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Distributed generation along with smart grid applications are poised to make important contributions to the clean-tech sector and remote communities. The dependence on one source for energy supply does not prove reliable enough when the renewable resource, such as wind or solar, is variable, creating a dependence on external fuel supply and a vulnerability to foreign control. Developing an energy strategy through intelligent energy system simulation and optimization can help communities make informed decisions about their energy investments. This dissertation reasons that distributed renewable energy systems without operative computational infrastructures face a fundamental economic challenge derived from their ad-hoc design and implementation. To address this, it proposes the method of Optimal Operational Awareness (OOA)—a feedback mechanism on the state of, and changes in, the properties of the implemented subsystems and their behaviour, to meet users objectives. Despite many applications of hybrid renewable energy systems, and reputable multi-objective evolutionary algorithms (MOEAs) for optimization, no one has applied MOEAs to dynamic system operation for optimized engagement of system components. This thesis describes an application of the NSGA-II algorithm to the multi-objective optimization of the operation of a stand-alone wind-PV-biomass-diesel system with batteries and CAES storage and a central controller. The simultaneous objectives are to minimize the levelized cost of energy (LCOE), and unmet load (UL) while maximizing the renewable energy ratio (RER). This work provides a case-study evaluation from data collected on-site at the island of Fernando de Noronha (FDN), Brazil. The results show that FDN could move from an annual average of 33% RER and LCOE range of $0.26 - $0.36 per kWh to an increased RER range of 60% - 100% and LCOE of $0.10 - $0.50 per kWh, while maintaining UL of 0%, by increasing its renewable energy generation and storage capacity approximately five times. Furthermore, optimal operational awareness for this configuration shows that despite 100% RER, certain periods experience a high LCOE of $2.00 per kWh, resulting from energy spillage due to oversupply, indicating sub-optimal system sizing and wasted energy to trim by improving system configuration. This work concludes that it is possible to achieve 100% RER, but storage and/or backup diesel generation are important to include in systems with highly variable supply. The cost of electricity decreases as renewable energy penetration increases, but is configuration dependent as well dependent on storage state of charge. Oversizing storage can be just as costly, if not more costly, than supplying energy with diesel generation, thus proper sizing and dispatch strategy are critical to achieve economic electricity supply. Furthermore, the role of multiple renewable energy generators in providing autonomous supply can be more valuable to the user than increased supply cost.

Distributed denial of service attacks (or DDoS) are a common occurrence on the internet and are becoming more intense as the bot-nets, used to launch them, grow bigger. Preventing or stopping DDoS is not possible without radically changing the internet infrastructure; various DDoS mitigation techniques have been devised with different degrees of success. All mitigation techniques share the need for a DDoS detection mechanism. DDoS detection based on traffic self-similarity estimation is a relatively new approach which is built on the notion that undis- turbed network traffic displays fractal like properties. These fractal like properties are known to degrade in presence of abnormal traffic conditions like DDoS. Detection is possible by observing the changes in the level of self-similarity in the traffic flow at the target of the attack. Existing literature assumes that DDoS traffic lacks the self-similar properties of undisturbed traffic. We show how existing bot- nets could be used to generate a self-similar traffic flow and thus break such assumptions. We then study the implications of self-similar attack traffic on DDoS detection. We find that, even when DDoS traffic is self-similar, detection is still possible. We also find that the traffic flow resulting from the superimposition of DDoS flow and legitimate traffic flow possesses a level of self-similarity that depends non-linearly on both relative traffic intensity and on the difference in self-similarity between the two incoming flows.

Distributed Gossip in networks is a well studied and observed problem which can be accomplished using different gossiping styles. This work focusses on the development, analysis and evaluation of a novel in-situ distributed gossip protocol framework design called (INDIGO). A core aspect of INDIGO is its ability to execute on a simulation setup as well as a system testbed setup in a seamless manner allowing easy portability. The evaluations focus on application of INDIGO to solve problems such as distributed average consensus, distributed seismic event location and lastly distributed seismic tomography. The results obtained herein validate the efficacy and reliability of INDIGO.

Context: Multi Mediation is a process of collecting data from network(s) &amp; network elements, pre-processing this data and distributing it to various systems like Big Data analysis, Billing Systems, Network Monitoring Systems, and Service Assurance etc. With the growing demand for networks and emergence of new services, data collected from networks is growing. There is need for efficiently organizing this data and this can be done using databases. Although RDBMS offers Scale-up solutions to handle voluminous data and concurrent requests, this approach is expensive. So, alternatives like distributed databases are an attractive solution. Suitable distributed database for Multi Mediation, needs to be investigated. Objectives: In this research we analyze two distributed databases in terms of performance, scalability and availability. The inter-relations between performance, scalability and availability of distributed databases are also analyzed. The distributed databases that are analyzed are MySQL Cluster 7.4.4 and Apache Cassandra 2.0.13. Performance, scalability and availability are quantified, measurements are made in the context of Multi Mediation system. Methods: The methods to carry out this research are both qualitative and quantitative. Qualitative study is made for the selection of databases for evaluation. A benchmarking harness application is designed to quantitatively evaluate the performance of distributed database in the context of Multi Mediation. Several experiments are designed and performed using the benchmarking harness on the database cluster. Results: Results collected include average response time &amp; average throughput of the distributed databases in various scenarios. The average throughput &amp; average INSERT response time results favor Apache Cassandra low availability configuration. MySQL Cluster average SELECT response time is better than Apache Cassandra for greater number of client threads, in high availability and low availability configurations.Conclusions: Although Apache Cassandra outperforms MySQL Cluster, the support for transaction and ACID compliance are not to be forgotten for the selection of database. Apart from the contextual benchmarks, organizational choices, development costs, resource utilizations etc. are more influential parameters for selection of database within an organization. There is still a need for further evaluation of distributed databases. / <p>I am indebted to my advisor Prof. Lars Lundberg and his valuable ideas which helped in the completion of this work. In fact he has guided on every crucial and important stages of this research work.</p><p>I sincerely thank Prof. Markus Fiedler &amp; Prof. Kurt Tutschku for their endless support during the work.</p><p>I am grateful to Neeraj Garg, Sourab, Saket &amp; Kulbir at Ericsson, for providing me necessary equipment and helping me financially during my work.</p><p>To my family members and friends who one way or the other shared their support. Thank you.</p><p>Above all I would like to thank the Supreme Personality of Godhead, the author of everything.</p>

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Diagnosing performance problems in modern datacenters and distributed systems is challenging, as the root cause could be contained in any one of the system’s numerous components or, worse, could be a result of interactions among them. As distributed systems continue to increase in complexity, diagnosis tasks will only become more challenging. There is a need for a new class of diagnosis techniques capable of helping developers address problems in these distributed environments. As a step toward satisfying this need, this dissertation proposes a novel technique, called request-flow comparison, for automatically localizing the sources of performance changes from the myriad potential culprits in a distributed system to just a few potential ones. Request-flow comparison works by contrasting the workflow of how individual requests are serviced within and among every component of the distributed system between two periods: a non-problem period and a problem period. By identifying and ranking performance-affecting changes, request-flow comparison provides developers with promising starting points for their diagnosis efforts. Request workflows are obtained with less than 1% overhead via use of recently developed end-to-end tracing techniques. To demonstrate the utility of request-flow comparison in various distributed systems, this dissertation describes its implementation in a tool called Spectroscope and describes how Spectroscope was used to diagnose real, previously unsolved problems in the Ursa Minor distributed storage service and in select Google services. It also explores request-flow comparison’s applicability to the Hadoop File System. Via a 26-person user study, it identifies effective visualizations for presenting request-flow comparison’s results and further demonstrates that request-flow comparison helps developers quickly identify starting points for diagnosis.This dissertation also distills design choices that will maximize an end-to-end tracing infrastructure’s utility for diagnosis tasks and other use cases.

Social networks are large graphs which require multiple servers to store and manage them. Providing performant scalable systems that store these graphs through partitioning them into subgraphs is an important issue. In such systems each partition is hosted by a server to satisfy multiple objectives. These objectives include balancing server loads, reducing remote traversals (number of edges cut), and adapting the partitioning to changes in the structure of the graph in the face of changing workloads. To address these issues, a dynamic repartitioning algorithm is required to modify an existing partitioning to maintain good quality partitions. Such a repartitioner should not impose a significant overhead to the system. This thesis introduces a greedy repartitioner, which dynamically modifies a partitioning using a small amount of resources. In contrast to the existing repartitioning algorithms, the greedy repartitioner is performant (in terms of time and memory), making it suitable for implementing and using it in a real system. The greedy repartitioner is integrated into DistNeo4j, which is designed as an extension of the open source Neo4j graph database system, to support workloads over partitioned graph data distributed over multiple servers. Using real-world data sets, this thesis shows that DistNeo4j leverages the greedy repartitioner to maintain high quality partitions and provides a 2 to 3 times performance improvement over the de-facto hash-based partitioning.

This thesis includes studies of developing distributed optical fiber vibration sensor based on Rayleigh backscattering with broad frequency response range and high spatial resolution. Distributed vibration sensor based on all-polarization-maintaining configurations of the phase-sensitive optical time domain reflectometry (OTDR) is developed to achieve high frequency response and spatial resolution. Signal fading and noise induced by polarization change can be mitigated via polarization-maintaining components. Pencil-break event is tested as a vibration source and the layout of the sensing fiber part is designed for real applications. The spatial resolution is 1m and the maximum distance between sensing fiber and vibration event is 18cm. Wavelet denoising method is introduced to improve the performance of the distributed vibration sensor based on phase-sensitive OTDR in standard single-mode fiber. Noise can be reduced more effectively by thresholding the wavelet coefficient. Sub-meter spatial resolution is obtained with a detectable frequency up to 8 kHz. A new distributed vibration sensor based on time-division multiplexing (TDM) scheme is also studied. A special probe waveform including a narrow pules and a quasi-continuous wave can combine the conventional phase-sensitive OTDR system and polarization diversity scheme together in one single-mode fiber without crosstalk. Position and frequency of the vibration can be determined by these two detection systems consecutively in different time slots. Vibration event up to 0.6 MHz is detected with 1m spatial resolution along a 680m single-mode sensing fiber. Continuous wavelet transform (CWT) is investigated to study the non-stationary vibration events measured by our phase OTDR system. The CWT approach can access both frequency and time information of the vibration event simultaneously. Distributed vibration measurements of 500Hz and 500Hz to 1 kHz sweep events over 20 cm fiber length are demonstrated using a single-mode fiber. Optical frequency-domain reflectometry (OFDR) for vibration sensing is proposed for the first time. The local Rayleigh backscatter spectrum shift in time sequence could be used to determine dynamic strain information at a specific position of the vibrated state with respect to that of the non-vibrated state. Measurable frequency range of 0-32 Hz with the spatial resolution of 10 cm is demonstrated along a 17 m fiber.