Browsing by Advisor "Simmhan, Yogesh"
Now showing items 1-11 of 11
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Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud
Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments ... -
Benchmarking and Scheduling Strategies for Distributed Stream Processing
(2018-08-20)The velocity dimension of Big Data refers to the need to rapidly process data that arrives continuously as streams of messages or events. Distributed Stream Processing Systems (DSPS) refer to distributed programming and ... -
Efficient and Resilient Stream Processing in Distributed Shared Environment
Internet of Things (IoT) deployments comprising of sensors and actuators collect observational data and provide continuous streams of data, often called streaming data or fast data. Smart Cities use such IoT technologies ... -
Intelligent Orchestration of Autonomous Systems Across Edge-Cloud Continuum
The benefits of autonomous mobile platforms, such as Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras, are enhanced by compact edge accelerators that are co-located, such as the NVIDIA Jetson with 100s of CUDA ... -
Modeling and Adaptive Scheduling Strategies for Distributed Graph Algorithms
Graph processing at scales of millions-billions of vertices and edges has become common to solve real-world problems in domains like social networks, smart cities and genomics. Distributed "Big Data" platforms for graph ... -
Optimization of Traversal Queries on Distributed Graph Stores
In this era of Big Data Analytics, much of the semi-structured data has inherent interconnectivity between representative entities. These are increasingly being modeled as property graphs because of the semantic advantages ... -
Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms
Graphs with temporal characteristics are increasingly becoming prominent. Their vertices, edges and attributes are annotated with a lifespan, allowing one to add or remove vertices and edges. Such graphs can grow to millions ... -
Reliable and Efficient Application Scheduling on Edge, Fog and Cloud
Cloud computing has emerged in the last decade as a popular distributed computing service offered by commercial providers. Public Clouds offer pay-as-you-go access to elastic resources that can be acquired and released ... -
Scalable Distributed Frameworks for Temporal Analysis and Partitioning of Streaming Graphs
The analysis of graph-structured data has become increasingly important as networks in various domains, including science, engineering, and business, grow in size, complexity, and dynamism. While static graph analysis ... -
Scalable Video Data Management and Visual Querying System for Autonomous Camera Networks
Video data has been historically known for its unstructured nature, rich semantic content and scalability issues in terms of storage. With advances in computer vision and Deep Neural Net works (DNNs) it is now possible ... -
Systems Optimizations for DNN Training and Inference on Accelerated Edge Devices
Deep Neural Networks (DNNs) have had a significant impact on a wide variety of domains, such as Autonomous Vehicles, Smart Cities, and Healthcare, through low-latency inferencing on edge computing devices close to the data ...