taken up mariculture technologies to the stake holder level. Due to the success achieved mariculture, it has been identified as a potential source of production enhancement for high valued species like lobster, seabass, cobia and pompano for which the capture fishery is negligible

Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond

The acceleration race of digital computing technologies seems to be steering toward impasses -- technological, economical and environmental -- a condition that has spurred research efforts in alternative, "neuromorphic" (brain-like) computing technologies. Furthermore, since decades the idea of exploiting nonlinear physical phenomena "directly" for non-digital computing has been explored under names like "unconventional computing", "natural computing", "physical computing", or "in-materio computing". This has been taking place in niches which are small compared to other sectors of computer science. In this paper I stake out the grounds of how a general concept of "computing" can be developed which comprises digital, neuromorphic, unconventional and possible future "computing" paradigms. The main contribution of this paper is a wide-scope survey of existing formal conceptualizations of "computing". The survey inspects approaches rooted in three different kinds of background mathematics: discrete-symbolic formalisms, probabilistic modeling, and dynamical-systems oriented views. It turns out that different choices of background mathematics lead to decisively different understandings of what "computing" is. Across all of this diversity, a unifying coordinate system for theorizing about "computing" can be distilled. Within these coordinates I locate anchor points for a foundational formal theory of a future computing-engineering discipline that includes, but will reach beyond, digital and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now (March 2021) been published as "Toward a generalized theory comprising digital, neuromorphic, and unconventional computing" in the new open-access journal Neuromorphic Computing and Engineerin

Natural Salinity Removal Processes in Reservoirs

A small but significant amount of salinity removal has been reported by various authors to occur in mainstem Colorado River reservoirs. Recalculation of some of these salinity budgets, together with a review of the data bases used, usggests taht removal has not often been conclusively demonstrated. Laboratory microcosm experiments and field data indicate that calcium carbonate precipitation, perhaps with some coprecipitation of magnesium carbonate, is the mechanism responsible for most of the salinity removal in Oneida Reservoir, Idaho. Coprecipitation processes (including ion exchange), coagulation, and bioassimilation do not appear to be important natural salinity removal mechanisms. Finally, loss of calcium, relative to monovalent cations, may decrease water quality for irrigation purposes through increasing the sodium adsorption ratio (SAR), despite a pross decrease in the TDS. The potential role of various reservoir operation options in managing natural salinity removal processes and the value of such removal is discussed

Ammonia cycling and emerging particulate matter pollutants under arable land-use management: A modelling approach

Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Particulate Matter (PM) has been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe threshold of exposure in place to-date. Ammonia (NH3) emissions are linked to the secondary production of PM through atmospheric reactions occurring with acidic atmospheric components such as sulfuric acid, nitric acid, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. More than 95% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. This study aims to advance knowledge and understanding of the role of arable agricultural practices and management in NH3 enrichment and aid in mapping of the sources of PM production. The nature and contribution of NH3 in the atmosphere to secondary PM in defined arable settings will be examined to provide greater insight into system dynamics facilitating emission control and mitigation measures to be implemented. This will be achieved through a review of existing literature and database assessment combined with the application of a localised field monitoring network in arable agricultural settings. As Ireland currently has no active atmospheric NH3 monitoring in place, reported emission levels can prove to be imprecise. And lead to over- and under-estimation of NH3 gas emissions to the atmosphere from sources such as agriculture. By establishing localized monitoring stations at emission sources, the precision of the estimated NH3 concentrations in the atmosphere can be improved. This can also lead to improved understanding of PM dynamics and formation. This will be achieved by using a combination of active and passive sampling instruments for in-field atmospheric sample collection, which will then be analysed in the laboratory using ion chromatography. Additionally, to gain a fuller understanding of the dynamics of an agricultural system, background monitoring of soil properties and water nutrient enrichment will also be carried out. The output of this project will build on existing theories of NH3, and PM dynamics established by previous research, and combine these with field data, including agricultural practices, NH3 source production and PM generation, soil and water enrichment and quality background monitoring to synthesise a new mechanistic paradigm. This new understanding will be operationalised through the development of a conceptual model of NH3 dynamics and PM generation, and agri-ecological interactions known as Conceptual Ammonia-aeroSol bIOspheric Simulation (CASIOS). The model builds on a Drivers, Pressures, State, Impacts, Responses framework, with an additional attribute introduced under the term ‘Concept’ which includes environmental conditions previously not considered under this paradigm

(Acido)bacterial diversity in space and time

Recent technological achievements enabled microbiologists to fully grasp the vast diversity of microbial life that is resident in soils, highly complex matrices of alternating micro-habitats on very small scales. Since then, microbial community composition has been catalogued for many different terrestrial habitats. This triggered the investigation and definition of processes which shape these communities. In most cases, the environment determines community composition, and similar habitats may feature similar microbial communities despite being far apart. However, some habitats have been described as subjected to pronounced neutral processes, which are dispersal, ecological drift or speciation. The balance between these process types is now the subject of many studies looking at microbial communities. It is also clear that these processes need to be monitored on both temporal and spatial scales, as the two dimensions are inseparably interlinked. However, most microbial studies deal with only one aspect, but do not control for the other. In this work, the outcome of a highly sophisticated plot scale experiment is presented encompassing 358 sampling locations distributed between six intra-annual sampling points on a 10 m x 10 m unfertilized grassland site in the Swabian Alb. RNA was extracted from the A-horizon of each soil and the hypervariable region 3 of the ribosomal small subunit was amplified and sequenced with barcoded Illumina sequencing. Roughly 400 million eubacterial reads were obtained. The dataset was used to assess the population dynamics of Acidobacteria, as well as the spatio-temporal co-occurenze of functionally depending microorganism. Additionally, preliminary results motivated the assessment of common methods for the examination of rhizospheric communities. In combination, the diversity of bacterial communities in space and time was tested from different angles, reflecting different research question, and they all revealed a far more complex reality than previously thought