title: Failure resilience and traffic engineering for multi-controller software defined networking

authors: Zaicu, Nicu Florin

abstract: This thesis explores and proposes solutions to address the challenges faced by Multi-Controller SDN (MCSDN) systems when deploying TE optimisation on WANs. Despite the interest from the research community, existing MCSDN systems present limitations. For example, TE optimisation systems are computationally complex, have high consistency requirements, and need network-wide state to operate. Because of such requirements, MCSDN systems can encounter performance overheads and state consistency problems when implementing TE. Moreover, performance and consistency problems are more prominent when deploying the system on WANs as these network types have higher inter-device latency, delaying state propagation.

Unlike existing literature, this thesis presents several design choices that address all four challenges affecting MCSDN systems (scalability, consistency, resilience, and coordination). We use the presented design choices to build Helix, a hierarchical MCSDN system. Helix provides better scalability, performance and failure resilience compared to existing MCSDN systems by sharing minimal state between controllers, offloading operations closer to the data plane and deploying lightweight tasks.

A challenge that we faced when building Helix was that existing TE algorithms did not meet Helix's design choices. This thesis presents a new CSPF-based TE algorithm that needs minimal state to operate and supports offloading inter-area TE to local controllers, fulfilling Helix's requirements. Helix's TE algorithm provides better performance and forwarding stability, addressing 1.6x more congestion while performing up to 29x fewer path modifications than the other algorithms evaluated in our experiments.

While MCSDN literature has explored evaluating different aspects of system performance, there is a lack of readily available tools and concrete testing methodologies. To this end, this thesis provides concrete testing methodologies and tools readily available to the MCSDN community to evaluate the data plane failure resilience, control plane failure resilience, and TE optimisation performance of MCSDN systems.

text: Failure resilience and traffic engineering for multi-controller software defined networking This thesis explores and proposes solutions to address the challenges faced by Multi-Controller SDN (MCSDN) systems when deploying TE optimisation on WANs. Despite the interest from the research community, existing MCSDN systems present limitations. For example, TE optimisation systems are computationally complex, have high consistency requirements, and need network-wide state to operate. Because of such requirements, MCSDN systems can encounter performance overheads and state consistency problems when implementing TE. Moreover, performance and consistency problems are more prominent when deploying the system on WANs as these network types have higher inter-device latency, delaying state propagation.

Unlike existing literature, this thesis presents several design choices that address all four challenges affecting MCSDN systems (scalability, consistency, resilience, and coordination). We use the presented design choices to build Helix, a hierarchical MCSDN system. Helix provides better scalability, performance and failure resilience compared to existing MCSDN systems by sharing minimal state between controllers, offloading operations closer to the data plane and deploying lightweight tasks.

A challenge that we faced when building Helix was that existing TE algorithms did not meet Helix's design choices. This thesis presents a new CSPF-based TE algorithm that needs minimal state to operate and supports offloading inter-area TE to local controllers, fulfilling Helix's requirements. Helix's TE algorithm provides better performance and forwarding stability, addressing 1.6x more congestion while performing up to 29x fewer path modifications than the other algorithms evaluated in our experiments.

While MCSDN literature has explored evaluating different aspects of system performance, there is a lack of readily available tools and concrete testing methodologies. To this end, this thesis provides concrete testing methodologies and tools readily available to the MCSDN community to evaluate the data plane failure resilience, control plane failure resilience, and TE optimisation performance of MCSDN systems.

year: 2022

title: Large-scale automatic species identification

authors: Mo, Jeff

subjects: computer science

abstract: The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control.

text: Large-scale automatic species identification The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control.

year: 2017

title: Māori academic success: Why the deficit perspectives?

authors: Aporosa, S. 'Apo'

subjects: Māori

abstract: Academics have long discussed the power of communication to both motivate and discourage. Concerning education, Paulo Freire (1997) had explained the dialogical learning process and the empowerment that comes from the inclusion of student knowledge. Alternatively, Carlson & Dimitriadis (2003) describe disempowerment and underachievement among Afro-American students resulting from societal expectations that they will fail. In a recent education environment scan focused on the Waikato-Tainui rohe, 28 kaupapa Māori education providers were identified as sites of high educational success, with one described as “outshining private schools and bucking national trends” (Carson, 2013). This aside, perceptions of generalized educational failure among Māori are commonplace in Aotearoa New Zealand. These impressions are fed by the media, the Ministry of Education (MOE) and in some cases, academic publications. This paper discusses the power of deficit models and counter-narratives to academic underachievement. This will be juxtaposed with commentary on why some students and their education providers within the Waikato-Tainui rohe appear to be unaffected by these counter- narratives and deficit theorizing.

text: Māori academic success: Why the deficit perspectives? Academics have long discussed the power of communication to both motivate and discourage. Concerning education, Paulo Freire (1997) had explained the dialogical learning process and the empowerment that comes from the inclusion of student knowledge. Alternatively, Carlson & Dimitriadis (2003) describe disempowerment and underachievement among Afro-American students resulting from societal expectations that they will fail. In a recent education environment scan focused on the Waikato-Tainui rohe, 28 kaupapa Māori education providers were identified as sites of high educational success, with one described as “outshining private schools and bucking national trends” (Carson, 2013). This aside, perceptions of generalized educational failure among Māori are commonplace in Aotearoa New Zealand. These impressions are fed by the media, the Ministry of Education (MOE) and in some cases, academic publications. This paper discusses the power of deficit models and counter-narratives to academic underachievement. This will be juxtaposed with commentary on why some students and their education providers within the Waikato-Tainui rohe appear to be unaffected by these counter- narratives and deficit theorizing.

year: 2016